**Physical and Radiobiological Evaluation of Radiotherapy Treatment Plan**

Suk Lee, Yuan Jie Cao and Chul Yong Kim

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/60846

#### **Abstract**

Radiation treatment planning plays an important role in modern radiation therapy; it could simulate to plan the geometric, radiobiological, and dosimetric aspects of the therapy using radiation transport simulations and optimization. In this chapter, we have reviewed several quantitative methods used for evaluating radiation treatment plans and discussed some important considering points. For the purpose of quantita‐ tive plan evaluation, we reviewed dosimetrical indexes like PITV, CI, TCI, HI, MHI, CN, COSI, and QF. Furthermore, radiobiological indexes like Niemierko's EUD-based TCP and NTCP were included for the purpose of radiobiological outcome modeling. Additionally, we have reviewed dose tolerance for critical organs including RTOG clinical trial results, QUENTEC data, Emami data, and Milano clinical trial results. For the purpose of clinical evaluation of radiation-induced organ toxicity, we have re‐ viewed RTOG and EORTC toxicity criteria. Several programs could help for the easy calculation and analysis of dosimetrical plan indexes and biological results. We have reviewed the recent trend in this field and proposed further clinical use of such pro‐ grams. Along this line, we have proposed clinically optimized plan comparison proto‐ cols and indicated further directions of such studies.

**Keywords:** Treatment plan evaluation, Dosimetrical indices, Radiobiological indices, Tolerance doses, Radiation toxicity

#### **1. Introduction**

We have reviewed the methods used for quantitative comparison of different radiation treatment plans, the process of treatment plan comparison protocol, and the further direction of treatment plan evaluation programs. For the purpose of quantitative plan evaluation, we reviewed dosimetrical indexes like prescription isodose to target volume (PITV) ratio,

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

homogeneity index (HI), conformity index (CI), target coverage index (TCI), modified dose homogeneity index (MHI), conformity number (CN), critical organ scoring index (COSI), and quality factor (QF). Furthermore, radiobiological indexes like Niemierko's EUD-based tumor control probability (TCP) and normal tissue complication probability (NTCP) were included for the purpose of radiobiological outcome modeling. Additionally, we have reviewed dose tolerance for critical organs including RTOG clinical trial results, QUENTEC data, Emami data, and Milano clinical trial results. For the purpose of clinical evaluation of radiation-induced organ toxicity, we have reviewed RTOG and EORTC toxicity criteria. Several programs could help for the easy calculation and analysis of dosimetrical plan indexes and biological results. We have reviewed the recent trend in this field and proposed further clinical use of such programs. It is well known that plan comparison study still remain many controversies. The major issue is that plan evaluation methods are used in plan comparison and plan optimiza‐ tion. We have reviewed well-known dosimetric and biological plan indexes and several commercial and non-commercial plan evaluation programs. Along this line, we have proposed clinically optimized plan comparison protocols and indicated the further directions of such studies.

### **2. Background: Radiotherapy, radiation treatment planning, and planning decision support program**

#### **2.1. Radiotherapy**

Over the past few decades, radiation treatment has become a technologically advanced field in modern medicine, especially with the advent of intensity-modulated radiation therapy (IMRT) [1]. Traditional radiation therapy planning is a manual, iterative, and simple process in which treatment fields are placed and beam modifiers are inserted.

Modifications are then made after manual inspection of the dose distribution calculated after each iteration [2]. In IMRT, the dose calculation engine specified dose distribution over the target volume and surrounding normal structures. Furthermore, dose calculation engine displayed a 2D dose intensity map by using its optimization algorithms [3]. Moreover, the inverse planning algorithm required users to set a dose/volume criteria for the specific organ/ structure, and the computer calculated to find out a final solution to satisfy the criteria. [4]. Another breakthrough of modern radiation treatment is image-guided radiotherapy (IGRT). With the adoption and integration of imaging information in treatment designs, IGRT is the most innovative area in advanced radiotherapy [5]. IGRT has increased knowledge of exact tumor targets and their movements during the treatment process [6]. Despite improvements in target coverage and normal tissue sparing, the implementation of IMRT and IGRT remains a labor-intensive trial and error process. The creation of optimized treatment plans for personalized therapy still requires significant time and effort. Radiation treatment includes CT simulation, organ contouring, treatment planning, quality assurance, and dose delivery (Figure 1) [7].

Physical and Radiobiological Evaluation of Radiotherapy Treatment Plan http://dx.doi.org/10.5772/60846 111

**Figure 1.** Clinical workflow of radiation treatment plan (a); radiation treatment includes CT simulation, organ contour‐ ing, treatment planning, quality assurance, and dose delivery. (b); configuration of radiotherapy equipment.

#### **2.2. Radiation treatment planning**

homogeneity index (HI), conformity index (CI), target coverage index (TCI), modified dose homogeneity index (MHI), conformity number (CN), critical organ scoring index (COSI), and quality factor (QF). Furthermore, radiobiological indexes like Niemierko's EUD-based tumor control probability (TCP) and normal tissue complication probability (NTCP) were included for the purpose of radiobiological outcome modeling. Additionally, we have reviewed dose tolerance for critical organs including RTOG clinical trial results, QUENTEC data, Emami data, and Milano clinical trial results. For the purpose of clinical evaluation of radiation-induced organ toxicity, we have reviewed RTOG and EORTC toxicity criteria. Several programs could help for the easy calculation and analysis of dosimetrical plan indexes and biological results. We have reviewed the recent trend in this field and proposed further clinical use of such programs. It is well known that plan comparison study still remain many controversies. The major issue is that plan evaluation methods are used in plan comparison and plan optimiza‐ tion. We have reviewed well-known dosimetric and biological plan indexes and several commercial and non-commercial plan evaluation programs. Along this line, we have proposed clinically optimized plan comparison protocols and indicated the further directions of such

**2. Background: Radiotherapy, radiation treatment planning, and planning**

Over the past few decades, radiation treatment has become a technologically advanced field in modern medicine, especially with the advent of intensity-modulated radiation therapy (IMRT) [1]. Traditional radiation therapy planning is a manual, iterative, and simple process

Modifications are then made after manual inspection of the dose distribution calculated after each iteration [2]. In IMRT, the dose calculation engine specified dose distribution over the target volume and surrounding normal structures. Furthermore, dose calculation engine displayed a 2D dose intensity map by using its optimization algorithms [3]. Moreover, the inverse planning algorithm required users to set a dose/volume criteria for the specific organ/ structure, and the computer calculated to find out a final solution to satisfy the criteria. [4]. Another breakthrough of modern radiation treatment is image-guided radiotherapy (IGRT). With the adoption and integration of imaging information in treatment designs, IGRT is the most innovative area in advanced radiotherapy [5]. IGRT has increased knowledge of exact tumor targets and their movements during the treatment process [6]. Despite improvements in target coverage and normal tissue sparing, the implementation of IMRT and IGRT remains a labor-intensive trial and error process. The creation of optimized treatment plans for personalized therapy still requires significant time and effort. Radiation treatment includes CT simulation, organ contouring, treatment planning, quality assurance, and dose delivery

in which treatment fields are placed and beam modifiers are inserted.

studies.

**decision support program**

110 Evolution of Ionizing Radiation Research

**2.1. Radiotherapy**

(Figure 1) [7].

