MR diagnosis of SCC arising within ovarian cystic teratomas: analysis of mural nodule characteristics.

PURPOSE: This study aims to evaluate and identify magnetic resonance (MR) findings of mural nodules to detect squamous cell carcinoma arising from ovarian mature cystic teratoma (SCC-MCT). METHODS: This retrospective study examined 135 patients (SCC-MCTs, n = 12; and benign MCTs, n = 123) with confirmed diagnoses across five different institutions between January 2010 and June 2022. Preoperative MR images for each patient were independently assessed by two experienced radiologists and analyzed following previously reported findings (PRFs): age, tumor size, presence of mural nodules, size of mural nodule, and the angle between mural nodule and cyst wall (acute or obtuse). Furthermore, this study evaluated four mural nodule features-diffusion restriction, fat intensity, Palm tree appearance, and calcification-and the presence of transmural extension. RESULTS: There were significant differences between the SCC-MCT and benign MCT groups in terms of all PRFs and all mural nodule findings (p < 0.01). Among the PRFs, "tumor size" demonstrated the highest diagnostic performance, with a sensitivity of 83.3% and a specificity of 88.6%. A combination of the aforementioned four mural nodule findings showed a sensitivity and specificity of 83.3% and 97.6%, respectively, for the diagnosis of SCC-MCT. Regarding diagnosis based on a combination of four mural nodule findings, the specificity was significantly higher than the diagnosis based on tumor size (p = 0.021). Based on these mural nodule findings, three SCC-MCT patients without transmural invasion could be diagnosed. CONCLUSION: Mural nodule MR findings had a higher diagnostic performance than PRFs for SCC-MCT and can potentially allow early detection of SCC-MCTs.

Classification tree analysis to evaluate the most useful magnetic resonance image type in the differentiation between early and progressed hepatocellular carcinoma.

AIM: Using classification tree analysis, we evaluated the most useful magnetic resonance (MR) image type in the differentiation between early and progressed hepatocellular carcinoma (eHCC and pHCC). METHODS: We included pathologically proven 214 HCCs (28 eHCCs and 186 pHCCs) in 144 patients. The signal intensity of HCCs was assessed on in-phase (T1in) and opposed-phase T1-weighted images (T1op), ultrafast T2-weighted images (ufT2WI), fat-saturated T2-weighted images (fsT2WI), diffusion-weighted images (DWI), contrast enhanced T1-weighted images in the arterial phase (AP), portal venous phase (PVP), and the hepatobiliary phase. Fat content and washout were also evaluated. Fisher's exact test was performed to evaluate usefulness for the differentiation. Then, we chose MR images using binary logistic regression analysis and performed classification and regression tree analysis with them. Diagnostic performances of the classification tree were evaluated using a stratified 10-fold cross-validation method. RESULTS: T1in, ufT2WI, fsT2WI, DWI, AP, PVP, fat content, and washout were all useful for the differentiation (p < 0.05), and AP and T1in were finally chosen for creating classification trees (p < 0.05). AP appeared in the first node in the tree. The area under the curve, sensitivity and specificity for eHCC, and balanced accuracy of the classification tree were 0.83 (95% CI 0.74-0.91), 0.64 (18/28, 95% CI 0.46-0.82), 0.94 (174/186, 95% CI 0.90-0.97), and 0.79 (95% CI 0.70-0.87), respectively. CONCLUSIONS: AP is the most useful MR image type and T1in the second in the differentiation between eHCC and pHCC.