Japanese Journal of Radiology最新文献

{"title":"Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm.","authors":"Keitaro Sofue, Yoshiko Ueno, Shinji Yabe, Eisuke Ueshima, Takeru Yamaguchi, Atsuhiro Masuda, Arata Sakai, Hirochika Toyama, Takumi Fukumoto, Masatoshi Hori, Takamichi Murakami","doi":"10.1007/s11604-025-01804-7","DOIUrl":"10.1007/s11604-025-01804-7","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the image quality and clinical utility of a deep learning reconstruction (DLR) algorithm in ultra-high-resolution computed tomography (UHR-CT) for the diagnosis of pancreatic cystic neoplasms (PCNs).</p><p><strong>Methods: </strong>This retrospective study included 45 patients with PCNs between March 2020 and February 2022. Contrast-enhanced UHR-CT images were obtained and reconstructed using DLR and hybrid iterative reconstruction (IR). Image noise and contrast-to-noise ratio (CNR) were measured. Two radiologists assessed the diagnostic performance of the imaging findings associated with PCNs using a 5-point Likert scale. The diagnostic performance metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC), were calculated. Quantitative and qualitative features were compared between CT with DLR and hybrid IR. Interobserver agreement for qualitative assessments was also analyzed.</p><p><strong>Results: </strong>DLR significantly reduced image noise and increased CNR compared to hybrid IR for all objects (p < 0.001). Radiologists rated DLR images as superior in overall quality, lesion delineation, and vessel conspicuity (p < 0.001). DLR produced higher AUROC values for diagnostic imaging findings (ductal communication: 0.887‒0.938 vs. 0.816‒0.827 and enhanced mural nodule: 0.843‒0.916 vs. 0.785‒0.801), although DLR did not directly improve sensitivity, specificity, and accuracy. Interobserver agreement for qualitative assessments was higher in CT with DLR (κ = 0.69‒0.82 vs. 0.57‒0.73).</p><p><strong>Conclusion: </strong>DLR improved image quality and diagnostic performance by effectively reducing image noise and improving lesion conspicuity in the diagnosis of PCNs on UHR-CT. The DLR demonstrated greater diagnostic confidence for the assessment of imaging findings associated with PCNs.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1652-1662"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in drug treatment for prostate cancer.","authors":"Kotaro Suzuki, Naohiro Fujimoto, Masaki Shiota, Satoru Kawakami, Takahiro Kimura, Kohei Hashimoto, Takeo Kosaka, Hideaki Miyake, Hiroji Uemura","doi":"10.1007/s11604-025-01816-3","DOIUrl":"10.1007/s11604-025-01816-3","url":null,"abstract":"<p><p>With the advent of androgen receptor signaling inhibitors (ARSI), chemotherapy, poly (adenosine diphosphate-ribose) polymerase inhibitor (PARPi), and radioligand therapy targeting prostate-specific membrane antigen (PSMA), survival outcomes in metastatic prostate cancer (PCa) have dramatically improved over the past two decades. In addition, recent advancements in imaging modalities and genomic testing have significantly impacted diagnostic and therapeutic strategies for advanced PCa. However, with increasing drug options and the emergence of new disease concepts for non-metastatic castration-resistant prostate cancer (nmCRPC), treatment strategies for advanced PCa have become more complicated and uncertain. In addition, the emergence of aggressive PCa variants, including treatment-related neuroendocrine PCa (t-NEPC), is another clinical concern in the era of upfront and long-term use of potent ARSIs. The aim of this review is to summarize the latest drug treatment strategies and clinical concerns in the management of advanced PCa in Japan, based on the Japanese Clinical Practice Guidelines for Prostate Cancer published in 2023.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1618-1627"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144325745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.","authors":"Sonia Triggiani, Giuseppe Pellegrino, Sveva Mortellaro, Alessandra Bubba, Carolina Lanza, Serena Carriero, Pierpaolo Biondetti, Salvatore Alessio Angileri, Roberta Fusco, Vincenza Granata, Gianpaolo Carrafiello","doi":"10.1007/s11604-025-01811-8","DOIUrl":"10.1007/s11604-025-01811-8","url":null,"abstract":"<p><p>Pulmonary embolism (PE) remains a critical condition that demands rapid and accurate diagnosis, for which computed tomographic pulmonary angiography (CTPA) is widely recognized as the diagnostic gold standard. However, recent advancements in imaging technologies-such as dual-energy computed tomography (DECT), photon-counting CT (PCD-CT), and artificial intelligence (AI)-offer promising enhancements to traditional