For radiation treatment, a team of radiation oncologists, radiation therapists, medical physi‐ cists, and medical dosimetrists plan the appropriate external beam radiotherapy treatment technique for a patient with cancer [8]. There are generally two different types of planning algorithms, forward planning and inverse planning. The forward planning technique is mostly used in external-beam radiotherapy treatment planning process. For example, a medical physicist determines the beam angles in the treatment planning systems to maximize tumor dose when sparing the healthy tissues. This type of planning is used for the majority of external-beam radiotherapy treatments, but is only useful for relatively uncomplicated cases in which the tumor has a simple shape and is not near any critical organs. Inverse planning is a technique used to inversely design radiotherapy treatment plans (Figure 2). The radiation oncologist defines a patient's critical organs and tumor. Then, the dosimetrist provides target doses for each. An optimization program is then run to find the treatment plan that best matches all input criteria. This type of trial-and-error planning process is time and labor intensive.

**Figure 2.** Workflow of inverse radiation treatment planning.

There are several commercial treatment planning systems (TPS) available nowadays. Table 1 summarizes information about commercial TPS [9].

#### **2.3. Planning decision support program**

Dose volume histogram (DVH) provides dose volume coverage information. However, it fails to provide more information like hot spot and dose homogeneity. Dosimetrical indices were widely used for plan evaluation for a specific purpose. For example, a homogeneity index refers to the intensity of dose distributions in target volume, those plans with both "hot" spot and "cold" spot could be distinguished by this index. Additionally, some indices consider dose conformity in the target volume. Conformity index was an example of such indices. Another method to review and evaluate treatment plan quality was biological index. A tumor control probability could indirectly estimate a tumor could be controlled by a certain dose. Further‐ more, normal tissue complication probability could estimate the probability of a surrounding critical structure becomes some radiation-induced complications. Many programs have been designed and developed to calculate both dosimetrical and biological indices since the 2000s [10-29]. This is shown in Figure 3.


**Table 1.** Commercial RTP lists

technique for a patient with cancer [8]. There are generally two different types of planning algorithms, forward planning and inverse planning. The forward planning technique is mostly used in external-beam radiotherapy treatment planning process. For example, a medical physicist determines the beam angles in the treatment planning systems to maximize tumor dose when sparing the healthy tissues. This type of planning is used for the majority of external-beam radiotherapy treatments, but is only useful for relatively uncomplicated cases in which the tumor has a simple shape and is not near any critical organs. Inverse planning is a technique used to inversely design radiotherapy treatment plans (Figure 2). The radiation oncologist defines a patient's critical organs and tumor. Then, the dosimetrist provides target doses for each. An optimization program is then run to find the treatment plan that best matches all input criteria. This type of trial-and-error planning process is time and labor

There are several commercial treatment planning systems (TPS) available nowadays. Table 1

Dose volume histogram (DVH) provides dose volume coverage information. However, it fails to provide more information like hot spot and dose homogeneity. Dosimetrical indices were widely used for plan evaluation for a specific purpose. For example, a homogeneity index refers to the intensity of dose distributions in target volume, those plans with both "hot" spot

intensive.

112 Evolution of Ionizing Radiation Research

**Figure 2.** Workflow of inverse radiation treatment planning.

**2.3. Planning decision support program**

summarizes information about commercial TPS [9].


**Figure 3.** Timeline of plan analysis programs [10-11, 13, 17-18, 22-24, 28, 52-53].

#### **3. Plan evaluation**

#### **3.1. Plan evaluation methods**

#### *3.1.1. Qualitative analysis*

In conventional radiation therapy, an isodose distribution is used for plan analysis and evaluation. Figure 4 shows the typical isodose distribution of 3D conformal treatment plans and IMRT plans.

#### *3.1.2. Quantitative analysis*

DVH is the relationship between the dose distribution of a certain organ and 100% normalized volume of such organ. It was calculated and generated based on 3D reconstructed images in the treatment planning systems [9]. DVH could simplify 3D information of dose distribution

**Figure 4.** Typical isodose distribution of (a) 3D conformal treatment plan and (b) IMRT plan.

**Figure 3.** Timeline of plan analysis programs [10-11, 13, 17-18, 22-24, 28, 52-53].

In conventional radiation therapy, an isodose distribution is used for plan analysis and evaluation. Figure 4 shows the typical isodose distribution of 3D conformal treatment plans

DVH is the relationship between the dose distribution of a certain organ and 100% normalized volume of such organ. It was calculated and generated based on 3D reconstructed images in the treatment planning systems [9]. DVH could simplify 3D information of dose distribution

**3. Plan evaluation**

114 Evolution of Ionizing Radiation Research

*3.1.1. Qualitative analysis*

*3.1.2. Quantitative analysis*

and IMRT plans.

**3.1. Plan evaluation methods**

into a 2D graph or quantitative values [30-34]. Figure 5 shows a typical DVH for helical tomotherapy (HT) and intensity modulated proton therapy (IMPT) plans for prostate cancer.

**Figure 5.** Typical DVH for helical tomotherapy (HT) treatment plan and intensity modulated arc therapy (IMAT) plan of prostate cancer: (a) axial slice, (b) sagittal slice. Planning target volume (PTV), critical structures, and four different isodose lines shown. (c) Dose-volume histogram comparison for prostate case. Solid lines, tomotherapy plan; dashed lines, intensity modulated arc therapy (IMAT) plan (International Journal of Radiation Oncology Biology Physics, 69(1), 2007).

#### **4. Plan analysis**

Isodose distribution and DVH analysis were insufficient compared to complicated and advanced planning techniques. As the femoral head DVHs in Figure 4 show, it was difficult to distinguish whether IMPT (continuous red line) or HT (dashed red line) plans were superior. For low dose volume (V0 to V20), IMPT was more favorable than HT. However, this relationship reversed for high dose volume (V20 to V50). As a result, there are several indexes that may represent target conformity and dose homogeneity [31, 35-38].

#### **4.1. Dosimetrical analysis**

#### *4.1.1. Index*

Several quantitative evaluation tools were reviewed in this paper. These included the pre‐ scription isodose to target volume (PITV) ratio, homogeneity index (HI), conformity index (CI), target coverage index (TCI), modified dose homogeneity index (MHI), conformity number (CN), quality factor (QF) for PTV, maximum dose, mean dose, dose volume histogram (DVH), and critical organ scoring index (COSI) for the OAR (Figure 6).

#### *4.1.2. PTV index*

The PITV ratio, obtained by dividing prescription isodose surface volume by target volume, is expressed as:

$$PITV = \frac{PIV}{TV} \tag{1}$$

In the above equation, PIV represents prescription isodose surface volume and TV refers to target volume [39]. The PITV ratio is a conformity measure, and a value of 1.0 indicates that the volume of the prescription isodose surface equals that of the PTV. A PITV ratio of 1.0 does not necessarily imply that both volumes are similar. To ensure adequate PTV coverage, this measure should always be used in conjunction with a PTV-DVH [39]. The CI and HI indices for targets were computed to assess the quality of IMRT plans. CI is defined as the ratio of target volume and the volume inside the isodose surface that corresponds to the prescription dose. CI is generally used to indicate the portion of a prescription dose that is delivered inside the PTV [40].

CI is expressed as:

$$CI = \frac{PTV\_{pD}}{PVV} \tag{2}$$

In the above equation, PIV represents prescription isodose surface volume and PTVPD represents PTV coverage at the prescription dose. CI of 1 indicates that 100% of a prescription dose is delivered to the PTV, and no dose is delivered to any adjacent tissue [40]. The CI is less than 1 for most clinical cases. Higher CI values indicate poorer dose conformity to the PTV. HI is defined as the ratio of maximum dose delivered to the PTV divided by the prescription dose delivered to the PTV [41].

HI is expressed as:

**4. Plan analysis**

116 Evolution of Ionizing Radiation Research

**4.1. Dosimetrical analysis**

*4.1.1. Index*

*4.1.2. PTV index*

is expressed as:

the PTV [40].

