Construction and Value Analysis of a Prognostic Assessment Model Based on Radiomics and Genetic Data for Colorectal Cancer.

Abstract

Aims/Background Colorectal cancer (CRC) is one of the major global health problems, with high morbidity and mortality, underscoring the need for new diagnostic and prognostic tools. Therefore, this study aims to evaluate the significance of integrating radiomics with genetic data in CRC prognostic assessment and improve the accuracy of prognosis prediction. Methods This study included computed tomography (CT) images from 225 CRC patients and RNA-seq information from 654 patients, obtained from the TICA database. Key radiomics features and genes were identified through radiomics feature extraction, least absolute shrinkage and selection operator (LASSO) regression analysis, and Kaplan-Meier survival analysis. Furthermore, a CRC prognostic model was constructed using these key genes and radiomics features. Results This study identified 170 key radiomics features. Out of them, five were significantly associated with CRC prognosis. Transcriptome data analysis identified 8 key genes, among which the high expressions of Inhibin Subunit Beta B (INHBB), Potassium Voltage-Gated Channel Subfamily Q Member 2 (KCNQ2), and Ubiquilin Like (UBQLNL) were significantly correlated with poor prognosis. Age, tumor stage, pathological T stage, and pathological N stage were determined as independent prognostic factors. Moreover, immune infiltration analysis demonstrated that the immune score of the low-risk group was higher than that of the high-risk group, with significant differences in some immune cells, and key genes were correlated with immune cells. Additionally, the constructed CRC prognostic model incorporating three genes, INHBB, KCNQ2, and UBQLNL, exhibited high prediction accuracy in the validation set, with area under the curve (AUC) values of 0.80, 0.87, and 0.84 at 1-year, 3-year, and 5-year, respectively, indicating good prediction performance and reliability of the model. Conclusion The multimodal data combining radiomics features and gene expression data can improve the accuracy of CRC prognostic assessment, providing a valuable prognostic prediction tool for clinical practice and helping to guide the selection and optimization of treatment regimens.