Objective To investigate the predictive value of radiomics predictive model based on computed tomography (CT) for histopathological growth patterns(HGP) in tissues of colo-rectal cancer liver metastases (CRCLM).

Methods The retrospective cohort study was conducted. The clinicopathological data of 100 CRCLM patients who were admitted to Wenzhou Central Hospital from May 2017 to May 2022 were collected. There were 56 males and 44 females, aged (54±13)years. All patients underwent preoperative enhanced abdominal CT and radical resection. A total of 100 CRCLM patients were randomly divided into 70 cases in the training set and 30 cases in the valida-tion set according to the ratio of 7:3 based on random number table. The training set was used to construct predictive model, and validation set was used to validate predictive model. Measurement data with normal distribution were represented as Mean±SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were expressed as M(Q₁,Q₃), and comparison between groups was conducted using the Mann‑Whitney U test. Count data were represented as absolute numbers, and comparison between groups was conducted using the chi-square test. Univariate analysis was performed using statistical methods appropriate to the data type. Multivariate analysis was conducted using the Logistic regression model. The receiver operating characteristic curve was drawn, and the area under curve, accuracy, sensitivity and specificity were calculated.

Results (1) Radiomics featuers extraction and selection. A total of 1 635 radiomics features of 100 CRCLM patients were extracted to select features with intra‑group correlation coefficient >0.8. After dimension reduction of features by using the maximal relevance and minimal redundancy, the Least Absolute Shrinkage and Selection Operator regression analysis, 6 radiomics features (wavelet‑LHL_firstorder_Kurtosis, lbp‑2D_firstorder_Median, original_shape_Spgericty, original_ shape_VolxelVolume, wavelet‑HLH_glrlm_GLNUN, wavelet‑HLL_glcm_Imc2) were finally screened out. (2) Analysis of influencing factors for HGP in tissues of CRCLM patients of the training set. Results of multivariate analysis showed that preoperative carcinoembryonic antigen and CA19‑9 were indepen-dent influencing factors for HGP in tissues of CRCLM patients of the training set (odds ratio=16.83, 3.26, 95% confidence interval as 1.04-258.60, 1.07-19.32, P<0.05). (3) Construction and evaluation of predictive model for HGP. The clinical‑radiomics combined predictive model was constructed based on the results of multivariate analysis and rediomics features using the random forest machine learning method. The AUC of clinical‑radiomics combined predictive model in the training set was 0.98(95% confidence interval as 0.96-1.00), with the accuracy, sensitivity and specificity as 0.91, 0.89 and 0.90. The AUC of clinical‑radiomics combined predictive model in the validation set was 0.84 (95% confidence interval as 0.63-1.00), with the accuracy, sensitivity and specificity as 0.90, 0.93 and 0.88.

Conclusion The clinical‑radiomics combined predictive model based on CT radio-mics and multivariate analysis of clinicopathological data has predictive value for HGP in CRCLM, with good performance.