CI is expressed as:

Isodose distribution and DVH analysis were insufficient compared to complicated and advanced planning techniques. As the femoral head DVHs in Figure 4 show, it was difficult to distinguish whether IMPT (continuous red line) or HT (dashed red line) plans were superior. For low dose volume (V0 to V20), IMPT was more favorable than HT. However, this relationship reversed for high dose volume (V20 to V50). As a result, there are several indexes that may

Several quantitative evaluation tools were reviewed in this paper. These included the pre‐ scription isodose to target volume (PITV) ratio, homogeneity index (HI), conformity index (CI), target coverage index (TCI), modified dose homogeneity index (MHI), conformity number (CN), quality factor (QF) for PTV, maximum dose, mean dose, dose volume histogram (DVH),

The PITV ratio, obtained by dividing prescription isodose surface volume by target volume,

In the above equation, PIV represents prescription isodose surface volume and TV refers to target volume [39]. The PITV ratio is a conformity measure, and a value of 1.0 indicates that the volume of the prescription isodose surface equals that of the PTV. A PITV ratio of 1.0 does not necessarily imply that both volumes are similar. To ensure adequate PTV coverage, this measure should always be used in conjunction with a PTV-DVH [39]. The CI and HI indices for targets were computed to assess the quality of IMRT plans. CI is defined as the ratio of target volume and the volume inside the isodose surface that corresponds to the prescription dose. CI is generally used to indicate the portion of a prescription dose that is delivered inside

*TV* (1)

*PIV* (2)

<sup>=</sup> *PIV PITV*

<sup>=</sup> *PD PTV CI*

represent target conformity and dose homogeneity [31, 35-38].

and critical organ scoring index (COSI) for the OAR (Figure 6).

$$HI = \frac{D\_{\text{max}}}{PD} \tag{3}$$

In the above equation, Dmax represents PTV maximum dose. An HI of 1 represents the ideal uniform dose within a target. Higher HI values indicate greater dose heterogeneity in the PTV [39].

TCI refers to the exact coverage of PTV in a treatment plan for a given prescription dose.

TCI is expressed as:

$$TCI = \frac{PTV\_{PD}}{PTV} \tag{4}$$

In the above equation, PTVPD represents PTV coverage at the prescription dose.

MHI is similar to HI, and is expressed as [41]:

$$\text{MHI} = \frac{D\_{\text{95}}}{D\_{\text{95}}} \tag{5}$$

In the above equation, D95 and D5 represent doses received at 95% and 5% of the volume coverage, respectively.

Conformity number (CN) is a relative measurement of dosimetric target coverage and sparing of normal tissues in a treatment plan [42]. The CN is expressed as:

$$\text{CN} = \text{TCI} \times \text{CI} = \frac{\text{PTV}\_{\text{PD}}}{\text{PTV}} \times \frac{\text{PTV}\_{\text{PD}}}{\text{PIV}} \tag{6}$$

In the above equation, PTVPD refers to PTV coverage at the prescription dose and PIV repre‐ sents prescription isodose surface volume [42].

**Figure 6.** Comparison of the various dosimetrical indices in various clinical cases.

#### **4.2 Biological analysis**

**Figure 6.** Comparison of the various dosimetrical indices in various clinical cases.

118 Evolution of Ionizing Radiation Research

#### *4.2.1. Overview of biological models*

For radiobiological model-based plan evaluation, Niemierko's equivalent uniform dose (EUD)-based NTCP and TCP model were reviewed [12, 19]. First, the DVHs from each plan were exported from the appropriate treatment planning system (TPS) for each modality. The DVHs were then imported into MATLAB version R2012a (The Math Works, Inc., Natick, MA, USA) for TCP and NTCP modeling analysis. According to Neimierko's phenomenological model, EUD is defined as:

$$ELDD = \left[\sum\_{i=1}^{} \left(V\_i EQD\_i^\*\right)\right]^{\frac{1}{\sigma}} \tag{7}$$

where *a* is a unitless model parameter that is specific to the nominal tumor structure of interest, and Vi is a unitless parameter that represents the ith partial volume receiving dose Di in Gy [12]. Since the relative volume of the whole structure of interest corresponds to 1, the sum of all partial volumes Vi will equal 1. In equation [5], the EQD is a biologically equivalent physical dose of 2 Gy defined as:

$$EQD = D \times \frac{\left(\frac{\alpha}{\beta} + \frac{D}{n\_f}\right)}{\left(\frac{\alpha}{\beta} + 2\right)}\tag{8}$$

where nf and df =D/nf are the number of fractions and the dose per fraction size of the treatment course, respectively. In this equation, α/β is the tissue-specific linear quadratic (LQ) parameter of the organ being exposed. Niemierko's TCP [12] is defined as:

$$TCP = \frac{1}{1 + \left(\frac{TCD\_{g0}}{EID}\right)^{\gamma \cdot 90}}\tag{9}$$

where TCD50 is the tumor dose required to control 50% of cancer cells when a tumor is homogeneously irradiated and γ<sup>50</sup> is a unitless model parameter that is specific to the tumor of interest. The slope of the dose response curve is described by γ50. Niemierko's NTCP [19] is defined as:

$$TCP = \frac{1}{1 + \left(\frac{TCD\_{g0}}{EID}\right)^{\gamma \cdot 90}} \tag{10}$$

where TD50 is the tolerance dose of a 50% complication rate at a specific time (e.g. 5 years in the Emami et al. normal tissue tolerance data [43]) for an entire organ of interest. This parameter also describes the slope of the dose response curve.

#### **4.3. Overall plan index**

#### *4.3.1. Overall plan index*

A comprehensive quality index (CQI) including surrounding OARs were introduced to evaluate the individual difference between OARs and PTV and the small volume of critical structures. CQI is expressed as [44]:

$$CQI = \frac{1}{N} \sum\_{l=1}^{N} QI\_l = \frac{1}{N} \sum\_{l=1}^{N} \frac{\left(D\_{\text{max}}^{plm1}\right)}{\left(D\_{\text{max}}^{plm2}\right)}\tag{11}$$

In this equation, I is the index of the critical organs, which are several critical structures in certain plan. CQI was designed to compare the ability of avoiding these organs around the PTV given the same weighting to all organs. Although CQI may overweight certain organs that are below tolerance, we chose this index as it represents a global measure of the capability of avoiding sensitive structures. Individual Qis are shown for direct comparison of each OAR. A CQI less than one indicates that HT provides a better plan for the surrounding OARs, and vice versa.

#### *4.3.2. COSI*

The COSI index accounts for both target coverage and critical organ irradiation [45]. The main advantage of this index is its ability to distinguish between different critical organs. COSI is expressed as:

$$\text{COSI} = 1 - \sum\_{1}^{n} w\_{i} \frac{V\_{i} \left( OAR \right)\_{>\text{td}}}{TCI} \tag{12}$$

where Vi (OAR)>tol is the volume fraction of OAR that receives more than a predefined tolerance dose. TCV is the volumetric target coverage, which is defined as the fractional volume of PTV covered by the prescribed isodose. Modified COSI is expressed as:

$$m\text{COSI} = \sum\_{i=1} W\_i \left( \frac{\text{COSI}\_{10} + \text{COSI}\_{20} + \dots + \text{COSI}\_{80}}{8} \right) \tag{13}$$

Although the COSI index focuses only on OARs that receive high dose region volumes, the modified COSI considers both high dose and low dose regions.

