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Purpose: To assess the value of orthogonal axial images (OAI) of MRI in gastric cancer T staging. Methods: This retrospective study enrolled 133 patients (median age, 63 [range, 24–85] years) with gastric adenocarcinoma who underwent both CT and MRI followed by surgery. MRI lacking or incorporating OAI and CT images were evaluated, respectively. Diagnostic performance (accuracy, sensitivity, and specificity) for each T stage, overall diagnostic accuracy and rates of over- and understaging were quantified employing pathological T stage as a reference standard. The McNemar's test was performed to compare the overall accuracy. Results: Among patients with pT1–pT4 disease, MRI with OAI (accuracy: 88.7–94.7%, sensitivity: 66.7–93.0%, specificity: 91.5–100.0%) exhibited superior diagnostic performance compared to MRI without OAI (accuracy: 81.2–88.7%, sensitivity: 46.2–83.1%, specificity: 85.5–99.1%) and CT (accuracy: 88.0–92.5%, sensitivity: 53.3–90.1%, specificity: 88.7–98.1%). The overall accuracy of MRI with OAI was significantly higher (83.5%) than that of MRI without OAI (67.7%) (p <.001). However, there was no significant difference in the overall accuracy of MRI with OAI and CT (78.9%) (p =.35). The over- and understaging rates of MRI with OAI (12.0, 4.5%) were lower than those of MRI without OAI (21.8, 10.5%) and CT (12.8, 8.3%). Conclusion: OAI play a pivotal role in the T staging of gastric cancer. MRI incorporating OAI demonstrated commendable performance for gastric cancer T-staging, with a slight tendency toward its superiority over CT. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort. Methods: Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WIS), standard diffusion-weighted imaging (DWIS), fast T2WI with DLR (T2WIDL), and fast DWI with DLR (DWIDL) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWIS and DWIDL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the area under the receiver operating characteristic curve (AUC). Results: Compared to T2WIS and DWIS, T2WIDL and DWIDL reduced the acquisition time from 5:57 min to 3:13 min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR. Conclusions: The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer. [ABSTRACT FROM AUTHOR]

Purpose: To explore the diagnostic efficacy of MR-based texture analysis in differentiation of small (≤ 4 cm) and very small (≤ 2 cm) renal cell carcinoma subtypes. Methods: One hundred and eight patients with pT1a (≤ 4 cm) renal cell carcinoma and pretreatment MRI were enrolled in this retrospective study. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from whole-tumor images. Among subtypes, patient age, tumor size, histological grading and texture parameters were compared. Diagnostic model using combination of texture parameters was constructed using logistic regression and validated using fivefold cross-validation. AUC with 95% CI, accuracy, sensitivity and specificity for subtype differentiation are reported. Further we explored the distinguishing ability of texture parameters and diagnostic model in very small (≤ 2 cm) RCC subgroups. Results: Significant texture parameters among RCC subtypes were identified. For small (≤ 4 cm) renal cell carcinoma subtyping, combining models based on texture parameters achieved good AUCs for differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC (0.79, 0.74 and 0.81). Further, in subgroups of very small (≤ 2 cm) RCCs, diagnostic models had better differentiating performances, achieving AUCs of 0.88, 0.99, 0.96 in differentiating ccRCC vs. non-ccRCC, chRCC vs. non-chRCC and ccRCC vs. chRCC. Conclusion: MR texture analysis may help to differentiate small (≤ 4 cm) and very small (≤ 2 cm) RCC subtypes. This non-invasive method can potentially provide additional information for localized RCC treatment and surveillance strategy. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the value of magnetic resonance imaging (MRI)-based radiomics in predicting the treatment response to concurrent chemoradiotherapy (CCRT) in patients with locally advanced cervical squamous cell cancer (LACSC). Methods: In total, 198 patients (training: n = 138; testing: n = 60) with LACSC treated with CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Responses were evaluated by MRI and clinical data performed at one month after completion of CCRT according to RECIST standards, and patients were divided into the residual group and nonresidual group. Overall, 200 radiomics features were extracted from T2-weighted imaging and apparent diffusion coefficient maps. The radiomics score (Rad-score) was constructed with a feature selection strategy. Logistic regression analysis was used for multivariate analysis of radiomics features and clinical variables. The performance of all models was assessed using receiver operating characteristic curves. Results: Among the clinical variables, tumor grade and FIGO stage were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.741 and 0.749 in the training and testing groups. The Rad-score, consisting of 4 radiomics features selected from 200 radiomics features, showed good predictive performance with an AUC of 0.819 in the training group and 0.776 in the testing group, which were higher than the clinical model, but the difference was not statistically significant. The combined model constructed with tumor grade, FIGO stage, and Rad-score achieved the best performance, with an AUC of 0.857 in the training group and 0.842 in the testing group, which were significantly higher than the clinical model. Conclusion: MRI-based radiomics features could be used as a noninvasive biomarker to improve the ability to predict the treatment response to CCRT in patients with LACSC. [ABSTRACT FROM AUTHOR]

