Radiomics in colorectal cancer patients

Table 1 Summary of the most important published papers regarding the usefulness of radiomics in colorectal cancer patients using computed tomography imaging

Ref.	Imaging	Main aim	Patients (n)	Main findings
Li et al[35], 2020	CT	Prediction of nodes metastases	766	Overall diagnostic values: Sensitivity = 60.3%; specificity = 84.3%; PPV = 75.2%; NPV = 72.9%; AUC = 0.750
Shi et al[16], 2020	CT	Detect RAS and BRAF phenotypes	159	Combined score (semantic features and radiomics) AUC = 0.950; validation cohort AUC = 0.790
Giannini et al[41], 2020	CT	Predict response to treatment	38 (141 lesions)	Per-lesion diagnostic values: Sensitivity = 89%; specificity = 85%; PPV = 78%; NPV = 93%
Dercle et al[47], 2020	CT	Tumor response to anti-EGFR therapy	667	Sensitivity to therapy: AUCs 0.800 and 0.720 for FOLFIRI and FOLFIRI + cetuximab
Dohan et al[48], 2020	CT	Overall survival	491	SPECTRA score > 0.02 has a lower OS; SPECTRA Score at 2 mo has the same prognostic values as RECIST at 6 mo
Giannini et al[41], 2020	CT	Predict response to treatment	57 (242 lesions)	Per-lesion diagnostic values: Sensitivity = 99%; specificity = 94%; PPV = 95%; NPV = 99%; the radiomic approach can predict R- wrongly classified by RECIST as R+
Taghavi et al[103], 2021	CT	Prediction of synchronous liver metastases	91	The radiomics model outperformed the clinical model: AUC = 0.93 vs 0.64
Rao et al[108], 2014	CT	Prediction of synchronous liver metastases	29	The mean entropy of the liver is significantly higher in metastatic patients (P = 0.02); Liver entropy can help the differential between metastatic and non-metastatic patients (AUC = 0.73-0.78)
Li et al[109], 2022	CT	Prediction of synchronous liver metastases	323	A combined clinical-radiomics model has a good AUC (= 0.79) in detecting liver metastases
Ng et al[111], 2013	CT	Prediction of overall survival	55	Entropy, uniformity, kurtosis, skewness, and standard deviation of the pixel distribution histogram can predict survival; each parameter can be considered an independent predictor of the overall survival state
Mühlberg et al[112], 2021	CT	Prediction of overall survival	103	Tumor burden score can discriminate patients with at least 1-year survival (AUC = 0.70); a machine-learning model better predict survival (AUC = 0.73)
Ravanelli et al[116], 2019	CT	Prediction of response and prognosis after chemotherapy	43	Uniformity is lower in responders (P < 0.001); uniformity is independently correlated with radiological response (OR = 20.00), overall survival (RR = 6.94) and progression-free survival (RR = 5.05)

PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the curve; OS: Overall survival; SPECTRA: Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced computed tomography TextuRe Analysis; CT: Computed tomography.

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.

Table 3 Summary of the most important published papers regarding the usefulness of radiomics in colorectal cancer patients using positron emission tomography/computed tomography imaging

Ref.	Imaging	Main aim	Patients (n)	Main findings
Lovinfosse et al[80], 2018	PET/CT	Progression-free and overall survival	86	SUVmean, dissimilarity, and contrast from the neighborhood intensity-difference matrix are independently associated with overall survival
Hotta et al[81], 2021	PET/CT	Progression-free and overall survival	94	MTV, TLG, and GLCM entropy are associated with overall survival; SUVmax, MTV, TLG, and GLCM entropy are associated with progression-free survival
Bundschuh et al[83], 2014	PET/CT	Response after neoadjuvant chemotherapy	27	COV can assess histopathologic response during (sensitivity 68%, specificity 88%) and after (sensitivity 79%, specificity 88%) therapy
Bang et al[84], 2016	PET/CT	Response after neoadjuvant chemotherapy	74	MV is associated with 3-yr disease-free survival; Kurtosis and kurtosis gradient are associated with 3-yr disease-free survival
Giannini et al[85], 2019	PET/CT	Response after neoadjuvant chemotherapy	52	Second-order texture features (five from PET and one from MRI) can help distinguish responder and non-responder patients: Sensitivity = 86%; specificity = 83%; AUC = 0.860
Yuan et al[89], 2021	PET/CT	Response after neoadjuvant chemotherapy	66	A radiomics model can predict TRG 0 vs TRG 1-3: Sensitivity = 77.8%, specificity = 89.7%, AUC = 0.858
Schurink et al[86], 2021	PET/CT	Response after neoadjuvant chemotherapy	61	Combined baseline and global tumor features better predict response compared to baseline and local texture (AUC = 0.83 vs 0.79)
Shen et al[87], 2020	PET/CT	Predict pathological complete response	169	RF can predict complete response: Sensitivity = 81.8%; specificity = 97.3%; PPV = 81.8%; NPV = 97.3%; accuracy = 95.3%
He et al[90], 2021	PET/CT	Prediction of nodes metastases	199	Logist regression and XGBoost can accurately predict nodes metastases with AUC = 0.866 and 0.903, respectively
Ma et al[91], 2022	PET/CT	Prediction of perineural invasion and outcome	131	12 radiomics signatures are associated with peri-neural invasion; a radiomic score can differentiate between perineural positive and negative lesions: AUC = 0.900
Li et al[92], 2021	PET/CT	Prediction of microsatellite instability	173	2 radiomics features can predict microsatellite instability: Sensitivity = 83.3%; specificity = 76.3%; accuracy = 76.8%
Lovinfosse et al[93], 2016	PET/CT	Prediction of RAS status	151	SUVmax, SUV mean, skewness, SUV standard deviation, and SUV coefficient of variation are associated with RAF mutation (all P < 0.001)
Chen et al[94], 2019	PET/CT	Prediction of genetic mutations	74	MTV and SUV max are increased in mutated KRAS tumors (all P < 0.001); short-run low gray-level emphasis is associated with p53 mutations (P = 0.001); gray-level zone emphasis is associated with APC mutations (P = 0.006)

PET/CT: Positron emission tomography/computed tomography; PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the curve; RF: Random forest; MTV: Metabolic tumor volume; TLG: Total lesion glycolysis; GLCM: Gray-level co-occurrence matrix; COV: Coefficient of variation; MV: Metabolic volume; OR: Odd ratio; MPP: Mean positive pixel.