#### *4.3.3. Quality factor*

where TD50 is the tolerance dose of a 50% complication rate at a specific time (e.g. 5 years in the Emami et al. normal tissue tolerance data [43]) for an entire organ of interest. This parameter

A comprehensive quality index (CQI) including surrounding OARs were introduced to evaluate the individual difference between OARs and PTV and the small volume of critical

<sup>2</sup> 1 1 max

In this equation, I is the index of the critical organs, which are several critical structures in certain plan. CQI was designed to compare the ability of avoiding these organs around the PTV given the same weighting to all organs. Although CQI may overweight certain organs that are below tolerance, we chose this index as it represents a global measure of the capability of avoiding sensitive structures. Individual Qis are shown for direct comparison of each OAR. A CQI less than one indicates that HT provides a better plan for the surrounding OARs, and

The COSI index accounts for both target coverage and critical organ irradiation [45]. The main advantage of this index is its ability to distinguish between different critical organs. COSI is

> 1 <sup>1</sup> <sup>&</sup>gt; = -å *<sup>n</sup> <sup>i</sup> tol*

<sup>=</sup><sup>1</sup> 8

*COSI w*

covered by the prescribed isodose. Modified COSI is expressed as:

*i*

modified COSI considers both high dose and low dose regions.

*i*

dose. TCV is the volumetric target coverage, which is defined as the fractional volume of PTV

æ ö + ++ <sup>=</sup> ç ÷ è ø <sup>å</sup> <sup>L</sup> *<sup>i</sup>*

Although the COSI index focuses only on OARs that receive high dose region volumes, the

( )

(OAR)>tol is the volume fraction of OAR that receives more than a predefined tolerance

10 20 80

*COSI COSI COSI mCOSI W* (13)

*TCI* (12)

*V OAR*

*plan N N <sup>i</sup> plan i i*

1 1 = = = = å å ( ) ( )

*D*

1 max

*CQI QI N N <sup>D</sup>* (11)

also describes the slope of the dose response curve.

**4.3. Overall plan index**

120 Evolution of Ionizing Radiation Research

*4.3.1. Overall plan index*

vice versa.

*4.3.2. COSI*

expressed as:

where Vi

structures. CQI is expressed as [44]:

The quality factor (QF) introduced in this study is a dosimetrical index that can evaluate the quality of an entire plan [23]. The QF of a plan is analytically expressed as:

$$QF = \left[ 2.718 \exp\left( - \sum\_{i=1}^{N} W\_i X\_i \right) \right] \tag{14}$$

In the above equation, Xi represents all PTV indices, including PITV, CI, HI, TCI, MHI, CN, and COSI. The weighting factor (Wi ) values can be adjusted between 0 and 1 for all relatively weighted indices for a user-defined number of indices (N). A weighting factor of 1 was used for all separate indices. Thus, the QF was mainly used to compare the conformity of plans throughout various trials of a treatment.

#### **5. Radiation tolerance dose and toxicity**

The dose to critical structures plays an important role in treatment plan evaluation and is a challenging parameter in radiotherapy treatment planning. Here, Emami data [43], QUENTEC data [46], RTOG data, and the Milano study were reviewed. Doses based on tumor location in the body related to critical organs are as follows (Table 2-4).

#### **5.1. Radiation toxicities**

The assessment and reporting of toxicity plays a central role in oncology [47-50]. The founda‐ tion of toxicity reporting is the toxicity criteria system. Multiple systems have been developed in the last 30 years, and they have evolved substantially since their first introduction. The wide adoption of standardized criteria will facilitate comparison between institutions and clinical trials.

The Radiation Therapy Oncology Group (RTOG) acute radiation morbidity scoring criteria developed in 1984 consists of 13 scales that cover most body regions [51]. This system was used by the RTOG and in other clinical trials for over 30 years. The inclusion of acute radiation criteria into a multimodality grading system facilitated toxicity grading in all oncologic disciplines. This system also allows radiation oncologists to recognize and grade toxicities that were not available in the previous RTOG system. Tables 5 and 6 summarize acute toxicity categorized by body region.

The RTOG/EORTC (European Organization for Research and Treatment of Cancer) system for scoring late effects was developed in 1984 alongside the RTOG acute criteria. It contains 16 organ categories (Tables 7, 8) and has been used widely. However, its shortcomings have prompted the development of other systems.



**RTOG data QUANTEC data Emami Data Milano Data** 

**Vol. Max. Dose Toxicity Rate Toxicity Endpoint Organ TD 5/5 TD 50/5**

<60 Gy <3% Symptomatic necrosis Whole 2/3 1/3 Whole 2/3 1/3

**Organ Dose tolerance Endpoint**

122 Evolution of Ionizing Radiation Research

**Critical** 

**Dose/** 

**fx Vol. Dose Max. Dose Protocol Treated organ Critical Structure Vol. Dose/**

2 Gy 5% 60 Gy 619 Postop H&N

Brain

2 Gy 60 Gy 522 Definitive H&N 72 Gy 5% Symptomatic necrosis Brain 4500 5000 6000 6000 6500 7500

**Structure** 

Brachial

Plexus

2 Gy 66 Gy 0619, 061

4 Gy 30 Gy 937 Lung

55 Gy

Intermediate

(0.03

539

risk

<54 Gy <5% Neuropathy or necrosis Brain stem 5000 5300 6000 6500 – – Brain

stem V60 < 0.9 mL <5% grade >= 1 toxicity

meningioma

Brain stem

33 fxs 54 Gy 615 Nasopharynx D1-10 cc <= 59 Gy <5% Neuropathy or necrosis

cc)

1.8-2Gy 0.03cc

Brainstem

1.8-2Gy

60 Gy

0539, 082

High risk

meningioma,

<64 Gy <5% Neuropathy or necrosis

glioblastoma

5

(0.03

cc)

2 Gy 52 Gy (0.03 cc 1016 Oropharynx

Cochlea 33 fxs 5% 55 Gy 615 Nasopharynx Cochlea Mean

Mean 20 Gy 1016 Oropharynx

Larynx

 <50 Gy Mean <44 Gy

<20% Edema

V50 <27% <20% Edema

5 Postop H&N, definitive H&N, nasopharynx Mean

Larynx,

glottis

2 Gy 45 Gy 0619, 061

<=45 Gy <30% Sensory-

<66 Gy <20% Vocal dysfunction

<30% Aspiration Larynx (edema) 4500 4500 – 8000 – –

Larynx (necrosis

 neural hearing loss

7 Postop H&N, lung, nasopharynx 90 Gy 10% Symptomatic necrosis


**Table 2.** Radiation tolerance dose in head and neck


**RTOG data QUANTEC data Emami Data Milano Data** 

**Vol. Max. Dose Toxicity Rate Toxicity Endpoint Organ TD 5/5 TD 50/5**

Long-term

salivary

function

<25%

Long-term

salivary

Parotid

gland 3200 3200 – 4600 4600 Parotid

Mean

grade 2

xerostom

ia, >75%

function

al loss

Late

dose <

26 Gy

function

<25%

Long-term

salivary

function

<25%

**Organ Dose tolerance Endpoint**

124 Evolution of Ionizing Radiation Research

**Critical** 

**Dose/** 

**fx Vol. Dose Max. Dose Protocol Treated organ Critical Structure Vol. Dose/**

**Structure** 

2 Gy

one

<26 Gy

gland

**Table 2.** Radiation tolerance dose in head and neck

Mean

0619,

Postop H&N,

0522,

definitive H&N,

Mean

<=25 Gy <20%

1016

oropharynx

Parotid,

bilateral

Parotid

Glands

2 Gy

one

<30 Gy 0619, 0522 Postop H&N, definitive H&N Mean

<=39 Gy <50%

gland

2 Gy

ned 20

<20 Gy 0619, 0522 Postop H&N, definitive H&N Parotid, unilateral

 Mean

<=20 Gy <20%

cc

Pharynx,

Pharynx,

2 Gy 33% 50Gy 1016 Oropharynx Pharyngeal constrictors

 Mean

<=50 Gy <20%

Symptomatic

dysphagia

and

aspiration

posterior

wall

Retina

1.8-2 Gy

Spinal

Cord 1.8 Gy 45 Gy 0623, 0615 Lung, Nasopharynx

50 Gy 0.20% Myelopathy

(20cm) (10cm) (5 cm)

