植田 高弘

OBJECTIVE: To clarify the qualitative and quantitative MRI features of myxoid leiomyosarcoma (MLMS) in comparison with conventional leiomyosarcoma (cLMS) and to identify the specific diagnostic pitfalls of the current consensus criteria when applied to the MLMS subtype. MATERIALS AND METHODS: This retrospective multicenter study (12 institutions) included patients with histopathologically confirmed leiomyosarcoma (MLMS or cLMS) who underwent preoperative MRI. A total of 38 women (15 with MLMS and 23 with cLMS; median age, 55 years) were evaluated. Three radiologists independently evaluated features, including T2 signal patterns (purely hyperintense, mixed, iso-to-moderately hyperintense) and a characteristic "marbled appearance" (heterogeneous signal intermingling). The tumors were classified as benign or malignant on the basis of the apparent diffusion coefficient (ADC) values in accordance with the current consensus criteria. A histogram analysis was performed using multiparametric sequences. RESULTS: MLMS frequently exhibited purely T2-hyperintense or mixed patterns (12 of 15 [80%]), whereas cLMS predominantly showed T2-isointensity to moderate hyperintensity (21 of 23 [91%]) (P < 0.001). The "marbled appearance" was identified in 11 of 15 (73%) MLMS cases and 2 of 23 (9%) cLMS cases (P < 0.001). Median ADC values of the most restricted component were significantly higher in MLMS (1.14 × 10⁻³ mm²/s) than in cLMS (0.80 × 10⁻³ mm²/s) (P < 0.001). Consequently, the consensus ADC criteria misclassified 10 of 12 (83%) MLMS cases as benign, in comparison with 3 of 21 (14%) cLMS cases (P < 0.001). Notably, among these 10 misclassified MLMS cases, 8 (80%) demonstrated the "marbled appearance." Histogram analysis confirmed that patients with MLMS had higher median normalized T2-weighted imaging signal intensity ratios (1.97 vs. 1.36; P = 0.019) and ADC ratios (1.39 vs. 0.82; P = 0.007). CONCLUSION: Strict reliance on ADC-based consensus criteria may lead to systematic under-recognition of MLMS. Recognizing the "marbled appearance" on MRI is crucial to prevent misclassifying these tumors as benign.

PURPOSE: Metabolic syndrome (MetS) is screened for or assessed by manual waist circumference (WC) measurements or adipose tissue evaluation using supine multidetector CT (MDCT). Currently, upright MDCT was clinically established and tested for different clinical aims. The purpose of this study was to directly compare the utility of metabolic disorder assessments and the diagnosis of MetS among WC, upright MDCT, and supine MDCT in a Japanese health checkup test cohort. MATERIALS AND METHODS: This study cohort consisted of 47 consecutive subjects who underwent WC measurements, blood tests, upright MDCT, and supine MDCT on the same day. In each subject, the total fat area (TFA), subcutaneous fat area (SFA), visceral fat area (VFA), and visceral-to-subcutaneous fat ratio (VSR) were semi-automatically assessed using upright MDCT and supine MDCT data. To compare each index on both CTs, as well as manual WC measurements between the MetS and non-MetS groups, Student's t-test was performed. Univariate regression analyses were performed to evaluate the relationship between each index and blood test results. Subsequently, receiver operating characteristic (ROC)-based positive tests were performed. Finally, the sensitivity, specificity, and accuracy were compared among all indexes from both CTs, and a manual WC measurement evaluation was conducted using McNemar's test. RESULTS: Some indices showed significant differences between two groups (p < 0.05). All indices shows significant correlations with BMI, TG, HDL-cholesterol, uric acid, glucose, HbA1c, insulin, HOMA-IR, GOT (or AST), GPT (or ALT), or creatinine (manual WC measurement: - 0.58 ≤ r ≤ 0.87, p < 0.05; supine MDCT: - 0.61 ≤ r ≤ 0.84, p < 0.05; upright MDCT: - 0.64 ≤ r ≤ 0.91, p < 0.05). TFA and VFA on upright MDCT had significantly higher sensitivities than WCs determined from manual and supine MDCT as well as the VSR (p < 0.05). CONCLUSION: Upright MDCT-derived adipose tissue indices showed preliminary potential for assessing metabolic syndrome, but larger prospective studies are needed to validate their clinical utility.

