Pilot Study on Leiomyosarcoma of the Soft Tissues: Advanced Imaging Features to Predict Response to Neo-adjuvant Therapy
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Abstract
Advanced MR imaging features of soft-tissue leiomyosarcoma (LMS) have not been reported, and no imaging biomarkers exist for response to neoadjuvant therapy (NAT). The exploratory study pilot aims to address these deficiencies and gather initial evidence supporting its utility. Nine patients with LMS and advanced MRI at the pre-operative time point comprised the study population. Patients were dichotomized into responders (those with a treatment effect of ≥90%) and partial/non-responders (those with a treatment effect of <90%) based on the pathology report. Custom software was used to measure lesions, derive volumetric radiomic features on contrast-enhanced susceptibility-weighted images (CE-SWI) and apparent diffusion coefficient (ADC) images, generate time-intensity curves (TICs), and estimate semi-quantitative perfusion variables. Imaging patterns were classified visually by the radiologists in consensus as aggressive vs. non-aggressive on CE-SWI, arterial phase perfusion images (PWI), and TICs. Fisher’s exact test was used to assess the association between these patterns and the response. At baseline, mean ADCmean was 1.28 × 10-3 mm2/s (95% CI: 0.843 - 1.72 × 10-3 mm2/s), and imaging patterns were all aggressive. No baseline features were predictive of response. At the preoperative time point, the non-aggressive CE-SWI pattern of “complete ring” was able to predict a response to NAT (odds ratio: 0.0, p = 0.018). CE-SWI radiomic analysis identified several predictive features. Receiver operating characteristic analysis of classification models based on CE-SWI and radiomic features on CE-SWI and ADC showed an AUC of 1. Our pilot study provides initial evidence on the use of advanced MRI features for characterizing treatment-naïve soft tissue LMS. Additionally, it supports the utility of advanced MRI features in determining treatment response on pre-operative/post-NAT scans, potentially outperforming all traditional size-based metrics.
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Copyright (c) 2025 Behrang Amini, Raul F Valenzuela, Elvis Duran-Sierra, Mathew Antony, Colleen M Costelloe, Elise F Nassif Haddad, Sam LH Lo, Pia V Valenzuela, John E Madewell, ossue V Espinoza, William A Murphy Jr.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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