Improving preoperative risk stratification in colorectal liver metastases: a multi-institutional evaluation of multimodal prediction models.

Abstract

Reliable prognostic models of death or liver recurrence following resection of colorectal liver metastases are critical to stratify patients for treatment. The study aimed to develop models incorporating clinical and imaging data into multimodal preoperative prediction models of hepatic disease-free survival and overall survival. We conducted a retrospective cohort study with 1,301 consecutive patients from Memorial Sloan Kettering Cancer Center and The University of Texas MD Anderson Cancer Center. Clinical and computed tomography (CT) data were included in radiomic and deep learning models and compared. Our findings suggest that a deep learning model that utilizes the tumor region from CT imaging is the highest performing individual model with C-index of 0.61 for both hepatic disease-free (HDFS) and overall survival. Combining radiomic and clinical models into a multimodal 'clinicoradiomic' model (C-index = 0.63 [0.58-0.69]) outperformed unimodal models (C-index = 0.61 [0.55-0.66]) and the current clinical risk score (C-index = 0.55 [0.49-0.60]) for HDFS. Our study successfully developed preoperative imaging models that integrate clinical data to predict patients at risk of hepatic recurrence, outperforming all currently available models.