Performance evaluation of a stacked classifier for predicting treatment response in unresectable colorectal liver metastases

Colorectal cancer is the third most common cancer globally, with a high mortality rate due to metastatic progression, particularly in the liver. Early diagnosis and effective treatment are critical for improving survival rates. Surgical resection remains the cornerstone for curative treatment, but only a small subset of patients are eligible for surgery due to the already advanced stage of the disease at the time of the diagnosis. For patients with initially unresectable colorectal liver metastases (CRLM), neoadjuvant chemotherapy can downstage tumors, potentially making surgical resection feasible. Predicting treatment response is crucial for optimizing treatment strategies, as it can guide personalized approaches and avoid exposing patients to the toxicity of chemotherapy. In this study we explored quantitative image features employing radiomics, which were then analyzed using machine learning algorithms to predict treatment outcome, offering a non-invasive tool to assist clinical decision-making and tailor personalized treatment strategies for CRLM patients. The study involved 355 patients with initially unresectable CRLM. Our findings demonstrate that baseline computed tomography scans contain valuable prognostic information. Radiomic-based models achieved high predictive performance, with the area under the receiver operating characteristic curve values reaching 77% for the a priori prediction of treatment response.