# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.8 Outcome Prediction  
Outcome prediction is one of the major applications of AI methods, being analyzed in about 24% of papers, mostly pertaining to ML approaches. Examples of the outcome prediction areas that have been investigated include overall survival [105], progression [106], recurrence [107], toxicity [23; 108; 109; 110], biomarker identification [111], detection and classification of tumors [112; 113], mutation prediction, treatment response [114; 115; 116], patient and risk stratification [117], and quality of life [118].  
Most papers (about 60%) have focused on applying ML or DL models to clinical, genomic, and mixed (e.g., clinical and treatment) data. For example, [PERSON] et al. [23] predicted the risk of xerostomia in head and neck cancer patients, [PERSON] et al. [108] evaluated the effects of multiple single nuclear polymorphisms (SNPs) on the risk of urinary symptoms in prostate cancer patients, [PERSON] et al. [109] estimated the risk of fistula formation in patients treated with interstitial brachytherapy for advanced gynecological malignancies, and [PERSON] et al. [110] assessed the heart disease risk in breast cancer patients. Other examples include predictions of survival in patients with non-small-cell lung cancer [119; 105] and recurrence in salivary gland tumor patients undergoing adjuvant chemotherapy [107]. Furthermore, AI can be used to predict treatment failure [114] or response after individualized carbon ion RT [115] or neoadjuvant therapy [116], or to monitor post-RT changes in soft-tissue sarcomas [120]. As additional examples of ML-based applications, [PERSON] et al. [111] identified gene biomarkers guiding breast cancer treatments, while [PERSON] et al. [118] predicted the quality of life of prostate cancer patients after RT treatment. Finally, [PERSON] et al. [121] developed an ML model to select the most critical features impacting on the choice of an optimal brachytherapy applicator.  
The remaining papers (about 40%) developed predictive models using radiomic or dosionic features. The radiomic and dosionic approaches have gained increasing interest in recent years because they allow the extraction of quantitative information (i.e., features) from images and dose distribution information, respectively. The features are extracted from CT, MRI, or PET images collected at baseline or during follow-up and could be included in predictive models for the already mentioned purposes. As examples, [PERSON] et al. [122] predicted overall survival rates in the early stages of non-small-cell lung cancer patients, [PERSON] et al. [112] classified lung cancer stages, [PERSON] et al. [117] stratified risk in prostate cancer patients, [PERSON] et al. detected [113] the degree of differentiation in esophageal squamous cell carcinoma, and [PERSON] et al. [106] evaluated progression in nasopharyngeal carcinoma patients without metastasis using radiomic features extracted from CT images  
Unfortunately, the number of analyzed cases is limited compared to the number of patients treated every day, meaning a harmonization procedure is needed. The accuracy of dose-effect models is strongly related to the volume, velocity, and reliability of the collected data, such as for big data [99]. In addition, to build reliable models in multicentric studies, enough data must be collected, demanding a harmonization procedure to unify data or databases. In this context, [PERSON] et al. [123] and [PERSON] et al. [124] developed methods for automatically re-labeling structure sets names according to AAPM TG-263.  
Another example was proposed by [PERSON] et al. [125] to standardize imaging features before radiomic analysis.  
Moreover, mobile, portable, and wearable devices might boost the collection of harmonized data from the real world to early identify patient toxicity or outcomes [126].