Integrating intratumoral and peritumoral radiomics with clinical risk factors for prognostic prediction in pancreatic ductal adenocarcinoma patients undergoing combined chemotherapy and HIFU ablation

Abstract

Objective A radiomics nomogram will be created utilizing MRI data from intratumoral and peritumoral areas to forecast survival outcomes in patients who have had treatment for pancreatic ductal adenocarcinoma (PDAC). Methods A total of 87 individuals diagnosed with PDAC were included in the study, with 60 patients in the training cohort and 27 patients in the validation cohort. A grand total of 2395 radiomics characteristics were extracted from the tumor region and the peritumoral region. The least absolute shrinkage and selection operator (LASSO) method was used to select features and create a radiomics score, also known as the Rad-score. A multivariate regression analysis was then conducted to build the radiomics nomogram. The evaluation of the nomogram included discrimination, calibration, and clinical utility assessments. Results Based on the conclusions derived from the multivariate Cox model, Rad-Score, jaundice, and tumor size were identified as independent risk factors for overall survival (OS). The inclusion of the Rad-score in the radiomics nomogram led to improved accuracy in predicting survival compared to the clinical model. Patients were categorized into high-risk and low-risk groups based on their Rad-Score. Kaplan-Meier analysis revealed a statistically significant difference between the two groups (p < 0.05). Furthermore, the radiomics nomogram demonstrated excellent ability to differentiate, calibrate, and provide clinical utility in both the training and validation groups. Conclusions The MRI-based intratumoral and peritumoral radiomics nomogram, integrating the Rad-score and clinical data, provided better prognostic prediction for PDAC patients after HIFU treatment, which may hold great potential for guiding personalized care for these patients.