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PURPOSE: To investigate the characteristics of suspicious MRI-only visible lesions and to explore the validity of subcategorizing these lesions into the following two groups: lesions that would require immediate biopsy (4Bi) and lesions for which careful clinical follow-up could be recommended (4Fo). METHODS: A retrospective review of 108 MRI-only visible lesions in 106 patients who were diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 between June 2018 and June 2022 at our institution was performed by two radiologists. The breast MR images were evaluated according to BI-RADS and additional MRI descriptors (linear ductal, branching, and apparent diffusion coefficient values). The lesions were categorized by previously reported classification systems, and the positive predictive values (PPVs) for the different categories were determined and compared. Subsequently, a new classification system was developed in this study. RESULTS: The total malignancy rate was 31% (34/108). No significant differences between benign and malignant lesions were identified for focus and mass lesions. For non-mass lesions, linear ductal and heterogeneous internal enhancement suggested a benign lesion (P = 0.0013 and P = 0.023, respectively), and branching internal enhancement suggested malignancy (P = 0.0066). Segmental distribution suggested malignancy (P = 0.0097). However, the PPV of segmental distribution with heterogeneous enhancement was significantly lower than that of category 4 segmental lesions with other enhancement patterns (11% vs. 59%; P = 0.0198).As a new classification, the distribution of focal, linear, and segmental was given a score of 0, 1, or 2, and the internal enhancement of heterogeneous, linear-ductal, clumped, branching, and clustered-ring enhancement was given a score of 0, 1, 2, 3, and 4, respectively. When categorized using a scoring system, a statistically significant difference in PPV was observed between 4Fo (n = 27) and 4Bi (n = 33) (7% vs. 61%, P = 0.000029). CONCLUSION: The new classification system was found to be highly capable of subcategorizing BI-RADS category 4 MRI-only visible non-mass lesions into 4Fo and 4Bi.
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A 60-year-old woman with a history of neurofibromatosis type 1, who was admitted with pulmonary hypertension, developed buttock pain and anemia, and contrast-enhanced computed tomography showed a large subcutaneous hematoma with minimal active extravasation. Angiography of the bilateral internal iliac arteries revealed diffuse, irregular blood vessels without extravasation. As the exact bleeding site could not be identified, the patient was managed conservatively. However, the patient's symptoms and anemia worsened the following day. Repeat angiography revealed two pseudoaneurysms in the right inferior gluteal artery, which were embolized using n-butyl-2-cyanoacrylate. Nonetheless, the patient's anemia further worsened the following day. Repeat contrast-enhanced CT revealed another site of extravasation in the enlarging hematoma, but no extravasation was observed on the subsequent angiography. Owing to the worsening anemia and enlarging hematoma, proximal embolization of the irregular bilateral inferior gluteal arteries was performed using gelatin sponge particles. The patient's anemia and symptoms improved. Vasculopathy associated with neurofibromatosis type 1 is rare, with an incidence of approximately 3%. In patients with neurofibromatosis type 1, the blood vessels become fragile because of tunica media thinning and elastic-lamina rupture. Histopathologically, neurofibromatosis type 1-associated vasculopathy is characterized by a mixture of normal and abnormal vessels. Abnormally fragile blood vessels may repeatedly rupture followed by physiological hemostasis, which may explain the diagnostic and therapeutic challenges during angiography in this case. In patients with neurofibromatosis type 1 with acute bleeding, irregular vessels without active extravasation on angiography may be indicated for embolization.
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PURPOSE: To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). MATERIALS AND METHODS: Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. RESULTS: Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. CONCLUSION: Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.