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Sarcoma

Sarcoma Poster Presentations 2

P65: Association between Preoperative Radiomics-based Models Accuracy and Tumor Grading in Patients with Primary Retroperitoneal Liposarcoma

Friday, March 22, 2024
2:58pm – 3:04pm ET
Location: C110
Disclosure(s):
Winan J. van Houdt, MD, PhD, MS: Amgen: Advisor (Ongoing), Research Grant (Ongoing); Asana BioSciences, LLC; Bristol-Myers Squibb; Merck & Co., Inc.; Novartis Pharmaceuticals Corporation; and Replimune Group Inc: Speaker (Terminated, December 21, 2022); Belpharma: Advisor (Terminated, November 15, 2022); Boehringer-Ingelheim: Speaker (Terminated, June 6, 2023); Sanofi/ Regeneron: Speaker (Terminated, October 18, 2023); Sirius medical: Research Grant (Ongoing)
Introduction:

Targeting the solid component of the tumors has determined a significant improvement in differentiating between low- and high- grade retroperitoneal liposarcoma (RLPS). However, grading accuracy on biopsy remains variable. The aim of this study was to evaluate the accuracy of radiomics-based preoperative CT models at predicting tumor grading in patients with primary RLPS.




Methods:

Data on consecutive patients who underwent surgery for primary RPLS were retrospectively analyzed. Low-grade (grade 1) RPLS corresponded to WDLPS and high-grade (grade 2 and 3) RLPS to DDLPS. Three different radiomics-based models were devised: Model 1) based on manually scored imaging features; Model 2) based on CT scan images; and Model 3) based on both manually scored and CT scan imaging features. The performances of the radiomics models were evaluated using the Area Under the Curve (AUC) of the receiver operating characteristic curve, balanced classification accuracy (BCA), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV).



Results: Data were available for N=64 patients with RLPS, of whom n=30 with low-grade RLPS (WDLPS) and n=34 with high-grade RLPS (DDLPS). No significant differences in terms of age (p=0.46), sex (p=0.13), tumor location (p=0.52), tumor size (p=0.16), and tumor volume (p=0.45) were observed between the two groups. The resulting AUC, sensitivity, and specificity at predicting the tumor grading on the final histology was 0.84, 0.83, and 0.72 for Model 1; 0.87, 0.81, and 0.77 for Model 2, and 0.84, 0.81, and 0.74 for Model 3. In addition, BCA, NPV, and PPV were 0.77, 0.80, and 0.79 for Model 1; 0.79, 0.80, and 0.82 for Model 2; and 0.77, 0.79, and 0.80 for Model 3.


Conclusions:

The radiomics-based preoperative CT-models represent an accurate, objective, and non-invasive tool to predict preoperative grading in RLPS. These models may potentially be used as an effective alternative to preoperative biopsy. Further large multicenter studies are warranted to validate these findings.

Learning Objectives:

  • Upon completion, participants will be able to describe the accuracy of radiomics-based models to predict tumor grading in patients with primary retroperitoneal liposarcoma.
  • Upon completion, participants will be able to distinguish between high- and low-grade retroperitoneal liposarcoma using radiomics-based models.
  • Upon completion, participants will be able to reflect on the possibility to develop a radiomics-based system as alternative to the traditional biopsy in patients with primary retroperitoneal liposarcoma.