(10cm) (5 cm)

Spinal cord Max < 50 Gy <5% grade >= 3 toxicity

Spinal

cord

–

Cervical

EUD <

<5%

spinal

52 Gy,

Max < 55

grade >=

3 toxicity

Gy

cord

Spinal cord

2 Gy Mean <39 Gy 1016 Oropharynx 69 Gy 50% Myelopathy

definitive H&N 60 Gy 6% Myelopathy

2 Gy

Submandi

bular

Gland

48 Gy

0619,

Postop H&N,

0522

(0.03

cc)

1.8-2 Gy

2 Gy 15% 60Gy 1016 Oropharynx

2 Gy Mean 45Gy 1016 Oropharynx

45 Gy

Intermediate

(0.03

539

risk

meningioma

High risk

meningioma,

Retina 4500 – – 6500 – –

glioblastoma,

nasopharynx

cc)

50 Gy

0539,

(0.03

0825,

cc)

0615

postcricoid 33 fxs 45 Gy 615 Nasopharynx

Combi

V50



**RTOG data QUANTEC data**

**fx Vol. Dose Max. Dose Protocol Treated organ Critical Structure Vol. Dose/Vol.**

**Critical** 

**Dose/** 

**Structure** 

Lung,

3 cm

CW to

413 Breast

2 Gy V20 20% 630 Sarcoma Mean 7 Gy 5% Symptomatic pneumonitis

Lung

2 Gy Mean 20 Gy 617 Lung Mean 20 Gy 20% Symptomatic pneumonitis

3 Gy Mean 20 Gy 937 Lung Mean 24 Gy 30% Symptomatic pneumonitis

3 Gy V20 <= 30% 937 Lung Mean 27 Gy 40% Symptomatic pneumonitis

Small bowel

(individual

V15 <120 cc <10% Grade 3+ toxicity Small intestine

loops)

Small bowel

(peritoneal

V45 <195 cc <10% Grade 3+ toxicity (obstruction, perforation)

cavity)

3 Gy 150 cc 30 Gy 937 Lung

3 Gy 100 cc 35 Gy 937 Lung

3 Gy 50 cc 40 Gy 937 Lung

Small

Bowel

3 Gy 1 cc 45 Gy 937 Lung

4 Gy 100 cc 30 Gy 937 Lung

4 Gy 50 cc 35 Gy 937 Lung Stomach

4 Gy 1 cc 40 Gy 937 Lung Stomach D100 <45 Gy <7% Ulceration (ulceration,p

erforation) �

 7000

2 Gy V20 37% 0617, 0623 Lung Mean 13 Gy 10% Symptomatic pneumonitis

Lungs,

total

field

single 2 Gy

V20 <=30% <20% Symptomatic pneumonitis

Rib cage – – 5000 – – 6500

 6500 Lung

V30 < 10- 15% MLD < 10-20 Gy

V20 < 25- 30% Late grade 3 in <5-10%

**Max. Dose Toxicity Rate Toxicity Endpoint Organ TD 5/5 TD 50/5**

**Emami Data**

**Milano Data**

**Organ Dose tolerance Endpoint**

V13 <

Late

126 Evolution of Ionizing Radiation Research

grade 2 in

<10-20%

40%

Physical and Radiobiological Evaluation of Radiotherapy Treatment Plan http://dx.doi.org/10.5772/60846 127


**Table 3.** Radiation tolerance dose in abdomen


**RTOG data QUANTEC data**

**fx Vol. Dose Max. Dose Protocol Treated organ Critical Structure Vol. Dose/Vol.**

**Critical** 

**Table 3.** Radiation tolerance dose in abdomen

**Dose/** 

**Structure** 

Liver

1.8 Gy 50% 35 Gy 436 Esophagus

Liver

Mean <28 Gy <5%

Mean <36 Gy <50%

3 Gy >700 cc <18 Gy 937 Lung Mean <42 Gy <50%

Mean <30-32 Gy

<5%

normal liver

 5500 Liver

function)

RILD (in

normal liver

function)

RILD (in

Child-Pugh A

or HCC)

RILD (in

Child-Pugh A

or HCC)

RILD (in

**Max. Dose Toxicity Rate Toxicity Endpoint Organ TD 5/5 TD 50/5**

**Emami Data**

**Milano Data**

128 Evolution of Ionizing Radiation Research

**Organ Dose tolerance Endpoint**

1/3: 40-80

Gy

Late

grade 3-4

2/3: 30-50

Gy

liver

toxicity <

5%

3/3: 25-

35%



**RTOG data Handbook QUANTEC data**

**Critical** 

External

1.8 Gy 50% 20 Gy 529 Anus

1.8 Gy 35% 30 Gy 529 Anus

1.8 Gy 5% 40 Gy 529 Anus

1.8 Gy 50% 30 Gy 529 Anus

1.8 Gy 15% 30 Gy 418 Endometr

Femoral

 ial Slipped epiphysis

Adult 42-50 Gy

head

1.8 Gy 40% 40 Gy 822 Rectum Avascular necrosis 30-40 Gy

1.8 Gy 35% 40 Gy 529 Anus

1.8 Gy 25% 45 Gy 822 Rectum

1.8 Gy 10% 50 Gy 534 Postop prostate

1.8 Gy 5% 44 Gy 529 Anus

RTOG

Prostate

Group

Consensu

s 2009

2 Gy 5% 60 Gy 630 Sarcoma

1.8 Gy 50 Gy 822 Rectum

1.8 Gy 45 Gy 712 Bladder

1.8 Gy 50% 30 Gy 529 Anus

1.8 Gy 35% 40 Gy 529 Anus

1.8 Gy 5% 50 Gy 529 Anus

1.8 Gy 50% 35 Gy 529 Anus Colon

Mean

dose to

95%

<50 Gy

<35%

erectile

perforation,

ulceration

dysfunction

Severe

obstruction,

gland

Penile

bulb

 <70 Gy

<55%

erectile

dysfunction

<35%

erectile

dysfunction

Severe

Severe

1.8 Gy 5% 50 Gy 529 Anus D90 <50 Gy

Bulb 1.8 Gy Mean 52.5 Gy 415 Prostate D60-70

4500 – 5500

5500 – 6500

Iliac

crests

Large

1.8 Gy 35% 40 Gy 529 Anus

Bowel

Penile

1.8 Gy 5% 50 Gy PMID 18947938

 <25 Gy

genitalia

Femoral

Head

**Structure Dose/fx Vol. Dose Max. Dose Protocol Treated organ Organ Partial Organ Tolerance (1.8 – 2.0 Gy/fx) Critical Structure**

**Vol. Dose/**

**Vol. Max. Dose Toxicity Rate Toxicity Endpoint Organ TD 5/5 TD 50/5**

**Organ Dose tolerance End point**

**Emami Data**

**Milano Data**

130 Evolution of Ionizing Radiation Research

#### Physical and Radiobiological Evaluation of Radiotherapy Treatment Plan http://dx.doi.org/10.5772/60846 131


**Table 4.** Radiation tolerance dose in pelvis


**Table 5.** Summary of RTOG acute toxicity criteria for head and neck region.

**RTOG data Handbook QUANTEC data**

**Structure Dose/fx Vol. Dose Max. Dose Protocol Treated organ Organ Partial Organ Tolerance (1.8 – 2.0 Gy/fx) Critical Structure Vol.**

