Dimensional assessment on baseline MRI of soft-tissue sarcomas: longest diameter, sum and product of diameters, and volume-which is the best measurement method to predict patients' outcomes?

Purpose: The longest diameter (LD) is a strong prognostic factor for patients with soft-tissue sarcoma (STS). Other dimensional assessments, such as the sum of diameters (SoD), product of diameters (PoD), and volume (3D-COG - proposed by the Children Oncology Group), can be rapidly performed; however, their prognostic values have never been compared to LD. Our goal was to investigate their performance in improving patients' prognostication for STS of the lower limbs.

Methods: All consecutive adults managed with curative intent at our sarcoma reference center for a newly diagnosed STS of the lower limbs between 2000 and 2017, with pre-treatment MRI, were included in this retrospective study. Multivariable Cox regression models were trained to predict metastasis-free survival (MFS) in a Training cohort of 66.7% patients based on LD, PoD, SoD, or 3D-COG (and systematically including age, histologic grade, histotype, radiotherapy, chemotherapy, and surgical margins as covariables). The models were then compared on a validation cohort of 33.3% patients using concordance indices (c-index). The same approach was applied for overall survival (OS) and local relapse-free survival (LFS). Measurement reproducibility among three readers was evaluated with an intraclass correlation coefficient (ICC).

Results: 382 patients were included in the survival modeling (72/253 [28.5%] metastatic relapses in Training and 36/129 [27.9%] metastatic relapses in Validation). Higher dimensions were associated with lower MFS (multivariable hazard ratio [HR] = 2.44 and P = 0.0018 for LD; HR = 1.88 and P = 0.0009 for PoD, HR = 1.52 and P = 0.0041 for SoD; and HR = 1.08 and P = 0.0195 for 3D-COG). Higher c-indices were obtained with PoD model in Training (c-index = 0.772) and Validation (c-index = 0.688), but they were not significantly higher than those obtained with LD model. None of the measurements was associated with LFS or OS. All measurements demonstrated excellent ICC (> 0.95).

Conclusion: Regarding its simplicity and good performance, LD appeared as the best metric to incorporate in prognostic models and nomograms for MFS.