**Predictive Solution for Radiation Toxicity Based on Big Data**

Suk Lee, Kwang Hyeon Kim, Choi Suk Woo, Jang Bo Shim, Yuan Jie Cao, Kyung Hwan Chang and Chul Yong Kim

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67059

#### **Abstract**

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients' cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system.

**Keywords:** big data, machine learning, radiation toxicity, predictive solution, radiation treatment planning

### **1. Introduction**

#### **1.1. Definition of big data and each clinical application overview**

Trifiletti et al. [1] describe the big data as follows: a lot of information and massive data sets or number of grains of sand in the earth for human analysis with 1012–1018 bytes [1].

Murdoch listed that the big data are the inevitable application in healthcare field as four things [10]:


This trend is called to be "big bang" to adapt and research for big data and machine learning in medicine. Especially, machine learning is widely used [4–6]. Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patient cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we describe various clinical cases and key machine learning algorithms in radiation oncology in this chapter.

First, what is the big data for a single patient in hospital? The data type and its size for each patient can be summarized in **Table 1**. In case of radiation oncology, imaging and treatment planning information could be a major treatable data [15].


**Table 1.** Data type and its size for each patient. In case of radiation oncology, imaging and treatment planning information could be the major treatable data [15].

Second, we would like to explain radiation treatment planning and decision support system in radiation oncology. When we set up treatment planning with parameters for patient cure in radiotherapy, it is based on the radiation treatment planning (RTP) system. The clinical target volume (CTV) and planning target volume (PTV) have to be targeted by maximum radiation, and critical organs have to be radiated by minimum. It is established based on the correlation between the dose and volume, also known as dose-volume histogram (DVH). At this process, considered parameters are the prescription dose (PD), dose distribution, dose fractionation, dose constraints at normal tissue, target volume, treatment machine setting values, etc. [2, 16].

Third, when the finish treatment planning has been completed, the DVH is acquired. The dose-volume distribution will be the basic information whether it could be use or not. But, these limited information do not give hot spot for target volume, conformity, homogeneity, and so on. And, the tumor control probability (TCP) and normal tissue complication probability (NTCP) have to be analyzed in parallel. As the knowledge-based judgment, other rival plans could be generated again [32]. Thus, some decision support system is needed to select the best treatment plan for personalized patient care. These decision support systems (BIOPLAN, CERR, DRESS, Slicer RT, etc.) that provide different functions to analyze treatment efficiency. And these were being researched and studied as the software program since the early 2000s to up to date [3, 26–28].

But now, these decision support systems are needed to add to specific function using machine learning and historical treatment results and previously mentioned big data information to predict patient toxicity or complication after radiation treatment.