diagnostic methods. This study reviews past, current and emerging technologies, focusing on their potential to optimize diagnostic accuracy, reduce contrast volumes and radiation doses, and streamline clinical workflows. DECT, with its dual-energy imaging capabilities, enhances image clarity even with lower contrast media volumes, thus reducing patient risk. Meanwhile, PCD-CT has shown potential for dose reduction and superior image resolution, particularly in challenging cases. AI-based tools further augment diagnostic speed and precision by assisting radiologists in image analysis, consequently decreasing workloads and expediting clinical decision-making. Collectively, these innovations hold promise for improved clinical management of PE, enabling not only more accurate diagnoses but also safer, more efficient patient care. Further research is necessary to fully integrate these advancements into routine clinical practice, potentially redefining diagnostic workflows for PE and enhancing patient outcomes.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1575-1589"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LI-RADS CT/MRI radiation treatment response assessment version 2024: the importance of pretreatment stage and time.","authors":"Sung-Hua Chiu, Aya Kamaya, Justin Ruey Tse","doi":"10.1007/s11604-025-01808-3","DOIUrl":"10.1007/s11604-025-01808-3","url":null,"abstract":"","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1725-1726"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-b-value diffusion-weighted imaging-derived parameters for differentiating high-grade serous ovarian carcinoma from other epithelial ovarian cancers.","authors":"Tsukasa Saida, Miki Yoshida, Taishi Amano, Masashi Shindo, Reo Nemoto, Takeo Iizuka, Ayumi Shikama, Toyomi Satoh, Takahito Nakajima","doi":"10.1007/s11604-025-01813-6","DOIUrl":"10.1007/s11604-025-01813-6","url":null,"abstract":"<p><strong>Objective: </strong>High-grade serous carcinoma (HGSC) is the most common ovarian cancer subtype, and its differentiation from others is crucial for treatment. This study aimed to evaluate parameters derived from multi-b-value diffusion-weighted imaging (DWI), including apparent diffusion coefficient (ADC), and metrics based on intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI), for differentiating HGSC from other ovarian cancers.</p><p><strong>Methods: </strong>We retrospectively analysed patients with primary epithelial ovarian cancer who underwent preoperative MRI including multi-b-value DWI. From the solid tissues of the tumours, diffusion parameters were derived from the multi-b-value DWI data using different models: ADC using a mono-exponential model; the true diffusion coefficient (Di), pseudo-diffusion coefficient (D*), and perfusion fraction (f) using the IVIM model; and kurtosis (K) using the DKI model.</p><p><strong>Results: </strong>This study included 56 patients with different histological cancer subtypes (mean age, 60 years; range, 24-87 years). The mean values of HGSC compared to the other cancers showed lower ADC (0.58 ± 0.21 × 10⁻³ mm²/s vs. 0.76 ± 0.18 × 10⁻³ mm²/s, p < 0.001), lower Di (0.37 ± 0.09 × 10⁻³ mm²/s vs. 0.42 ± 0.15 × 10⁻³ mm²/s, p = 0.201), and lower f (35.79 ± 11.48% vs. 48.01 ± 17.21%, p = 0.003), with a higher K (1.06 ± 0.25 vs. 0.84 ± 0.20, p = 0.341). Among these parameters, ADC showed the highest diagnostic performance in differentiating HGSC from others, with an area under the receiver operating characteristic curve of 0.79. These trends were particularly pronounced between HGSC and clear cell carcinoma, with significant differences in all parameters except D*. Additionally, K _Mean was the only parameter that showed a significant difference between HGSC and endometrioid carcinoma.</p><p><strong>Conclusion: </strong>Multi-b-value DWI-derived parameters, particularly ADC, may aid in the non-invasive preoperative differentiation of HGSC from other ovarian cancers. Multi-b-value DWI-derived parameters, especially ADC, demonstrated utility in differentiating high-grade serous carcinoma (HGSC) from other ovarian cancers, highlighting their potential in non-invasive preoperative tumor characterization.