1.8 Gy 200 cc 30 Gy 529 Anus

Small

Small

Small

bowel

V15 <120 cc

<10% Grade 3+ toxicity Small intestine

4000 – 5000

5500 – 6000

(individual

loops)

Small

bowel

V45 <195 cc

<10% Grade 3+ toxicity obstruction,

 perforation

(peritoneal

cavity)

volume 50 Gy

bowel

1.8 Gy 150 cc 35 Gy 529 Anus Whole <40 Gy

**Critical** 

**Table 4.** Radiation tolerance dose in pelvis

Small

1.8 Gy 180 cc 35 Gy 822 Rectum

1.8 Gy 100 cc 40 Gy 822 Rectum

1.8 Gy 20 cc 45 Gy 529 Anus

1.8 Gy 65 cc 45 Gy 822 Rectum

1.8 Gy 50 Gy 0822, 052

1.8 Gy 52 Gy PMID 18947938

 9 Rectum, anus

RTOG

Prostate

Group

Consensus

2009

1.8 Gy 30% 40 Gy 418 Endometrial

Skin,

longitudi

2 Gy 50% 20 Gy 630 Sarcoma

Testis 2 Gy 50% 3 Gy 630 Sarcoma Extremity Circumfe

Vulva 2 Gy 50% 30 Gy 630 Sarcoma Bone

Anus 2 Gy 50% 30 Gy 630 Sarcoma Ablation

bearing 2 Gy 50% 50 Gy 630 Sarcoma Bone Cortex 50 Gy

Joints 2 Gy 50% 50 Gy 630 Sarcoma Joint space Fibrotic constricti

 on 40-45 Gy

Bone,

weight-

marrow

 >40 Gy

Whole

abdomen <30 Gy

 rential 20-30 Gy

nal

Bowel

 **Dose/**

**Emami Data** **Vol. Max. Dose Toxicity Rate Toxicity Endpoint Organ TD 5/5 TD 50/5 Organ**

**Milano Data**

132 Evolution of Ionizing Radiation Research

 **Dose tolerance End point**


**Table 6.** Summary of RTOG acute toxicity criteria for body region.

Table 6. Summary of RTOG acute toxicity criteria for body region.



Table 7

Table 6. Summary of RTOG acute toxicity criteria for body region.

**Organ/Tissue Grade 0 Grade 1 Grade 2**

Anorexia with <=5% weight loss

from pretreatment baseline/nausea

Anorexia with <=15% weight loss

from pretreatment baseline/nausea

and/or vomiting requiring

antiemetics/abdominal pain

requiring analgesics

Diarrhea requiring

parasympatholytic drugs (e.g.,

Lomotil)/mucous discharge not

necessitating sanitary pads/rectal

or abdominal pain requiring

analgesics

Persistent cough requiring

narcotic, antitussive

agents/dyspnea with minimal

effort but not at rest

Frequency of urination or nocturia

that is less frequent than every

hour. Dysuria, urgency, bladder

spasm requiring local anesthetic

(e.g., Pyridium)

Symptomatic with EKG changes

and radiologic findings of

congestive heart failure or

pericardial disease/no specific

treatment required

not requiring antiemetics/abdominal

discomfort not requiring

parasympatholytic drugs or

analgesics

Increased frequency or change in

quality of bowel habits not requiring

medication/rectal discomfort not

requiring analgesics

Lung No change Mild symptoms of dry cough or dyspnea on exertion

Genitourinary No change

Heart No change over baseline

Frequency of urination or nocturia

twice pretreatment habit/dysuria,

urgency not requiring medication

Asymptomatic but objective

evidence of EKG changes or

pericardial abnormalities without

evidence of other heart disease

Lower G.I No change

Upper G.I No change

**Table 6.** Summary of RTOG acute toxicity criteria for body region.

**Grade 3** Anorexia with >15% weight loss from

pretreatment baseline or requiring N-G tube or

Ileus, subacute or acute obstruction,

performation, GI bleeding requiring

transfusion/abdominal pain requiring

tube decompression or bowel

diversion

parenteral support. Nausea and/or vomiting

requiring tube or parenteral support/abdominal

pain, severe despite medication/hematemesis or

melena/abdominal distention (flat plate

radiograph demonstrates distended bowel

loops)

Diarrhea requiring parenteral support/severe

Acute or subacute obstruction, fistula

or perforation; GI bleeding requiring

transfusion; abdominal pain or

tenesmus requiring tube

decompression or bowel diversion

Severe respiratory

insufficiency/continuous oxygen or

assisted ventilation

Hematuria requiring

transfusion/acute bladder obstruction

not secondary to clot passage,

ulceration, or necrosis

Congestive heart failure, angina

pectoris, pericardial disease,

arrhythmias not responsive to nonsurgical measures

mucous or blood discharge necessitating

sanitary pads/abdominal distention (flat plate

radiograph demonstrates distended bowel

loops)

Severe cough unresponsive to narcotic

antitussive agent or dyspnea at rest/clinical or

radiologic evidence of acute

pneumonitis/intermittent oxygen or steroids

may be required

Frequency with urgency and nocturia hourly or

more frequently/dysuria, pelvis pain, or bladder

spasm requiring regular, frequent narcotic/gross

hematuria with/without clot passage

Congestive heart failure, angina pectoris,

pericardial disease responding to therapy

**Grade 4**

134 Evolution of Ionizing Radiation Research


**Table 8.** Summary of RTOG late toxicity criteria by body region.

#### **6. Radiation treatment plan analysis programs**

In modern radiation therapy, physical dose indices, such as mean doses, dose-volume histograms (DVHs), and isodose distribution charts, are often used for treatment plan evaluation. DVHs provide dose volume coverage information. However, they fail to provide information regarding hot spots and dose homogeneity. When reviewing physical dose indices, the resulting biological objectives, such as tumor control rate and normal tissue complication probability, must be indirectly estimated based on clinical experience and knowledge. In some competing plans, it is possible that a similar mean dose, maximum dose, or minimum dose might have significantly different radiobiological outcomes. To facilitate the direct and accurate comparison and ranking of treatment plans, radiobiological models for treatment plan evaluation have been introduced. These radiobiological models are based on the idea that the radio-sensitivity of different organs should be taken into account. As a result, the physical dose delivered to an organ is directly associated with the dose–response proba‐ bility of inducing complications in normal tissues. Many programs have been designed and developed to calculate both dosimetrical and biological indices, as shown in Table 9 [10-29].

#### **7. Multidisciplinary strategies: Planning decision support concept**

#### **7.1. Methods could be used for planning a decision support system**

In this section, we highlight dosimetrical and biological models in radiation oncology treat‐ ment planning, with focus on the methodological aspects of prediction model development. In radiation treatment planning analysis, dose volume histograms were the most widely used quantitative results. To comprehensively evaluate a certain DVH, we proposed several dosimetrical and biological models in the earlier sections. For dosimetrical models, there were PTTV, CI, and TCI for target coverage index, and MHI, HI for homogeneity index and COSI, QF, and CQI for overall index. For radiobiological models, there were TCP and NTCP for tumor or critical structures, representatively. There were still other factors like treatment time, planning time, or overall moniter unites irradiated in patients could be helpful for making more reasonable decision. Some characteristic prognostic and predictive factors like radiationinduced organ toxicities were discussed in earlier sections. We also enumerate the normal tissue tolerance criteria including QUENTEC and EMAMI database.

