Contrast-enhanced CT-based multimodal modeling for overall survival prediction after resection of retroperitoneal liposarcoma: a bicentric retrospective study.
Authors
Affiliations (5)
Affiliations (5)
- Zhongshan Hospital, Shanghai, China.
- Shanghai Public Health Clinical Center, Shanghai, China.
- Zhongshan Hospital, Shanghai, China. [email protected].
- Zhongshan Hospital, Shanghai, China. [email protected].
- Zhongshan Hospital, Shanghai, China. [email protected].
Abstract
Postoperative risk estimation in retroperitoneal liposarcoma (RPLS) remains challenging because conventional clinicopathologic models capture only part of its biological heterogeneity. We aimed to develop and externally evaluate a postoperative prognostic model integrating preoperative CT features with postoperative clinicopathologic information to estimate overall survival (OS) after RPLS resection. This bicentric retrospective study included 367 patients who underwent complete RPLS resection between 2009 and 2021. Institution 1 provided the training cohort (n = 128) and internal validation cohort (n = 55); Institution 2 provided the external test cohort (n = 184). A radiomics signature was constructed from three-dimensional tumor features. A deep-learning signature was derived from the largest axial tumor section using an ImageNet-pretrained ResNet-101 trained with Cox partial-likelihood loss. Clinical variables underwent univariable and multivariable Cox regression analysis. The retained clinicopathologic variables and imaging signatures were integrated into a combined model termed RDCS. During follow-up, 71, 30, and 107 patients died in the training, internal validation, and external test cohorts, respectively. The corresponding RDCS C-indices were 0.775, 0.704, and 0.725, each higher than those of the individual radiomics, deep-learning, and clinical models. Calibration curves showed agreement between predicted and observed survival. Decision-curve analysis suggested potential clinical utility within the evaluated thresholds. In a separate exploratory comparison in the external primary-tumor subset (n = 82), C-indices were 0.746 for RDCS and 0.590 for the official Sarculator application. RDCS estimates OS after complete RPLS resection using preoperative CT-derived imaging signatures and postoperative clinicopathologic information. RDCS may complement established postoperative prognostic tools, but its application in surveillance and clinical management requires prospective validation and clinical impact assessment.