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1678-1687"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to: LI-RADS CT/MRI radiation treatment response assessment version 2024: the importance of pretreatment stage and time.","authors":"Jaeseung Shin, Sunyoung Lee","doi":"10.1007/s11604-025-01820-7","DOIUrl":"10.1007/s11604-025-01820-7","url":null,"abstract":"","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1727-1728"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of education on radiation exposure for medical students prior to clinical clerkship.","authors":"Satoshi Yamauchi, Kaede Kido, Ryosuke Taiji, Tomoko Ochi, Chisa Yoneima, Yuto Chanoki, Keisuke Oshima, Kohei Wakatsuki, Toshihiro Tanaka","doi":"10.1007/s11604-025-01815-4","DOIUrl":"10.1007/s11604-025-01815-4","url":null,"abstract":"<p><strong>Purpose: </strong>The widespread use of radiation in diagnostic imaging, interventional procedures, and radiotherapy requires robust radiation safety education. Despite its importance, medical students in Japan and other countries have limited awareness of radiation protection. In Japan, radiation safety education is essential due to recent legislative changes that position medical students as \"student doctors\" and expand their clinical responsibilities. The purpose of this study was to evaluate the knowledge, concerns, and anxieties of 5th-year medical students regarding radiation exposure before clinical training, and to evaluate the impact of a lecture-style intervention on these parameters.</p><p><strong>Materials and methods: </strong>A 20-min lecture on radiation safety was given to 95 fifth-year students at Nara Medical University immediately before clinical training. The lecture covered the basic concepts of radiation biology, legal regulations, radiation risks, and protective measures. A 25-item questionnaire using a five-point Likert scale was administered before and after the lecture. Exploratory factor analysis was conducted on the data, and mixed-design analysis of variance (ANOVA) was used to evaluate changes in the main factors identified by exploratory factor analysis. Subgroup analysis was also conducted based on gender and the selected clinical department.</p><p><strong>Results: </strong>Exploratory factor analysis identified four main factors: anxiety, interest, knowledge, and management of radiation exposure. After the lecture-based intervention, knowledge and management of radiation exposure scores increased significantly (p < .001), but interest did not change. There was no difference in score by gender.</p><p><strong>Conclusion: </strong>Lecture-based interventions can significantly improve senior medical students' knowledge and management skills regarding radiation safety. However, it was difficult to improve the interest of students who chose departments that were considered to have a low risk of radiation exposure. Our findings indicate the need for further development of educational strategies to improve awareness and education of radiation protection in clinical situations.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1716-1724"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High visceral-to-subcutaneous fat area ratio is an unfavorable prognostic indicator in patients with uterine sarcoma.","authors":"Mariko Kurokawa, Wataru Gonoi, Shouhei Hanaoka, Ryo Kurokawa, Shunichi Uehara, Masayoshi Kato, Mizuka Suzuki, Yusuke Toyohara, Yasunobu Takaki, Misako Kusakabe, Nao Kino, Takehiro Tsukazaki, Toshiyuki Unno, Kenbun Sone, Osamu Abe","doi":"10.1007/s11604-025-01812-7","DOIUrl":"10.1007/s11604-025-01812-7","url":null,"abstract":"<p><strong>Purpose: </strong>Uterine sarcoma is a rare disease whose association with body composition parameters is poorly understood. This study explored the impact of body composition parameters on overall survival with uterine sarcoma.</p><p><strong>Materials and methods: </strong>This multicenter study included 52 patients with uterine sarcomas treated at three Japanese hospitals between 2007 and 2023. A semi-automatic segmentation program based on deep learning analyzed transaxial CT images at the L3 vertebral level, calculating body composition parameters as follows: area indices (areas divided by height squared) of skeletal muscle, visceral and subcutaneous adipose tissue (SMI, VATI, and SATI, respectively); skeletal muscle density; and the visceral-to-subcutaneous fat area ratio (VSR). The optimal cutoff values for each parameter were calculated using maximally selected rank statistics with several p value approximations. The effects of body composition parameters and clinical data on overall survival (OS) and cancer-specific survival (CSS) were analyzed.</p><p><strong>Results: </strong>Univariate Cox proportional hazards regression analysis revealed that advanced stage (III-IV) and high VSR were unfavorable prognostic factors for both OS and CSS. Multivariate Cox proportional hazard regression analysis revealed that advanced stage (III-IV) (hazard ratios (HRs), 4.67 for OS and 4.36 for CSS, p < 0.01), and high VSR (HRs, 9.36 for OS and 8.22 for CSS, p < 0.001) were poor prognostic factors for both OS and CSS. Added values were observed when the VSR was incorporated into the OS and the CSS prediction models.