#### **7.2. The need of plan decision support concept in RT**

**Organ/Tiss**

**Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 Grade 5** 

136 Evolution of Ionizing Radiation Research

Lung None Asymptomatic or mild symptoms (dry cough); slight radiographic appearances Moderate symptomatic fibrosis or pneumonitis (severe cough); low grade fever; patchy radiographic appearances Severe symptomatic fibrosis or pneumonitis; dense radiographic changes Severe respiratory insufficiency/continuous O2/assisted ventilation Death related to radiation effects

Moderate angina on effort; mild

Severe angina; pericardial effusion;

Tamponade/severe heart

Death related to

radiation effects

failure/severe constrictive

pericarditis

Necrosis/perforation fistula Death related to radiation effects

constrictive pericarditis; moderate heart

failure; cardiac enlargement; EKG

abnormalities

Severe fibrosis; able to swallow only

liquids; may have pain on swallowing;

dilation required

Obstruction or bleeding, requiring surgery Necrosis/perforation fistula Death related to radiation effects

pericarditis; normal heart size; persistent

abnormal T wave and ST changes; low

ORS

Unable to take solid food normally;

swallowing semi-solid food; dilation

may be indicated

Moderate diarrhea and colic; bowel

movement >5 times daily; excessive rectal

mucus or intermittent bleeding

Liver None Mild lassitude; nausea, dyspepsia; slightly abnormal liver function Moderate symptoms; some abnormal liver function tests; serum albumin normal Disabling epatitis insufficiency; liver function tests grossly abnormal; low albumin; edema or ascites Necrosis/hepatic coma or encephalopathy Death related to radiation effects

**ue** 

**Table 8.** Summary of RTOG late toxicity criteria by body region.

Heart None

Esophagus None

Small/large

intestine None

Kidney None

Asymptomatic or mild symptoms;

transient T wave inversion and ST

changes; sinus tachycardia >110

Mild fibrosis; slight difficulty in

swallowing solids; no pain on

swallowing

Mild diarrhea; mild cramping; bowel

movement 5 times daily; slight rectal

discharge or bleeding

Transient albuminuria; no

Persistent moderate albuminuria (2+);

mild hypertension; no related anemia;

Severe albuminuria; severe hypertension

persistent anemia (< 10%); severe renal

Malignant hypotension;

Death related to

radiation effects

uremic coma/urea > 100%

failure; urea > 60 mg%; creatinine > 4.0

mg%; creatinine clearance < 50%

moderate impairment of renal function;

urea > 36–60mg%; creatinine clearance

(50–74%)

Bladder None Slight epithelial atrophy; minor telangiectasia (microscopic hematuria) Moderate frequency; generalized telangiectasia; intermittent macroscopic hematuria Severe frequency and dysuria; severe generalized telangiectasia (often with petechiae); frequent hematuria; reduction in bladder capacity (< 150 cc) Necrosis/contracted bladder (capacity < 100 cc); severe hemorrhagic cystitis Death related to radiation effects

Bone None Asymptomatic; no growth retardation; reduced bone density Moderate pain or tenderness; growth retardation; irregular bone sclerosis Severe pain or tenderness; complete arrest of bone growth; dense bone sclerosis Necrosis/spontaneous fracture Death related to radiation effects

Joint None Mild joint stiffness; slight limitation of movement moderate stiffness; intermittent or moderate joint pain; moderate limitation of movement Severe joint stiffness; pain with severe limitation of movement Necrosis/complete fixation Death related to radiation effects

hypertension; mild impairment of renal

function; urea 25–35 mg%; creatinine

1.5–2.0 mg%; Creatinine clearance >

75%

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of planning decisionsupport systems based on prediction models of treatment outcome [55-57]. In radiation oncology, these models combine both predictive and prognostic data factors from dosimetri‐ cal, biological, imaging, and other sources to achieve the highest accuracy to predict tumor response and follow-up event rates. The central challenge, however, is how to integrate diverse, multimodal information (imaging, dosimetrical, biological, and other data) in a quantitative manner to provide specific clinical predictions that accurately and robustly

Table 9. Review of previous programs

estimate patient outcomes as a function of the possible decisions. Currently, many prediction models are being published that consider factors related to disease and treatment, but without standardized assessments of their robustness, reproducibility, or clinical utility [58]. Conse‐ quently, these prediction models might not be suitable for clinical decision-support systems for routine care.



estimate patient outcomes as a function of the possible decisions. Currently, many prediction models are being published that consider factors related to disease and treatment, but without standardized assessments of their robustness, reproducibility, or clinical utility [58]. Conse‐ quently, these prediction models might not be suitable for clinical decision-support systems

for routine care.

138 Evolution of Ionizing Radiation Research

Table 9. Review of previous programs

**Program** 

**Patient** 

**Data** 

**Data** 

**Compatible** 

**3D image** 

**Physical Biological** 

**Overall** 

**Multi-RTP Analysis database** 

**Statistical analysis** 

**Independence** 

**from GUI** 

**Normal** 

**Survival** 

**statistic**

**statistic** 

× × × × × × MatLab Anil

Pyakuryal

(23) 2010

c.edu/~apyaku1

/

http://www2.ui

**Platform Author Paper Year Others** 

**information** 

HART ×

**format** 

AAPM/

RTOG,

Pinnacle × √

√

√

√

DicomR

T

AAPM/

Pinnacle,

expand

function

× √

√

× × × × √

× × √

MatLab,

Fortran,

Joseph O.

Deasy (10) 2003 http://www.cerr

 .info/about.php

C/C++, Java

with

DicomRT

toolbox

RTOG,

DicomR

T

(toolbox)

Matlab's

humanreadable

DREES √

data

No × × × × √

× × × × √

× MatLab Joseph O.

Deasy (11) 2006 http://cerr.info/

 drees/about.php

structure

s

× DVH file specialized

format

AAPM/

LANTIS

RTOG,

and IMPAC

× × × × √

× × × × √

× MatLab Olivier

Gayou (13) 2007

DicomR

based on

T

Eclipse,

Pinnacle,

Eclipse,

Dose Volume

Histogram

×

Tomo,

Pinnacle,

× × × × √

× × × × × × MatLab Jin Sung

Kim (17) 2008

http://mpjinsun

g.tistory.com/en

try/DVH-Analyzer-v10

DVH

Tomo

files

Analyzer

DRESS

× × × × √

× × × × × × MatLab Andrzej

Niemierko

(12) 2007

EUD-based

mathematical

model

EUCLID √

CERR ×

**compatibility** 

**with PACS** 

**module** 

**DVH** 

**Physical** 

**TCP/NTCP**

**calculator** 

**index** 

**Review of previous programs** 

**Input system Dicom RT platform Plan comparison Plan analysis Program features Paper publication** 


**Table 9.** Review of previous programs

Decision making in radiotherapy is mainly based on clinical features, such as the patient performance status, organ function, and grade and extent of the tumor (e.g., as defined by the TNM system). In almost all studies, such features have been found to be prognostic for survival and development of toxicity [59, 60]. Consequently, these features should be evaluated in building robust and clinically acceptable radiotherapy prognostic and predictive models. Moreover, measurement of some clinical variables, such as performance status, can be captured with minimal effort.

Toxicity measurements and scoring should also build on validated scoring systems, such as the Common Terminology Criteria for Adverse Events (CTCAE), which can be scored by the physician or patient [50, 61]. Indeed, a meta-analysis showed that high-quality toxicity assessments from observational trials are similar to those of randomized trials. [45, 46] However, a prospective protocol must clarify which scoring system was used and how changes in toxicity score were dealt with over time with respect to treatment. Finally, to ensure a standardized interpretation, the reporting of clinical and toxicity data and their analyses should be performed in line with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement for observational studies and genetic-association studies, which is represented as checklists of items that should be addressed in reports to facilitate the critical appraisal and interpretation of these types of studies (Figure 7).

