Quantification of Intratumoral Heterogeneity Based on Habitat Analysis for Preoperative Assessment of Lymphovascular Invasion in Colorectal Cancer.

Abstract

Rationale and objectives: Preoperative knowledge of the status of lymphovascular invasion (LVI) status in colorectal cancer (CRC) patients can provide valuable information for choosing appropriate treatment strategies. This study aimed to explore the value of heterogeneity features derived from the habitat analysis of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images in predicting LVI.

Materials and methods: Pretreatment ¹⁸F-FDG PET/computed tomography (CT) images from 177 patients diagnosed with CRC were retrospectively obtained (training cohort, n=106; validation cohort, n=71). Conventional radiomics features and habitat-derived tumor heterogeneity features were extracted from ¹⁸F-FDG PET scans. The output probabilities of the imaging-based random forest model were used to generate a radiomics score (Radscore) and intratumoral heterogeneity score (ITHscore). Multivariate logistic regression analysis was used to determine the independent risk factors for LVI. On this basis, four LVI status classification models were developed using (a) clinical variables (Clinical model), (b) tumor heterogeneity features (ITHscore model), (c) radiomics features (Radscore model), and (d) clinical variables, tumor heterogeneity features, and radiomics features (Combined model). The area under the curve (AUC) and decision curve analysis were used to evaluate model performance.

Results: Among all of the variables, the PET/CT-reported lymph node status, ITHscore, and Radscore were retained as predictors related to the risk of LVI in CRC patients (P<0.05). The predictive effect of the ITHscore model (AUC: 0.712) was better than that of the Radscore model (AUC: 0.650) and Clinical model (AUC: 0.652) in the validation cohort. The Combined model achieved better classification effects and clinical usefulness, and the AUCs of the training and validation cohorts were 0.857 and 0.798, respectively. A nomogram of the Combined model was established, and the calibration plot was well fitted (P>0.05). In addition, the results of Spearman's rank correlation tests showed that there was no significant correlation between the ITHscore and Radscore (R=0.044, P=0.655 in the training cohort; R=0.067, P=0.580 in the validation cohort).

Conclusion: Our results showed that the ITHscore is a novel and stable quantitative indicator of LVI and is helpful for effectively facilitating the risk stratification of LVI in CRC patients after integrating clinical variables and radiomics features.