Purpose: With advancements in medical imaging, more renal tumors are detected early, but it remains a challenge for radiologists to accurately distinguish subtypes of renal parenchymal tumors. We aimed to establish a novel deep convolutional neural network (CNN) model and investigate its effect on identifying subtypes of renal parenchymal tumors in T2-weighted fat saturation sequence magnetic resonance (MR) images. Methods: This retrospective study included 199 patients with pathologically confirmed renal parenchymal tumors, including 77, 46, 34, and 42 patients with clear cell renal cell carcinoma (ccRCC), chromophobe renal cell carcinoma (chRCC), angiomyolipoma (AML), and papillary renal cell carcinoma (pRCC), respectively. All enrolled patients underwent kidney MR scans with the field strength of 1.5 Tesla (T) or 3.0 T before surgery. We selected T2-weighted fat saturation sequence images of all patients and built a deep learning model to determine the type of renal tumors. Receiver operating characteristic (ROC) curve was depicted to estimate the performance of the CNN model; the accuracy, precision, sensitivity, specificity, F1-score, and area under the curve (AUC) were calculated. One-way analysis of variance and χ2 tests of independent samples were used to analyze the variables. Results: The experimental results demonstrated that the model had a 60.4% overall accuracy, a 61.7% average accuracy, and a macro-average AUC of 0.82. The AUCs for ccRCC, chRCC, AML, and pRCC were 0.94, 0.78, 0.80, and 0.76, respectively. Conclusion: Deep CNN model based on T2-weighted fat saturation sequence MR images was useful to classify the subtypes of renal parenchymal tumors with a relatively high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To compare the diagnostic accuracies of MDCT and high-resolution MRI (HR-MRI) for regional nodal metastases with different short-axis diameter ranges in rectal cancer patients. Methods: Rectal adenocarcinoma patients who underwent both MDCT and HR-MRI before surgery were included. The maximum short-axis diameters of the nodes were measured, and were classified as benign or malignant on imaging findings. All of the nodes were subdivided as follows: ≤ 5 mm (Group A), > 5 mm and ≤ 10 mm (Group B) , and > 10 mm (Group C). The postoperative pathological reports were used as the standard, and the sensitivity, specificity, accuracy, ROC curve, and AUC value were calculated for each subgroup. Results: A total of 592 nodes were included in the node-to-node evaluation. In Group A, the specificity and accuracy of HR-MRI were significantly higher than those of MDCT (99.28% vs. 93.99%, P < 0.001; 95.78% vs. 89.56%, P = 0.010; respectively). In Group B, the specificity and accuracy of HR-MRI were also higher than those of MDCT (98.36% vs. 55.74%, P < 0.001; 80.45% vs. 66.17%, P < 0.001; respectively). For Groups A and B, the AUCs of MDCT were both 0.65, whereas those of HR-MRI were 0.76 and 0.82, respectively. In Group C, all nine malignant nodes were correctly diagnosed metastases on MDCT, whereas one was misjudged as benign on HR-MRI. Conclusions: The diagnostic value of HR-MRI is superior to that of MDCT, with higher specificity, accuracy, and AUC values for HR-MRI than for MDCT. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study was to analyze MR imaging features of renal epithelioid angiomyolipoma (EAML).Methods: This study included 17 patients with histopathologically confirmed renal EAML who underwent renal MRI scanning before radical or partial nephrectomy. MR images were retrospectively reviewed and correlated with pathological findings.Result: Fifteen lesions (88.2%) appeared as round or oval. The tumor-kidney interface was round in 14 lesions (82.4%). Fifteen tumors (88.2%) presented mainly isointensity on T1WI, and eleven tumors (64.7%) presented mainly hypointensity on T2WI. Twelve lesions (70.6%) showed restricted diffusion on DWI, and the mean ADC value was 1.23 ± 0.28 × 10−3mm2/s. Minimal fat component was identified as loss of signal intensity on opposed-phase MR images in 6 cases (35.3%). Sixteen lesions (100%) demonstrated inhomogeneous enhancement, and 7 of 16 masses (43.8%) showed reticular enhancement. Rapid wash-in and wash-out enhancement was seen in 13 masses (81.3%). In the corticomedullary phase, the mass showed markedly enhancement in 14 cases (87.5%). The irregular vessels and hemorrhage were detected in 4 cases (23.5%) and 7 cases (41.2%), respectively. One patient (5.9%) had a lymph node involvement at initial diagnosis, and showed distant metastasis after operation. In the immunohistochemical analysis, 15 tumors (88.2%) were positive for melanocytic marker (HMB45 or Melan-A), and all cases (100%) were negative for epithelial-associated markers (CK or AE1/AE3).Conclusion: The presence of hypointensity on T2WI, restricted diffusion on DWI, round tumor-kidney interface, reticular, and marked enhancement (rapid wash-in and wash-out) should further raise suspicion for renal EAML. The diagnosis may be confirmed by pathological analysis. [ABSTRACT FROM AUTHOR]