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Contrast-enhanced CT-based multimodal modeling for overall survival prediction after resection of retroperitoneal
Peidang Fan1, Jiulong Zhang2, Wenyu Luo1
1Zhongshan Hospital, Shanghai, China.
Objective:
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.
Materials And Methods:
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.
Results:
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.
Conclusion:
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.