Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer

• Published in: Tomography
• Main Authors: Nicolò Gennaro, Moataz Soliman, Amir A. Borhani, Linda Kelahan, Hatice Savas, Ryan Avery, Kamal Subedi, Tugce A. Trabzonlu, Chase Krumpelman, Vahid Yaghmai, Young Chae, Jochen Lorch, Devalingam Mahalingam, Mary Mulcahy, Al Benson, Ulas Bagci, Yuri S. Velichko
• Format: Article
• Language: English
• Published: MDPI AG 2025-02-01
• Subjects: computer tomography; liver; metastases; radiomics; Delta radiomics; chemotherapy
• Online Access: https://www.mdpi.com/2379-139X/11/3/20

<b>Background/Objectives</b>: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). <b>Materials and Methods</b>: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. <b>Results</b>: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. <b>Conclusions</b>: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer.