Diagnostic value of imaging omics models based on CT enhanced intratumoral and peritumoral imaging in adjacent tumor deposition of colorectal cancer
LIU Yan
LUO Jinwen
LIU Yanli
TANG Yaxia
Abstract：Objective To explore the diagnostic value of a radiomics model based on the intratumoral and peritumoral regions in contrast-enhanced CT for peritumoral tumor deposits(TDs)in colorectal cancer(CRC).Methods A retrospective analysis was conducted on contrast-enhanced CT images of 330 CRC patients,confirmed by surgical pathology,from our hospital and the TCIA database between January 2017 and September 2024.Based on postoperative pathology,patients were classified into TDs-positive and TDs-negative groups.Using random sampling,patients were split into a training set(n=231)and a testing set(n=99)in a 7:3 ratio.Regions of interest(ROI)were manually delineated layer by layer on contrast-enhanced venous-phase images to generate volume of interest.The peritumoral ROIs were expanded outward by 2,4 and 6 mm.Radiomic features were extracted from each ROI using pyradiomics,and LASSO was employed for feature selection.XGBoost machine learning algorithm was used to construct separate prediction models for intratumoral,peritumoral,and combined intratumoral-peritumoral features.The diagnostic performance of each model was evaluated using ROC curves,and the DeLong test was used to compare the predictive performance of different models.Results The area under the ROC curve(AUC)for the intratumoral model was 0.937 in the training set and 0.828 in the testing set.Among the peritumoral models,the 4 mm peritumoral region exhibited the best diagnostic performance,achieving an AUC of 0.933 in the training set and 0.830 in the testing set.The combined intratumoral-peritumoral model demonstrated the highest predictive performance,with an AUC of 0.951 in the training set and 0.883 in the testing set.Decision curve analysis indicated that the combined model provided the highest net benefit for predicting TDs.Conclusion The radiomics model integrating intratumoral and peritumoral regions based on contrast-enhanced CT effectively predicts peritumoral TDs in CRC,offering the highest net benefit for TDs prediction.This model can assist clinicians in decision-making and outperforms traditional radiomics models based on either intratumoral or peritumoral features alone.
Keywords：computed tomographyintratumoral tumorradiomicscolorectal cancertumor deposition
Publication Date：2025-04-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 484-491 )