Leiomyoma or sarcoma? MRI performance in the differential diagnosis of sonographically suspicious uterine masses.

PURPOSE: To assess the diagnostic performance of MRI in distinguishing between leiomyomas and malignant/potentially malignant mesenchymal neoplasms in patients with rapidly enlarging/sonographically suspicious uterine masses. METHODS: IRB-approved retrospective study including 88 patients (51 ± 11 years) who underwent MRI for rapidly enlarging/sonographically suspicious uterine mass at our Institution between January 2016 and December 2021, followed by surgery or >12 months follow-up. Qualitative image analysis was independently performed by 2 radiologists and included lesion's margins (sharp/irregular), architecture (homogeneous/inhomogeneous), presence of endometrial infiltration (yes/no), necrotic areas (yes/no), hemorrhagic areas (yes/no), predominant signal intensity on T1-WI, T2-WI, CE T1-WI, DWI, and ADC map. The same radiologists performed quantitative image analysis in consensus, which included lesion's maximum diameter, lesion/myometrium signal intensity ratio on T2-WI and CE T1-weighted images, lesion/endometrium signal intensity ratio on DWI and ADC map and necrosis percentage. Lesions were classified as benign or malignant. Imaging findings were compared with pathology and/or follow-up. RESULTS: After surgery (52/88 patients) or follow-up (36/88 patients, 33 ± 20 months), 83/88 (94.3%) lesions were classified as benign and 5/88 (5.7%) as malignant/potentially malignant. Presence of necrotic areas, high necrosis percentage, hyperintensity on DWI and high lesion/endometrium DWI signal intensity ratio were significantly associated with malignant/potentially malignant lesions (p = 0.027, 0.002, 0.008 and 0.015, respectively). The two readers identified malignant/potentially malignant lesions with 95.5% accuracy, 80.0% sensitivity, 96.4% specificity, 57.1 % PPV, 93.3% NPV. CONCLUSION: MRI has high accuracy in identifying malignant/potentially malignant myometrial masses. In everyday practice, however, MRI positive predictive value is relatively low given the low pre-test malignancy probability.

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset.

PURPOSE: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. METHODS: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. RESULTS: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). CONCLUSIONS: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.

Prediction of histological grade of endometrial cancer by means of MRI.

OBJECTIVES: To evaluate the ability of MRI in predicting histological grade of endometrial cancer (EC). METHODS: IRB-approved retrospective study; requirement for informed consent was waived. 90 patients with histologically proven EC who underwent preoperative MRI and surgery at our Institution between Sept2011 and Nov2016 were included. Myometrial invasion (50%) was assessed. Neoplasm and uterus volumes were estimated according to the ellipsoid formula; neoplasm/uterus volume ratio (N/U) was calculated. ADC maps were generated and histogram analysis was performed using commercially available software. MRI parameters were compared with the definitive histological grade (G1 = 28 patients, G2 = 29, G3 = 33) using ANOVA and Tukey-Kramer tests. RESULTS: Deep myometrial invasion was significantly more frequent in G2-G3 lesions than in G1 ones (p < 0,005). N/U ratio was significantly higher for high-grade neoplasms (mean 0,08 for G1, 0,16 for G2 and 0,21 in G3; P = 0,002 for G1 vs. G2-G3); a cut off value of 0,13 enabled to distinguish G1 from G2-G3 lesions with 50% sensibility and 89% specificity. ADC values didn't show any statistically significant correlation with tumour grade. CONCLUSIONS: N/U ratio >0.13 and deep myometrial invasion are significantly correlated with high grade EC, whereas ADC values are not useful for predicting EC grade.

MRI for local staging of endometrial carcinoma: Is endovenous contrast medium administration still needed?

PURPOSE: To compare the diagnostic performance of T2-weighted images (T2-WI)+contrast-enhanced T1-weighted images (CE T1-WI) with the one of T2-WI+diffusion-weighted images (DWI) in the assessment of myometrial and cervical stromal infiltration by endometrial carcinoma (EC). MATERIALS AND METHODS: Institutional review board approved our retrospective study; requirement for informed consent was waived. 56 patients with histologically proven EC who underwent preoperative MRI and surgery at our Institution over a 34 months period were included. Two radiologists independently evaluated T2-WI+CE T1-WI and T2-WI+DWI of each patient. Confidence in imaging evaluation (0-3), depth of myometrial invasion (0.05) whereas both imaging sequences combinations showed the same diagnostic performance in recognizing cervical stromal infiltration (accuracy, sensitivity and specificity of 0.95, 0.98 and 0.80, p>0.05). CONCLUSION: T2-WI+DWI can reliably replace the "classical" combination T2-WI+CE T1-WI for local staging of endometrial carcinoma.