Application of high-density 2D receiver coil arrays for improved SNR in prostate MRI.

PURPOSE: To study if adaptive image receive (AIR) receiver coil elements can be configured into a 2D array with high (>45% by diameter) element-to-element overlap, allowing improved SNR at depth (0.7-1.5× element diameter) versus conventional (20%) overlap. METHODS: An anterior array composed of twenty 10-cm diameter elements with 45% overlap arranged into a 4 × 5 grid and a similar 3 × 7 twenty-one-element posterior array were constructed. SNR and g-factor were measured in a pelvic phantom using the new high-density (HD) arrays (41 total elements) and compared to vendor AIR-based arrays (30 total elements) with conventional overlap. T2-weighted fast-spin-echo (T2SE) images acquired using both arrays were compared in 20 subjects. SNR was estimated in vivo. Results were compared blindly by three uroradiologists using a five-point scale. Images using the HD arrays were also compared to a set of images acquired over a range of acceleration factors (R = 2.0, 2.5, 3.0) with the conventional arrays. RESULTS: SNR within the phantom was on average 15% higher for R = 1.0, 1.5, and 2.0 using the HD arrays. Across the 20 subjects SNR within the prostate was 11% higher and assessed radiologically as significantly higher (p < 0.001) for the HD versus conventional arrays. At all acceleration factors the new HD arrays outperformed the conventional arrays (p ≤ 0.01), allowing increased R for similar SNR. CONCLUSION: AIR elements can be configured into 2D arrays with high (45%) element-to-element overlap, consistently providing increased SNR at depth versus arrays with conventional (20%) overlap. The SNR improvement allows increased acceleration in T2SE prostate MRI.

Re-evaluation of the reduced heart weights in male rats in a 28-day oral repeated-dose toxicity study of tetramethylammonium hydroxide.

We recently conducted a detailed hazard assessment of tetramethylammonium hydroxide (TMAH), a priority chemical substance under the Japan Chemical Substances Control Law. During this assessment, there was debate regarding the reduced heart weight observed in the treated male groups in the 28-day rat oral repeated-dose toxicity study. This finding was not observed in females in this study and in both sexes of oral toxicity studies for tetramethylammonium chloride (TMAC) or tetramethylammonium hydrogen phthalate (TMAHP). Unpublished individual data from the oral TMAH developmental and reproductive toxicity (DART) screening study were also obtained; no effect on heart weight was observed. In addition, background data on rat heart weight from six 28-day oral toxicity studies conducted in the same facility, year, strain, age, and breeder as the TMAH study were obtained from the Japan Existing Chemical Substances Database (JECDB). These investigations suggest that the statistically significant lower heart weight in the treated males in the 28-day toxicity study is likely caused by an incidental skewing of individuals with heavier heart weights toward control male groups and is not due to TMAH treatment. Thus, it is worthwhile to include as much relevant data as possible to confirm or refute unexpected findings in toxicity studies.

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists. Materials and Methods Data from patients without known csPCa who underwent MRI from January 2017 to December 2019 at one of multiple sites of a single academic institution were retrospectively reviewed. A convolutional neural network was trained to predict csPCa from T2-weighted images, diffusion-weighted images, apparent diffusion coefficient maps, and T1-weighted contrast-enhanced images. The reference standard was pathologic diagnosis. Radiologist performance was evaluated as follows: Radiology reports were used for the internal test set, and four radiologists' PI-RADS ratings were used for the external (ProstateX) test set. The performance was compared using areas under the receiver operating characteristic curves (AUCs) and the DeLong test. Gradient-weighted class activation maps (Grad-CAMs) were used to show tumor localization. Results Among 5735 examinations in 5215 patients (mean age, 66 years ± 8 [SD]; all male), 1514 examinations (1454 patients) showed csPCa. In the internal test set (400 examinations), the AUC was 0.89 and 0.89 for the DL classifier and radiologists, respectively (P = .88). In the external test set (204 examinations), the AUC was 0.86 and 0.84 for the DL classifier and radiologists, respectively (P = .68). DL classifier plus radiologists had an AUC of 0.89 (P < .001). Grad-CAMs demonstrated activation over the csPCa lesion in 35 of 38 and 56 of 58 true-positive examinations in internal and external test sets, respectively. Conclusion The performance of a DL model was not different from that of radiologists in the detection of csPCa at MRI, and Grad-CAMs localized the tumor. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Johnson and Chandarana in this issue.

Image quality comparison of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients: multi-rater assessments including PI-QUAL version 2.

