Evaluation of Diagnostic Accuracy of Preoperative CT-Based Radiomics in Primary Retroperitoneal Sarcoma.

Background: Obtaining an accurate preoperative diagnosis in retroperitoneal sarcoma (RPS) is challenging, and radiomics-based models may offer a valid option to achieve this outcome. This study evaluated the accuracy of radiomics-based preoperative CT models at predicting tumour histology and grade in patients with primary RPS.

Methods: Data on consecutive patients who underwent surgery for primary retroperitoneal liposarcoma (RLPS) and leiomyosarcoma (RLMS) were analysed. Four different CT radiomics-based models were devised: 1) to distinguish between RLPS and RLMS; 2a) to predict overall tumour grade in both RLPS and RLMS; 2b and 2c) to predict tumour grade in RLPS and RMLS, respectively. The models were evaluated in a 100× random-split cross-validation.

Results: Data were available for 100 patients (64 RLPS, 36 RLMS), with 34 RLPS and 22 RLMS patients having a high-grade tumour on final histology. No significant differences in terms of age (p = 0.46), sex (p = 0.13), tumour location (p = 0.52), tumour diameter (p = 0.16), or tumour volume (p = 0.45) were observed between high- and low-grade tumours. The resulting area under the curve (AUC) at distinguishing between RLPS and RLMS was 0.94. The AUC at differentiating between high- and low-grade tumours for both RLPS and RMLS was 0.74. When tumour grade was analysed separately the corresponding AUC for RLPS and RLMS was 0.87 and 0.61, respectively.

Conclusions: Radiomics-based preoperative CT-models were demonstrated to be accurate at differentiating between RLMS and RLPS, and at predicting preoperative tumour grade in RLPS, whereas they performed poorly in RLMS.