Table Info

Table 2.

Recent research on AI models for predicting nCRT and chemotherapy response in the treatment of CRC

Topic	Research	Model	Performance	Year	Reference
nCRT	EUS images of 43 LARC patients as predictive biomarkers Images pre-processed by lee, wiener, median, frost, bilateral, and wavelet filters	LR and SVM	AUC: 0.71 and 0.76 Accuracy: 70.0% and 71.5% Sensitivity: 69.8% and 80.2% (respectively)	2022	[51]
	CT images of 215 LARC patients Images evaluated by filtration histogram texture analysis and fractal dimension	LR	Accuracy: 82% Specificity: 89% Sensitivity: 60%	2021	[52]
	pCR prediction in 282 LARC patients (248 training and 34 validation)	ANN	AUC/accuracy/sensitivity: 0.84/0.88/0.94 respectively	2020	[53]
	pCR prediction in 6,555 non-metastatic cancer patients undergoing radical resection	LR	92.4%/88.2%: With/without—pathological complete response (overall survival rate of 3 years)	2019	[54]
	MRI of 98 patients (53/45: training test/validation set respectively) Image preprocessing by EMLMs and LOG filters	SVM, NN, BN, and KNN	Test (AUC and accuracy): 97.8% and 92.8% Validation (AUC and accuracy): 95% and 90%	2019	[55]
	MRI of 55 LARC patients to predict pCR and pNR rates	RF	0.83: Mean of AUC	2019	[56]
Chemotherapy	Irinotecan drug toxicity prediction in 20 metastatic CRC patients (liver function bloody tests and tumor markers)	SVM	Accuracy: 91%/76%/75% for diarrhea/leukopenia/neutropenia respectively	2019	[57]
Chemotherapy	Detection of IC₅₀ of a drug Evaluation of QSAR using NMR Analysis of 18,850 organic compounds	KNN, RF, and SVM	Above 63% accuracy	2018	[58]

AUC: area under curve; NN: neural network; BN: Bayesian network; LARC: locally advanced rectal cancer; EUS: endorectal ultrasound; IC₅₀: half maximal inhibitory concentration; LOG: Laplacian of Gaussian; NMR: nuclear magnetic resonance; CT: computed tomography; MRI: magnetic resonance imaging; pCR: pathologic complete response; EMLMs: ensemble machine learning models; pNR: pathologic non responder

Declarations

Acknowledgments

We are thankful to Department of Biotechnology, Jaypee Institute of Information Technology, Noida for necessary facilities.

Author contributions

KD, PKT, MP, and CKJ: Conceptualization. KD and PKT: Investigation, Writing—original draft. KD, PKT, and MP: Visualization. SK, CKJ, RK, and SV: Writing—review & editing, Supervision. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

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