Novel approach to MRI based risk stratification of uterine myometrial lesions

Surgery for uterine mesenchymal tumors is common in gynecology. Preoperative diagnosis of malignant tumors can lead to appropriate management for the lesions. This study aims to externally validate a previous MRI-based expert consensus algorithm and evaluate the potential modification of MR-based scoring system's accuracy in diagnosing uterine mesenchymal tumors (UMT). With institutional ethics committee approval and a waiver of informed consent (CRM-2405-410), a bicentric retrospective observational cohort study was conducted from January 2018 to December 2023. The study included women with a pathological diagnosis of uterine mesenchymal tumor following a pelvic MRI within six months. Clinical and MR criteria were blindly recorded by two radiologists (6- and 3-years' experience in gynaecological MR imaging) who assessed several MR features. Continuous variables were analyzed using a Mann-Whitney test, and categorical variables using Fisher's exact test. Odds ratios (OR) for predicting malignancy were calculated with 95% confidence intervals and p-values. The cohort included 455 women (mean age: 43 years, range: 15-82 years) with mesenchymal tumors: 437 leiomyomas, 2 STUMPs (0.4 %), and 16 malignant UMT (3.5 %). Using initial criteria (enlarged pelvic lymph nodes, T2W signal intensity, DW signal intensity compared to endometrium, and ADC cutoff value of 0.9 × 10-3 mm2/sec), the model accurately classified 421 out of 455 cases (Accuracy: 92,5% (CI 93,1-94,3) and missed with 7 tumors (5 leiomyosarcomas, 2 STUMP). The sensitivity was 61.1 % (CI95% 38.5-83.6) and specificity was 93.8 % (CI95% 91.2-95.8) A modified algorithmic approach added "irregular tumor margins" and menopausal status, modified DW signal compared to bladder, and an elevated ADC cutoff value of 1.23 × 10-3 mm2/sec, improving classification to 446 out of 455 cases (Accuracy: 98 % (CI95% 97.1 %-98.1 %) with only 3 missed tumors (2 STUMP and one leiomyosarcoma). The sensitivity was 83.3 % (CI95% 79-88) and specificity was 98.6 % (CI95% 98-99). The new algorithm significantly improved accuracy (p = 0.001), allowing the development of a 5-category scoring system. Modified MR imaging evaluation algorithms increase true positive diagnosis of malignant UMTs leading to effective differentiation from benign leiomyomas. The new algorithm can allow for appropriate triage of potentially malignant UMTs, alleviating risk associated with morcellation in patients with uterine leiomyosarcoma. Our study demonstrates that combining 5 criteria based on multivariate analysis in a new algorithm (T2W signal, DW signal, ADC cut off value of 1.23 x 10-3 mm2/sec, tumor margins and menopausal status) allows us to distinguish benign from malignant uterine mesenchymal tumors with an accuracy of 98 % (CI95% 97,1%-98,1%), a sensitivity of 83.3 % (CI95% 79-88) and a specificity of 98.6 % (CI95% 98-99). This model allows to build a stratification score that would help in the management of typical and atypical uterine lesions.