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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., (© 2024. The Author(s).)

Intracystic papillary neoplasm (ICPN) of gallbladder is a comparatively new concept and is described as pre-malignant lesions in Nakanuma et al. (In: Bosman et al. (eds) WHO Classification of Tumours of the Digestive System, World Health Organization of Tumours, IARC, Lyon, 2010). ICPN with high-grade intraepithelial neoplasia is understood to include intraepithelial carcinoma or noninvasive carcinoma. And lesions with invasive cancer components are classified as ICPN with an associated invasive carcinoma [1]. According to Adsay et al., more than half of patients diagnosed with ICPN have invasive cancer components (Adsay et al., Am J Surg Pathol 36:1279-1301, 2012).Polypoid masses in the gallbladder including benign, malignant, and non-neoplastic lesions have been called gallbladder polyps, and ICPN is also a polypoid lesion in the gallbladder. However, it is difficult to differentiate between them. In the literature, it is said that the possibility of malignancy is high in lesions exceeding 1 cm (Terzi et al., Surgery 127:622-627, 2000). And there are few reports on characteristic imaging findings of ICPN.We have experienced three cases (two females and one male) of ICPN and report our imaging findings. Contrast-enhanced computed tomography revealed large papillary polypoid lesions approximately 2-4 cm in size in the gallbladder. Findings suggestive of deformation of the gallbladder wall and extrinsic progression were absent in all cases. T2-weighted magnetic resonance imaging revealed intense signals and diffusion-weighted imaging showed high intensity. Expanding of the gallbladder was seen in case 1, and a tumor stalk-like appearance was seen in the papillary mass in cases 2 and 3. Surgery was performed in all three cases and ICPN was diagnosed pathologically. The cancer was localized to the mucosa, with no infiltration of surrounding tissue in all three 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.

We performed numerical and visual evaluation of compressed sensing MRI (CS-MRI) using 3D Cartesian sampling by numerical simulation. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weighted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for numerical evaluation. Sampling ratio of the Cartesian grid was 30%. Reconstruction was performed by the projection onto convex sets (POCS) method with soft thresholding, subject to data fidelity constraints. In the absence of noise, RMSE of 3D-T1WI was 1.50, ant that of the 2D-T1WI of the transverse plane was in the range of 1.06 to 1.54; anatomical ROIs was in the range of 0.75 to 2.80; those of T2WI were 3.20, 2.77 to 3.06, and 1.81 to 4.51; those of PDWI were 1.69, 1.33 to 1.49, and 1.08 to 1.86. Visual evaluation was performed by three radiologists on the basis of three categories: artifact, anatomical structure, and tissue contrast. Average score of the visual evaluation indicated that anatomical structure and tissue contrast of CS images were equal to those of the original image, although a few artifacts were visible. If noise level was assumed to be 20 dB or less, anatomical structure and tissue contrast were not significantly degraded compared to noise-free CS images.

Two-dimensional radial MRI using compressed sensing (2D radial CS) enables incoherence sampling in k space unlike conventional Cartesian MRI, however 2D radial CS has not been sufficiently investigated. Numerical and visual evaluations of 2D radial CS were performed in this paper. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weigthted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for the numerical evaluation. The Brainweb MRI Data Base was used for test images. Projection of 80 spokes with linear sampling of 256 pixels was used. Reconstruction was performed by minimizing the L1 norm of a transformed image using wavelet transform and spatial finite-differences (total variation), subject to data fidelity constraint. In the absence of noise, the root mean square error (RMSE) of T1WI was in the range of 3.75 to 5.05; that of the anatomical region of interests (ROIs) was in the range of 1.54 to 10.24; those of T2WI were 8.75 to 11.65 and 4.31 to 6.99; and those of PDWI were 3.44 to 4.46 and 1.34 to 3.09. Visual evaluation was performed by three radiologists on the basis of three categories: artifact, anatomical structure, and tissue contrast. Average percent scores of the visual evaluation were 96% for T1WI, 74-81% for T2WI, and 81-89% for PDWI.

