Artificial intelligence and machine learning in colorectal cancer

Table 3 Overview of treatment, toxicity, and prognosis studies

Ref.	Objective	Results
Huang et al[69], 2020	Artificial neural network K-nearest neighbors, support vector machine, naïve Bayesian classifier, mixed logistic regression models were used to predict response	Accuracy of 0.88, AUC of 0.86 and sensitivity of 0.94
Ferrari et al[70], 2019	AI models to assess response to therapy in locally advanced rectal cancer	Able to identify patients who will have complete response at the end of the treatment and those who will not respond to therapy at an early stage of the treatment with an AUC of 0.83
Shayesteh et al[71], 2019	MRI based ensemble learning methods to predict the response to nCRT	AUC of 95% and accuracy of 90%
Ferrari et al[71], 2019	Algorithms to identify pathological CR and NR patients after neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer	AUC of 0.86 and 0.83 for pathological CRs and NRs
Oyaga-Iriarte et al[73], 2019	Algorithms in metastatic CRC patients to predict Irinotecan toxicity	Accuracy of 76%, 75%, and 91% for predicting leukopenia, neutropenia, and diarrhea respectively
Sailer et al[81], 2015	Compared ten data mining algorithms to predict the 5-yr survival based on seven attributes	Accuracy of 67.7% compared to clinical judgment of 59%

AI: Artificial intelligence; AUC: Area under the curve; CR: Complete responders; MRI: Magnetic resonance imaging; nCRT: Neoadjuvant chemoradiotherapy; NR: Non-responders; CRs: Complete responders.