Artificial intelligence and machine learning in colorectal cancer

Table 1 Overview of screening studies

Ref.	Objective	Results
Wang et al[19], 2019	Effect of computer aided detection deep learning models on polyps and adenoma detection rates	Increase in adenoma detection rates [29.1% vs 20.3%, P < 0.001] and mean number of identified adenomas per patient [0.53 vs 0.31, P <0.001]; More hyperplastic adenomas (114 vs 52, P < 0.001) and diminutive polyps (185 vs 102, P < 0.001) identified
Nazarian et al[20], 2021	Detection rates of polyp and adenoma with AI vs without AI	Increase in both polyp detection rates (odds ratio [OR] 1.75, 95%CI 1.56-1.96; P < 0.001) as well as adenoma detection rates (OR 1.53, 95%CI 1.32-1.77; P < 0.001)
Johnson et al[23], 2008; Pickhardt et al[24], 2003	Degree to which CTC is effective in detecting asymptomatic colorectal lesions	Reported identification of 90% of patients with asymptomatic adenomas or cancers (≥ 10 mm in diameter) using CT colonography
Grosu et al[25], 2021	Development of machine learning method differentiating between benign and precancerous lesions in average risk asymptomatic patients using CTC	Sensitivity of 82%, specificity of 85% and AUC of 0.91
Song et al[26], 2015	Development of virtual pathological model to assess the suitability of using image high-order differentiations to distinguish colorectal lesions	Improvement of ROC curve (AUC) from 0.74 to 0.85
Blanes-Vidal et al[30], 2019	Algorithms developed to match CE and colonoscopy-identified polyps based on their estimated size, morphology and location as well as utilizing deep convolutional neural networks for automatic colorectal polyp detection	Localization resulted in high sensitivity (97.1%), specificity (93.3%), and accuracy (96.4%) for identifying polyps when compared to the manual process of polyp detection
Kinar et al[35], 2017	AI-assisted prediction model (MeScore®, Calgary, Alberta, Canada) was designed to identify people at high risk for CRC	Revealed a 2.1-fold increase in cancer detection rates when the model is used in combination with FOBT
Gupta et al[36], 2019	Using CellMax (CMx®) platform to detect and isolate circulating tumor cells in peripheral blood samples	A sensitivity and specificity of 80%

AI: Artificial intelligence; AUC: Area under the curve; CTC: Computed tomographic colonography; CT: Computed tomography; CE: Capsule endoscopy; ROC: Receiver operating characteristic.

Table 2 Overview of diagnosis studies

Ref.	Objective	Results
Min et al[40], 2019	System designed to predict the histology of colorectal polyps by analyzing linked color imaging	83.3% sensitivity, 70.1% specificity, 82.6% PPV, 71.2% NPV and an accuracy of 78.4% when compared to expert endoscopists
Gross et al[45], 2011	Development of computer-assisted model for polyp classification by analyzing 9 vessel features, from patients who underwent magnifying endoscopy with NBI	Higher sensitivity (95% vs 86%), specificity (90.3% vs 87.8%) and accuracy (93.1% vs 86.8%) when compared to novice endoscopists but comparable to those of expert endoscopists (sensitivity, specificity, and accuracy of 93.4%, 91.8% and 92.7%, respectively)
Chen et al[46], 2018	Designed a deep learning model to classify diminutive colorectal polyps using magnifying NBI images with 284 diminutive colorectal polyps extracted	Able to distinguish between neoplastic and hyperplastic lesions in a shorter period compared to expert endoscopists (0.45 vs 1.54 seconds) and had a sensitivity, specificity, accuracy, PPV, and NPV of 96.3%, 78.1%, 90.1%, 89.6% and 91.5% respectively
Mori et al[48], 2015	Computer-aided algorithm designed to histologically differentiate colorectal lesions in vivo using endocytoscopy	92% sensitivity and 89.2% accuracy in establishing a histological diagnosis.
Takeda et al[51], 2017	Model investigated the role of a computer-aided endocytoscopy system on the diagnosis of invasive colorectal carcinoma	89.4% sensitivity, 98.9% specificity, 98.8% PPV, 90.1% NPV and 94.1% accuracy
Takemura et al[53], 2010	Software model to automatically quantify and classify pit patterns. Used texture and quantitative analysis to classify pit patterns	Type I and II pit patterns were in complete agreement with the endoscopic diagnosis on discriminant analysis. Type III was diagnosed in 29 of 30 cases (96.7%) and type IV was diagnosed in one case. Twenty-nine of 30 cases (96.7%) were diagnosed as type IV pit pattern. The overall accuracy of the computerized recognition system was 132 of 134 (98.5%)
André et al[55], 2012	Automated polyp characterization system to distinguish between benign and malignant lesions using the k-nearest neighbor classification	Accuracy of 89.6%
Ştefănescu et al[56], 2016	A neural network analysis algorithm differentiating advanced colorectal adenocarcinomas from the normal mucosa	Accuracy of 84.5%

PPV: Positive predictive value; NPV: Negative predictive value.

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.