OBJECTIVES: To compare the image quality of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients, with a specific focus on the degree of susceptibility artifacts. METHODS: This single-center retrospective study included post-hip arthroplasty patients who underwent 1.5T prostate MRIs between 2021 and 2023, as well as comparative 3T prostate MRIs. Three blinded abdominal radiologists retrospectively reviewed their diffusion-weighted imaging (DWI, 50 s/mm2), T2-weighted imaging (T2WI), and dynamic contrast-enhanced imaging (DCE) to evaluate the image quality. The degree of susceptibility artifacts was categorized using a three-point scale, with 3 indicating the least artifact and 1 indicating the most. Image quality was also evaluated using Prostate Imaging Quality (PI-QUAL) version 2. The median of the three raters' scores was compared between 1.5T and 3T prostate MRIs using the Wilcoxon signed-rank test. The inter-rater agreement was evaluated using the multi-rater generalized kappa. RESULTS: Twenty pairs of 1.5T and 3T prostate MRI examinations from 20 unique patients were included. The DWI susceptibility artifact score at 1.5T was significantly higher than at 3T (mean score ± standard deviation, 2.80 ± 0.41 vs. 2.35 ± 0.93, p = 0.014). In contrast, no significant differences were observed in the susceptibility artifact scores in T2WI and DCE, or in the PI-QUAL score. The inter-reader agreement in the susceptibility artifact score was moderate (multi-rater generalized kappa: 0.60) in DWI, perfect in T2WI (not applicable), and substantial (0.65) in DCE. The inter-reader agreement was fair (0.27) in the PI-QUAL score. CONCLUSION: Using 1.5T scanners may be preferable to reduce susceptibility artifacts from hip prostheses in DWI.

Natural language processing pipeline to extract prostate cancer-related information from clinical notes.

OBJECTIVES: To develop an automated pipeline for extracting prostate cancer-related information from clinical notes. MATERIALS AND METHODS: This retrospective study included 23,225 patients who underwent prostate MRI between 2017 and 2022. Cancer risk factors (family history of cancer and digital rectal exam findings), pre-MRI prostate pathology, and treatment history of prostate cancer were extracted from free-text clinical notes in English as binary or multi-class classification tasks. Any sentence containing pre-defined keywords was extracted from clinical notes within one year before the MRI. After manually creating sentence-level datasets with ground truth, Bidirectional Encoder Representations from Transformers (BERT)-based sentence-level models were fine-tuned using the extracted sentence as input and the category as output. The patient-level output was determined by compilation of multiple sentence-level outputs using tree-based models. Sentence-level classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) on 15% of the sentence-level dataset (sentence-level test set). The patient-level classification performance was evaluated on the patient-level test set created by radiologists by reviewing the clinical notes of 603 patients. Accuracy and sensitivity were compared between the pipeline and radiologists. RESULTS: Sentence-level AUCs were ≥ 0.94. The pipeline showed higher patient-level sensitivity for extracting cancer risk factors (e.g., family history of prostate cancer, 96.5% vs. 77.9%, p < 0.001), but lower accuracy in classifying pre-MRI prostate pathology (92.5% vs. 95.9%, p = 0.002) and treatment history of prostate cancer (95.5% vs. 97.7%, p = 0.03) than radiologists, respectively. CONCLUSION: The proposed pipeline showed promising performance, especially for extracting cancer risk factors from patient's clinical notes. CLINICAL RELEVANCE STATEMENT: The natural language processing pipeline showed a higher sensitivity for extracting prostate cancer risk factors than radiologists and may help efficiently gather relevant text information when interpreting prostate MRI. KEY POINTS: When interpreting prostate MRI, it is necessary to extract prostate cancer-related information from clinical notes. This pipeline extracted the presence of prostate cancer risk factors with higher sensitivity than radiologists. Natural language processing may help radiologists efficiently gather relevant prostate cancer-related text information.

Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.

PURPOSE: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. METHODS: 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test. RESULTS: Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set). CONCLUSION: DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2-5% for a commonly used specificity level.

Clinical importance of anti-Ro52 antibody for polymyositis and dermatomyositis.

OBJECTIVES: To clarify clinical features of anti-Ro52 antibody (Ab)-positive polymyositis (PM)/dermatomyositis (DM). PATIENTS AND METHODS: We retrospectively examined clinical features and status of anti-Ro52 Ab in patients with PM/DM admitted at the University of Tsukuba Hospital between January 2019 and February 2023. We compared anti-Ro52 Ab-positive and -negative groups. RESULTS: A total of 40 patients were selected and analyzed. Median age at diagnosis was 61.5 (48.8-69.3) years and 34 cases were female. Twenty-three cases were PM and 17 cases were DM (including 6 clinically amyopathic dermatomyositis: CADM). Twenty-two cases were positive for anti-Ro52 Ab, 14 for anti-ARS Ab, and 6 for anti-MDA5 Ab. Interstitial lung disease (ILD) was detected in 29 cases, 9 of which were rapidly progressive. Glucocorticoid (GC)-resistant cardiomyopathy was detected in 6 cases, malignancy in 3 cases, and Sjögren's syndrome (SS) in 4 cases. Of the 22 anti-Ro52 Ab positive cases, only 3 were single-positive and the remaining 19 cases simultaneously had other autoantibodies. Comparing the anti-Ro52 Ab-positive and -negative groups, the frequencies of anti-ARS Ab positivity (63.6% vs. 0%), ILD (95.5% vs. 44.4%), GC-resistant cardiomyopathy (27.3% vs. 0%), concomitant use of immunosuppressants (95.5% vs. 55.6%), and levels of C-reactive protein (CRP) were significantly higher in the anti-Ro52 Ab-positive group (p<0.05). The frequencies of PM/DM, positivity of anti-MDA5 Ab, malignancies, and SS were comparable between groups. CONCLUSION: Anti-Ro52 Ab were frequently positive in PM/DM and anti-Ro52 Ab-positive patients showed significantly higher rates of anti-ARS Ab positivity and ILD, GC-resistant cardiomyopathy, concomitant use of immunosuppressants, and higher levels of CRP. Anti-Ro52 Ab may be useful as a severity marker in PM/DM.

Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI.

PURPOSE: To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1 mm) T2-weighted spin-echo (T2SE) prostate MRI. METHODS: Large-area contrast and high-contrast spatial resolution of the reconstruction methods were assessed quantitatively in experimental phantom studies. The methods were next evaluated radiologically in 17 subjects at 3.0 Tesla for whom prostate MRI was clinically indicated. For each subject, the axial T2SE raw data were directed to MBIR and to the DL reconstruction at three vendor-provided levels: (L)ow, (M)edium, and (H)igh. Thin-slice images from the four reconstructions were compared using evaluation criteria related to SNR, sharpness, contrast fidelity, and reviewer preference. Results were compared using the Wilcoxon signed-rank test using Bonferroni correction, and inter-reader comparisons were done using the Cohen and Krippendorf tests. RESULTS: Baseline contrast and resolution in phantom studies were equivalent for all four reconstruction pathways as desired. In vivo, all three DL levels (L, M, H) provided improved SNR versus MBIR. For virtually, all other evaluation criteria DL L and M were superior to MBIR. DL L and M were evaluated as superior to DL H in fidelity of contrast. For 44 of the 51 evaluations, the DL M reconstruction was preferred. CONCLUSION: The deep learning reconstruction method provides significant SNR improvement in thin-slice (1 mm) T2SE images of the prostate while retaining image contrast. However, if taken to too high a level (DL High), both radiological sharpness and fidelity of contrast diminish.

Writing Case Reports Can Improve Seven Components in Clinical Reasoning.

Case reports provide scientific knowledge and opportunities for new clinical research. However, it is estimated that less than 5% of cases presented by Japanese generalists at academic conferences are published due to various barriers such as the complex process of writing articles, conducting literature searches, the significant time required, the reluctance to write in English, and the challenge of selecting appropriate journals for publication. Therefore, the purpose of this opinion paper is to provide clinicians with practical tips for writing case reports that promote diagnostic excellence. In recent years, clinical practitioners have been striving for diagnostic excellence and optimal methods to accurately and comprehensively understand the patient's condition. To write a case report, it is essential to be mindful of the elements of diagnostic excellence and consider the quality of the diagnostic reasoning process. We (the authors) are seven academic generalists who are members of the Japanese Society of Hospital General Medicine (JSHGM) - Junior Doctors Association, with a median of 7 years after graduation and extensive experience publishing case reports in international peer-reviewed journals. We conducted a narrative review and discussed ways to write case reports to promote diagnostic excellence, leveraging our unique perspectives as academic generalists. Our review did not identify any reports addressing the critical points in writing case reports that embody diagnostic excellence. Therefore, this report proposes a methodology that describes the process involved in writing diagnostic excellence-promoting case reports and provides an overview of the lessons learned. Based on our review and discussion, we explain the essential points for promoting diagnostic excellence through case reports categorized into seven components of clinical reasoning. These strategies are useful in daily clinical practice and instrumental in promoting diagnostic excellence through case reports.

Imaging of Perirenal and Intrarenal Lymphatic Vessels: Anatomy-based Approach.

The lymphatic system (or lymphatics) consists of lymphoid organs and lymphatic vessels. Despite the numerous previously published studies describing conditions related to perirenal and intrarenal lymphoid organs in the radiology literature, the radiologic findings of conditions related to intrarenal and perirenal lymphatic vessels have been scarcely reported. In the renal cortex, interlobular lymphatic capillaries do not have valves; therefore, lymph can travel along the primary route toward the hilum, as well as toward the capsular lymphatic plexus. These two lymphatic pathways can be opacified by contrast medium via pyelolymphatic backflow at CT urography, which reflects urinary contrast agent leakage into perirenal lymphatic vessels via forniceal rupture. Pyelolymphatic backflow toward the renal hilum should be distinguished from urinary leakage due to urinary injury. Delayed subcapsular contrast material retention via pyelolymphatic backflow, appearing as hyperattenuating subcapsular foci on CT images, mimics other subcapsular cystic diseases. In contrast to renal parapelvic cysts originating from the renal parenchyma, renal peripelvic cysts are known to be of lymphatic origin. Congenital renal lymphangiectasia is mainly seen in children and assessed and followed up at imaging. Several lymphatic conditions, including lymphatic leakage as an early complication and acquired renal lymphangiectasia as a late complication, are sometimes identified at imaging follow-up of kidney transplant. Lymphangiographic contrast material accumulation in the renal hilar lymphatic vessels is characteristic of chylo-urinary fistula. Chyluria appears as a fat-layering fluid-fluid level in the urinary bladder or upper urinary tract. Recognition of the anatomic pathway of tumor spread via lymphatic vessels at imaging is of clinical importance for accurate management at oncologic imaging. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.