**3.3 How far are the reported results by the investigators correct?**

The reported prediction results [15–38, 41–47, 52–60, 63–67, 71, 72, 74–77, 79–83, 85–88, 89–95, 97–102] by investigators indicate the performance of these predictive models on data that used in modeling. However, these ML models can suffer from different data biases which may lead to lack of generalizability. A machine learning system trained on local datasets only may not be able to predict (reproduce) the needs of outof-sample datasets (new datasets that are not presented in the training data). External validation of models in cohorts, which were acquired independently from the discovery cohort (e.g., from another institution) is considered the gold standard for true estimates of performance and generalizability of prediction models [6]. The application of different algorithms to the same dataset may yield variable results for predictors found to be significantly associated with the outcome of interest [6, 105]. However, this may

**61**

*Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine*

realistic confidence levels with implications for their practical clinical value [6].

also suggest a potential limitation of self-critical assessment of published ML models or

Although promising and improving accuracy results of many ML-based predictive models in radiation oncology have been reported [18, 19, 21, 31–38, 41–43, 53–55, 74, 79–83, 85, 86, 89–95, 97–102], the effective applications of these methods in day-to-day clinical practice are very few yet. Such an example of a recently deployed commercial product into clinical use is Quick Match (Siris Medical, Redwood City, CA, USA) [68]. A private initiative, such as IBM's Watson, is already used in some institutions such as the Memorial Sloan Kettering Cancer Center in New York [106–109]. Watson Oncology [108] is a cognitive AI computing system designed to support the broader oncology community of physicians as they consider treatment options with their patients. To improve the prediction accuracy of these reported results, more training and validation datasets from multi-institution are required. Such frameworks, e.g., [50] to compare these methods on standard consensus data to establish benchmarks for evaluating different models would definitely lead to improving these results and developing robust toolkits/systems. It is anticipated to see ML and AI tools very soon settled more effectively with the indispensable role in the routine clinical practice for the benefit of patients, society, and the profession.

The machine learning systems have been developed and deployed to do jobs on their own. Automated clinical processes in radiation oncology could be auto-piloted with driving technologies to execute automated tasks. For example, data-driven planning [63–67] is not fully automated at present as it requires expert oversight and/or intervention to ensure safely deliverable treatment plans. One challenge of achieving full automatic planning using reinforcement learning lies in the close integration and need for robust TPSs [14]. The future vision is toward a fully-automated planning process, from contouring to plan creation. Machine-based and patientbased virtual QA can have profound implications on the current IMRT/VMAT process. The automated process nature would definitely lead to expediting radiation

oncology workflow and reduce the time burden of human intervention [62].

**3.6 Impact on clinical decision-making support toward precision medicine in** 

ML tools for computer-aided detection/diagnosis [15–22] as "second opinion" systems for clinical decision-making support would undoubtedly enhance the radiologists' performance and hence improved diagnostic performance. The emerging paradigms in radiomics for therapeutic outcome predictions (i.e., patient's survival, decrease recurrence, late complication, etc.) [97–102] for individual patients would maximize its potential impact on precision radiotherapy. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis [97]. Therefore, physicians may prescribe a more or less intense radiation regimen for an individual based on model predictions of local control benefit and toxicity risk [2], which would be considered for the optimal treatment planning design process and hence improving the quality of life for radiotherapy cancer patients. Effective implementation of adaptive radiation therapy with ML [85–88] can also further improve the precision in the radiotherapy treatments. The pre-planning prediction of dosimetric tradeoffs to assist physicians and patients

*DOI: http://dx.doi.org/10.5772/intechopen.84629*

**3.4 How would the reported results be improved?**

**3.5 Impact on automating the clinical process**

**radiation oncology**

also suggest a potential limitation of self-critical assessment of published ML models or realistic confidence levels with implications for their practical clinical value [6].