This paper describes numerical and visual evaluations of compressed sensing MRI (CS-MRI) using 2D Cartesian sampling by numerical simulation. The BrainWeb MRI Data Base was used for test images. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weighted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for the numerical evaluation. Sampling ratio was 50%. Reconstruction was performed by minimizing the L1 norm of a transformed image using wavelet transform and total variation, subject to data fidelity constraints. The conjugate gradient method was used in the minimization of the object function. In the absence of noise, the root mean square error (RMSE) of T1WI was in the range of 2.99 to 3.57; that of the anatomical region of interests (ROIs) was in the range of 1.77 to 8.53; those of T2WI were 4.72 to 5.65 and 3.28 to 5.54; and those of PDWI were 1.91 to 2.36 and 1.32 to 2.09. Visual evaluation was performed by three radiologists on the basis of three categories: artifact, anatomical structure, tissue contrast. CS image quality was nearly equal to that of the original image, although a few artifacts were visible. If the noise level was assumed to be 30 dB or less, T1-CS image and PD-CS images were not significantly degraded compared to noise-free images.

Purpose: To retrospectively determine whether hepatobiliary phase (HBP) sequence outperforms unenhanced T1-weighted imaging (uT1wI) in distinguishing the ablation margin (AM) from hepatocellular carcinoma (HCC) 24 h after thermoablation., Material and Methods: Ninety-one patients [mean age, 65.7 years; 68 M/23F] with 138 HCCs (>6 months follow-up) underwent pre- and postablation gadoxetate disodium-enhanced MRI. AM showed a hyperintense middle zone (MZ) surrounding central hypo- or hyperintense HCCs on uT1wI, and an intermediate-intense MZ encompassing central hypo- or hyperintense HCCs during HBP. The visible AM was defined as persistent MZ around HCCs, which were demarcated from MZ, or peripherally band encompassing MZ, which were not demarcated from HCC. The indefinite AM was defined as no demarcating HCCs from MZ. The ability to distinguish AM from HCC was classified as visible or indefinite on axial (ax)-uT1wI, ax-HBP, coronal (cor)-HBP, and combined all images. To investigate the AM visibility during HBP, significance of differences upon comparison of ax-uT1wI with combined images was analyzed. Preablation liver-tumor contrast ratio (LTCR) on ax-uT1wI and ax-HBP sequence is compared between the visible and indefinite AM., Results: The McNemar test demonstrated a significant increase (p < 0.05) in visible AM from ax-uT1wI (60), to ax-HBP (70), cor-HBP (79), and combined images (83). TLCR with visible AM was significantly higher than that with indefinite AM on ax-uT1wI (0.4 vs. 0.2, p = 0.001) and ax-HBP sequence (0.9 vs. 0.6, p = 0.004)., Conclusions: HBP sequence might have higher feasibility to distinguish AM from tumor than ax-uT1wI. The TLCR value in visible AM was higher than that in indefinite AM on both ax-uT1wI and ax-HBP sequences.

Purpose: To evaluate morphological and signal intensity (SI) changes of placental insufficiency on magnetic resonance imaging (MRI) and to assess morphological changes and decreased flow voids (FVs) on T2-weighted rapid acquisition with relaxation enhancement (RARE) images for diagnosing placental insufficiency., Methods: Fifty singleton fetuses underwent MRI using a 1.5-T MR scanner. Placental thickness, area, volume, SI, amniotic fluid SI, and size of FVs between the uterus and the placenta were measured on MR images. Two radiologists reviewed T2-weighted RARE images for globular appearance of the placenta and FVs between the uterus and the placenta. Data were analyzed using t-tests, McNemar's tests, and areas under the receiver operating characteristic curve (AUCs) at 5% level of significance., Results: Twenty-five of the 50 pregnancies were categorized as having an insufficient placenta. Significant differences were observed between insufficient and normal placentas in mean placental thickness, area, volume, placenta to amniotic fluid SI ratio, and size of FVs (49.0 mm vs. 36.9 mm, 1.62 × 10(4) mm(2) vs. 2.67 × 10(4) mm(2), 5.13 × 10(5) mm(3) vs. 6.56 × 10(5) mm(3), 0.549 vs. 0.685, and 3.4 mm vs. 4.3 mm, respectively). The sensitivity and accuracy using globular appearance plus decreased FVs were greater than those using decreased FVs (P < 0.01). There was no significant difference among AUCs using globular appearance and decreased FVs, and globular appearance plus decreased FVs., Conclusions: Placental insufficiency was associated with placental thickness, area, volume, placenta to amniotic fluid SI ratio, and size of FVs. Evaluating FVs on T2-weighted RARE images can be useful for detecting placental insufficiency, particularly in placentas without globular appearance on MR images.