**Figure 7.** Design of planning decision support concept in radiotherapy treatment planning.

**Review of previous programs** 

**Input system Dicom RT platform Plan comparison Plan analysis Program features Paper publication** 

**Program** 

**Table 9.** Review of previous programs

**Patient** 

**Data** 

**Data** 

**Compatible** 

**3D image** 

**Physical Biological** 

**Overall** 

**Multi-RTP Analysis database** 

**Statistical analysis** 

**Independence** 

**from GUI** 

**Normal** 

**Survival** 

**statistic**

**statistic** 

× × × × × × MatLab Su FC (25) 2010

× × × × × × MatLab Holloway

LC (15) 2012

**Platform Author Paper Year Others** 

140 Evolution of Ionizing Radiation Research

**information** 

**format** 

**compatibility** 

BEUDcal × DVH file DVH file × × √

Comp Plan × DVH file

CalcNTCP ×

Manual

Manual

× × × × √

× × × × × × Visual Basic

× × × × × × Microsoft

Ecel Chang JH

(53) 2011

gle.com/site/rad

biomod/home

https://sites.goo

 Khan HA

(16) 2007

input

RADBIOMOD × DVH file

BioSuite × DVH file Pinnacle,

RTToolbox √

RT

developed

× √

√

× √

× × √

× × √

C++ Lanlan,

Zhang (28) 2013

planning

system

Dicom

Eclups

Virtuos, our

in-house

× × √

× √

× × × × × √

C++ J Uzan (54) 2012

input

Manual

× × × √

√

input

DVH file in

× × × × √

Excel

× √

**with PACS** 

**module** 

**DVH** 

**Physical** 

**TCP/NTCP**

**calculator** 

**index** 

Despite the challenges that remain, the vision of predictive models leading to plan decision support concept that are continuously updated via rapid learning on large datasets is clear, and numerous steps have already been taken. These include universal data-quality assurance programs and semantic interoperability issues. However, we believe that this truly innovative journey will lead to necessary improvement of healthcare effectiveness and efficiency. Indeed, investments are being made in research and innovation for health-informatics systems, with an emphasis on interoperability and standards for secured data transfer, which shows that "eHealth" will be among the largest health-care innovations of the coming decade. Accurate, externally validated prediction models are being rapidly developed, whereby multiple features related to the patient's disease are combined into an integrated prediction. The key, however, is standardization—mainly in data acquisition across all areas, including dosimet‐ rical-based and biological-based models, patient preferences, and possible treatments. These crucial features are the basis of validating a plan decision support system, which, in turn, will stimulate developments in rapid-learning health care and will enable the next major advances in shared decision making.

#### **8. Conclusion**

Plan comparison studies still remain controversial. The main reason for this is because plan parameters, optimization methods, and OAR constraints are difficult to clearly define. Many researchers have focused on the influence of planning parameters on the results of treatment plans [62-64]. For instance, Gutiérrez et al. [65] reported that the use of a field width of 1 cm resulted in dosimetrically superior plans for brain irradiation compared to plans that use a field width of 2.5 cm. More recently, Skorska and Piotrowski studied the influence of treat‐ ment-planning parameters on plan qualities for prostate cancer patients using helical tomo‐ therapy [66]. This study revealed that using a field width of 1 cm, instead of 5 cm, leads to decreases in the D20%, D40%, D60%, and D80% of the small intestine by 2.45%, 8.48%, 6.36%, and 5%. This results in 1.22Gy, 4.24Gy, 3.18Gy, and 2.50Gy, respectively, for the prescribed dose of 50 Gy. Another bias of plan comparison studies is that the quality of a planner's abilities and planning techniques may vary. Performing repeat planning processes and using multiple planners to cross check would minimize such bias. The use of OAR dose tolerance guidelines, such as RTOG or QUENTEC protocols, would minimize human error.

Other major issues among plan comparison studies are the method of plan analysis and evaluation. Many studies have focused on developing a simple index that represents the overall quality of plans [14, 19, 41, 42, 67]. However, none of these plans are easily used in a clinic. There is a need for programs that can easily calculate dosimetrical and biological indices [10, 12, 13, 15, 16, 22-25, 28, 68, 78-82].

There is a growing trend of studying the relationships between treatment plan results and clinical outcomes, such as toxicities, survival, and patterns of failure [69-77]. Such studies may help physicians and physicists learn more about the influence of plan results and plan quality on patient treatment.

#### **Acknowledgements**

Despite the challenges that remain, the vision of predictive models leading to plan decision support concept that are continuously updated via rapid learning on large datasets is clear, and numerous steps have already been taken. These include universal data-quality assurance programs and semantic interoperability issues. However, we believe that this truly innovative journey will lead to necessary improvement of healthcare effectiveness and efficiency. Indeed, investments are being made in research and innovation for health-informatics systems, with an emphasis on interoperability and standards for secured data transfer, which shows that "eHealth" will be among the largest health-care innovations of the coming decade. Accurate, externally validated prediction models are being rapidly developed, whereby multiple features related to the patient's disease are combined into an integrated prediction. The key, however, is standardization—mainly in data acquisition across all areas, including dosimet‐ rical-based and biological-based models, patient preferences, and possible treatments. These crucial features are the basis of validating a plan decision support system, which, in turn, will stimulate developments in rapid-learning health care and will enable the next major advances

Plan comparison studies still remain controversial. The main reason for this is because plan parameters, optimization methods, and OAR constraints are difficult to clearly define. Many researchers have focused on the influence of planning parameters on the results of treatment plans [62-64]. For instance, Gutiérrez et al. [65] reported that the use of a field width of 1 cm resulted in dosimetrically superior plans for brain irradiation compared to plans that use a field width of 2.5 cm. More recently, Skorska and Piotrowski studied the influence of treat‐ ment-planning parameters on plan qualities for prostate cancer patients using helical tomo‐ therapy [66]. This study revealed that using a field width of 1 cm, instead of 5 cm, leads to decreases in the D20%, D40%, D60%, and D80% of the small intestine by 2.45%, 8.48%, 6.36%, and 5%. This results in 1.22Gy, 4.24Gy, 3.18Gy, and 2.50Gy, respectively, for the prescribed dose of 50 Gy. Another bias of plan comparison studies is that the quality of a planner's abilities and planning techniques may vary. Performing repeat planning processes and using multiple planners to cross check would minimize such bias. The use of OAR dose tolerance guidelines,

Other major issues among plan comparison studies are the method of plan analysis and evaluation. Many studies have focused on developing a simple index that represents the overall quality of plans [14, 19, 41, 42, 67]. However, none of these plans are easily used in a clinic. There is a need for programs that can easily calculate dosimetrical and biological indices

There is a growing trend of studying the relationships between treatment plan results and clinical outcomes, such as toxicities, survival, and patterns of failure [69-77]. Such studies may help physicians and physicists learn more about the influence of plan results and plan quality

such as RTOG or QUENTEC protocols, would minimize human error.

[10, 12, 13, 15, 16, 22-25, 28, 68, 78-82].

on patient treatment.

in shared decision making.

142 Evolution of Ionizing Radiation Research

**8. Conclusion**

This chapter was developed by a special working group of the Korea University Medical Physics Lab from the department of radiation oncology, college of medicine, Korea University, Seoul, 136-705, Korea. Members of the planning index study working group include Kwang Hyeon Kim, M.S., Kyung Hwan Chang, Ph.D., and Jang Bo Shim, M.S.

#### **Author details**

Suk Lee\* , Yuan Jie Cao and Chul Yong Kim

\*Address all correspondence to: sukmp@korea.ac.kr

Department of Radiation Oncology, College of Medicine, Korea University, Seoul, Korea

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