Radiomic features of primary retroperitoneal sarcomas: a prognostic study

Abstract

Background

Risk-stratification of patients with retroperitoneal sarcomas (RPS) relies on validated nomograms, such as Sarculator. This retrospective study investigated whether radiomic features extracted from computed tomography (CT) imaging could i) enhance the performance of Sarculator and ii) identify G3 dedifferentiated liposarcoma (DDLPS) or leiomyosarcoma (LMS), which are currently consider in a randomized clinical trial testing neoadjuvant chemotherapy.

Methods

Patients with primary localized RPS treated with curative-intent surgery (2011–2015) and available pre-operative CT imaging were included. Regions of interest (ROIs) were manually annotated on both unenhanced and portal venous phase acquisitions. Top performing radiomic features were selected with outcome-specific random forest models, through generation of replicative experiments (contexts) where patients were split into training and testing sets. Endpoints were overall and disease-free survival (OS, DFS).

Prognostic models for DFS and OS included the top five selected radiomic features and the Sarculator nomogram score.

Models accuracy was assessed with Harrell’s Concordance (C-)index.

Results

The study included 112 patients, with a median follow-up of 77 months (IQR 65–92 months).

Sarculator alone achieved a C-index of 0.622 and 0.686 for DFS and OS, respectively. Radiomic features only marginally enhanced the prediction accuracy of Sarculator for OS (C-index=0.726, C-index gain: 0.04) or DFS (C-index=0.639, C-index gain: 0.017). Finally, radiomic features identified patients with G3 DDLPS or LMS with an accuracy of 0.806.

Conclusion

Radiomic features marginally improved the performance of Sarculator in RPS.

However, they accurately identified G3 DDLPS or LMS at diagnosis, potentially improving patients selection for neoadjuvant treatments.