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.