Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study.

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.

CT features for diagnosing acute torsion of uterine subserosal leiomyoma.

PURPOSE: To evaluate the usefulness of computed tomographic (CT) features for identifying acute torsion of uterine subserosal leiomyoma (USL). SUBJECTS AND METHODS: We analyzed contrast-enhanced CT examinations of 7 USLs with torsion and 44 USLs without torsion. Two radiologists evaluated the CT features, which consisted of poor contrast enhancement inside the USL, thin rim enhancement around the USL, calcification within the USL, a beak sign between the uterus and USL, fan-shaped poor contrast enhancement in the uterus area adjacent to the USL (dark fan sign), and ascites. We analyzed the frequencies of these CT features in the USLs with versus without torsion using Fisher's exact test. RESULTS: The respective frequencies of CT features in USLs with and without torsion were as follows: poor contrast enhancement, 86 and 5% (P = 0.001); thin rim enhancement, 71 and 9% (P = 0.001); calcification, 29 and 18% (P = 0.61); beak sign, 57 and 86% (P = 0.10); dark fan sign, 57 and 0% (P = 0.001); and ascites, 100 and 20% (P = 0.01). CONCLUSIONS: The CT features of poor contrast enhancement, thin rim enhancement, and dark fan sign are valuable for identifying acute torsion of USL.

Magnetic Resonance Imaging of a Urethral Caruncle and the Pathologic Correlation: A Report of 3 Cases.

A urethral caruncle is the most common disease of the urethra in postmenopausal women. A definitive diagnosis can usually be reached based on physical examination. Cross-sectional imaging is performed when malignant urethral tumor is suspected, such as a urethral carcinoma. No articles have discussed the detailed imaging of urethral caruncles. We present 3 patients with symptomatic urethral caruncles who underwent magnetic resonance imaging preoperatively.

CT urography in the urinary bladder: to compare excretory phase images using a low noise index and a high noise index with adaptive noise reduction filter.

BACKGROUND: Although CT urography (CTU) is widely used for the evaluation of the entire urinary tract, the most important drawback is the radiation exposure. PURPOSE: To evaluate the effect of a noise reduction filter (NRF) using a phantom and to quantitatively and qualitatively compare excretory phase (EP) images using a low noise index (NI) with those using a high NI and postprocessing NRF (pNRF). MATERIAL AND METHODS: Each NI value was defined for a slice thickness of 5 mm, and reconstructed images with a slice thickness of 1.25 mm were assessed. Sixty patients who were at high risk of developing bladder tumors (BT) were divided into two groups according to whether their EP images were obtained using an NI of 9.88 (29 patients; group A) or an NI of 20 and pNRF (31 patients; group B). The CT dose index volume (CTDI(vol)) and the contrast-to-noise ratio (CNR) of the bladder with respect to the anterior pelvic fat were compared in both groups. Qualitative assessment of the urinary bladder for image noise, sharpness, streak artifacts, homogeneity, and the conspicuity of polypoid or sessile-shaped BTs with a short-axis diameter greater than 10 mm was performed using a 3-point scale. RESULTS: The phantom study showed noise reduction of approximately 40% and 76% dose reduction between group A and group B. CTDI(vol) demonstrated a 73% reduction in group B (4.6 ± 1.1 mGy) compared with group A (16.9 ± 3.4 mGy). The CNR value was not significantly different (P = 0.60) between group A (16.1 ± 5.1) and group B (16.6 ± 7.6). Although group A was superior (P < 0.01) to group B with regard to image noise, other qualitative analyses did not show significant differences. CONCLUSION: EP images using a high NI and pNRF were quantitatively and qualitatively comparable to those using a low NI, except with regard to image noise.