Prevascular mediastinal lesions encompass a broad spectrum of entities, ranging from benign cysts to malignant tumors such as thymic epithelial tumors (TETs), lymphoma, and germ cell tumors (GCTs). With the increasing use of computed tomography (CT), incidentally detected, asymptomatic prevascular mediastinal nodules are encountered more frequently in daily practice. However, accurate characterization based on CT alone remains challenging because cystic lesions may exhibit soft-tissue attenuation or pseudoenhancement, resulting in substantial overlap with solid tumors and potential overtreatment. This review mainly focuses on the differential diagnosis of incidentally detected prevascular mediastinal nodules and summarizes their epidemiologic, clinical, and imaging characteristics. Particular emphasis is placed on a practical diagnostic strategy that integrates contrast-enhanced CT for lesion localization and morphologic assessment with magnetic resonance imaging (MRI) for problem-solving. Although lymphomas and GCTs usually present as large symptomatic masses rather than incidental nodules, they are also discussed because they remain important considerations in the differential diagnosis of prevascular mediastinal lesions. MRI provides superior tissue characterization, enabling reliable differentiation of cystic and solid lesions, evaluation of intratumoral architecture, assessment of tumor cellularity using diffusion-weighted imaging and apparent diffusion coefficient values, and more accurate evaluation of local invasion. The selective role of fluorine-18 fluorodeoxyglucose positron emission tomography/CT (FDG-PET/CT), particularly for lymphoma, aggressive malignancies, and disease staging, is also discussed. By integrating imaging findings with epidemiologic information, clinical presentation, and laboratory data, a stepwise diagnostic approach can improve diagnostic confidence, reduce unnecessary invasive procedures, and facilitate appropriate management of prevascular mediastinal nodules.

Background/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the super-high-resolution (SHR) mode for external, middle and inner ear evaluations and diagnosis in patients with and without otologic diseases. Methods: Included in this study were 140 patients who had undergone HDCT, consisting of 32 otologic disease patients and 108 non-otologic disease patients, and 280 inner and middle ears and temporal bones were evaluated on a per ear analysis. Signal-to-noise ratios (SNRs) of the temporal bone surrounding the aural vestibule of the ear and in the vestibule as well as the cerebellar hemisphere, overall image and detailed evaluation of the visibility of anatomical landmarks in the middle and inner ear and temporal bone obtained with the two methods were assessed and statistically compared using the paired t-test or Wilcoxon's signed-rank test. Then, receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance between two reconstruction methods. Results: Each SNR of DLR was significantly higher than that of hybrid-type IR (p < 0.05). Overall image quality and detailed visualization of each anatomical structure obtained with DLR were significantly better than those obtained with hybrid-type IR (p < 0.05). The area under the curve of DLR had no significant difference with hybrid-type IR (p = 0.18). Conclusions: DLR has superior potential to hybrid-type IR for better image quality and visualization of anatomical landmarks in middle and inner ears and temporal bones on HDCT, although diagnostic performance was not affected in clinical practice.

PURPOSE: Since the clinical application of computed tomography (CT), cardiac and respiratory motion artifacts have caused decreased image quality and reduced detection or quantitative or qualitative evaluation of lung parenchymal or vascular abnormalities on chest CT with lung window settings in patients with pulmonary diseases. Recently, a deep learning (DL)-based motion correction algorithm (CLEAR Motion) has been developed and clinically used for chest CT. We hypothesized that CLEAR Motion can significantly reduce motion artifacts on chest CT examinations relative to conventional chest CT images reconstructed without CLEAR Motion. The purpose of this study was to determine the utility of CLEAR Motion for image quality improvement in chest CT with lung window settings in patients with various pulmonary diseases. MATERIALS AND METHODS: Fifty-six consecutive patients with various thoracic diseases underwent non-electrocardiogram-gated chest helical CT examination using a 320-detector row CT and underwent reconstruction using the conventional reconstruction method and CLEAR Motion. To compare the quantitative image quality, the cardio-pulmonary edge distance (CPED) and slope (CPES) were measured on each CT scan in the axial plane. Comparing cardiac motion reduction capability, overall image quality, cardiac motion artifact, and region conspicuity were visually assessed in the lung window setting on the axial, coronal, and sagittal planes. The paired t-test and Wilcoxon signed-rank test were then performed. RESULTS: The CPEDs and CPESs of the entire lung and left lung on CT with CLEAR Motion were significantly superior to those of CT without CLEAR Motion (p < 0.001). The overall image quality, cardiac motion artifact, and region conspicuity on CT with CLEAR Motion were significantly higher than those without CLEAR Motion on each plane (p < 0.001). CONCLUSION: The DL-based motion correction algorithm named as 'CLEAR Motion' has a potential to improve image quality on chest CT with lung window setting in patients with pulmonary diseases.