Radiomics in colorectal cancer patients

Table 2 Summary of the most important published papers regarding the usefulness of radiomics in colorectal cancer patients using magnetic resonance imaging

Ref.	Imaging	Main aim	Patients (n)	Main findings
Horvat et al[52], 2022	MRI	Response to chemotherapy	114	Combined radiological-radiomics model increased agreement (κ = 0.82 vs κ = 0.25)
Dinapoli et al[53], 2018	MRI	Pathological complete response	221	Significant covariates, skewness, and entropy can predict pathological complete response, with AUCs = 0.730 and 0.750 for internal and external cohorts
Shahzadi et al[50], 2022	MRI	Response to chemotherapy	190	Radiomics combined with the T stage better predict response
Liu et al[23], 2021	MRI	Prediction of nodes metastases	186	Clinical-radiomics model improves performance: AUC = 0.827
Chen et al[72], 2022	MRI	Tumor differentiation and nodes metastases	37 (487 nodes)	Radiomics features of the primary tumor can predict tumor differentiation: AUC = 0.798
Liu et al[73], 2017	MRI	Tumor differentiation	68	Skewness and entropy are lower in pT1-2 in comparison with pT3-4 (P < 0.05)
Yang et al[74], 2019	MRI	Prediction of T and N stage	88	Skewness, kurtosis, and energy are higher in metastatic nodes in comparison with non-metastatic ones (P < 0.001)
Ma et al[75], 2019	MRI	Prediction of nodes metastases and N staging	152	SVM has higher diagnostic values for T and N stages (AUC = 0.862) in comparison with MLP and RF
Zhu et al[76], 2019	MRI	Prediction of nodes metastases	215	Radiomic model AUC = 0.818
Zhou et al[77], 2020	MRI	Prediction of nodes metastases	391	The combined model predicts nodes metastases: NPV = 93.7%, AUC = 0.818
Shu et al[34], 2019	MRI	Prediction of synchronous liver metastases	194	The Radiomics model combined clinical risk factors and LASSO features and showed a good predictive performance: AUC = 0.921
Liu et al[107], 2020	MRI	Prediction of synchronous liver metastases	127	A radiomic nomogram presents an accuracy of 81.6% in predicting liver metastases (AUC = 0.918)
Granata et al[115], 2022	MRI	Prediction of overall survival	90	Second-order features can predict infiltrative tumor growth, tumor budding, and mucinous type; a second-order feature can predict the risk of recurrence with an accuracy of 90%
Jalil et al[119], 2017	MRI	Prediction of prognosis after chemotherapy	56	MPP can predict overall survival (HR = 6.9) and disease-free survival (HR = 3.36); texture analysis can predict relapse-free survival on pre- and post-treatment analyses

AUC: Area under the curve; SVM: Support vector machine; MLP: Multilayer perceptron; RF: Random forest; NPV: Negative predictive value; HR: Hazard ratio; MPP: Mean positive pixel; MRI: Magnetic resonance imaging.