Radiomics in colorectal cancer patients

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.