</p><p><strong>Conclusion: </strong>Increased VSR and tumor stage are significant predictors of poor overall survival in patients with uterine sarcoma.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1670-1677"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RANO 2.0: critical updates and practical considerations for radiological assessment in neuro-oncology.","authors":"Akihiko Sakata, Yasutaka Fushimi, Sonoko Oshima, Megumi Uto, Yohei Mineharu, Satoshi Nakajima, Sachi Okuchi, Takayuki Yamamoto, Sayo Otani, Satoshi Ikeda, Shigeki Takada, Takashi Mizowaki, Yoshiki Arakawa, Yuji Nakamoto","doi":"10.1007/s11604-025-01821-6","DOIUrl":"10.1007/s11604-025-01821-6","url":null,"abstract":"<p><p>Appropriate response assessment criteria are crucial for accurate evaluation of clinical trial outcomes, and numerous criteria have been proposed to address this need. With the introduction of Response Assessment in Neuro-Oncology (RANO) criteria version 2.0 (RANO 2.0) in 2023, response assessment in gliomas has evolved significantly, requiring both clinicians and radiologists to develop a comprehensive understanding of its modifications and implementation. This review first provides an overview of standard management and imaging schedule in glioma treatment. We then review the basic framework of RANO 2.0, inherited from previous response criteria, with particular emphasis on major modifications to this framework: the implementation of the Brain Tumor Imaging Protocol and the adoption of post-radiation scan as the baseline scan. Additionally, we analyze critical changes in response evaluation and interpretation, specifically focusing on the role of preliminary progressive disease assessment with confirmation scans, and the elimination of T2/FLAIR lesion measurements from enhancing tumor assessment. Through illustrative clinical cases, we demonstrate the practical application of these modifications and discuss the implementation of three distinct imaging-based categories: enhancing tumor, non-enhancing tumor, and tumors with both enhancing and non-enhancing components (in short, mixed tumor). This comprehensive narrative review provides clinicians with practical guidance for implementing RANO 2.0 in their clinical practice.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1557-1574"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of AI diagnostic systems for breast ultrasound: comparative analysis with radiologists and the effect of AI assistance.","authors":"Sayumi Tsuyuzaki, Tomoyuki Fujioka, Emi Yamaga, Leona Katsuta, Mio Mori, Yuka Yashima, Mayumi Hara, Arisa Sato, Iichiroh Onishi, Jitsuro Tsukada, Tomoyuki Aruga, Kazunori Kubota, Ukihide Tateishi","doi":"10.1007/s11604-025-01809-2","DOIUrl":"10.1007/s11604-025-01809-2","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study is to evaluate the diagnostic accuracy of an artificial intelligence (AI)-based Computer-Aided Diagnosis (CADx) system for breast ultrasound, compare its performance with radiologists, and assess the effect of AI-assisted diagnosis. This study aims to investigate the system's ability to differentiate between benign and malignant breast masses among Japanese patients.</p><p><strong>Materials and methods: </strong>This retrospective study included 171 breast mass ultrasound images (92 benign, 79 malignant). The AI system, BU-CAD™, provided Breast Imaging Reporting and Data System (BI-RADS) categorization, which was compared with the performance of three radiologists. Diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were analyzed. Radiologists' diagnostic performance with and without AI assistance was also compared, and their reading time was measured using a stopwatch.</p><p><strong>Results: </strong>The AI system demonstrated a sensitivity of 91.1%, specificity of 92.4%, and an AUC of 0.948. It showed comparable diagnostic performance to Radiologist 1, with 10 years of experience in breast imaging (0.948 vs. 0.950; p = 0.893), and superior performance to Radiologist 2 (7 years of experience, 0.948 vs. 0.881; p = 0.015) and Radiologist 3 (3 years of experience, 0.948 vs. 0.832; p = 0.001). When comparing diagnostic performance with and without AI, the use of AI significantly improved the AUC for Radiologists 2 and 3 (p = 0.001 and 0.005, respectively). However, there was no significant difference for Radiologist 1 (p = 0.139). In terms of diagnosis time, the use of AI reduced the reading time for all radiologists. Although there was no significant difference in diagnostic performance between AI and Radiologist 1, the use of AI substantially decreased the diagnosis time for Radiologist 1 as well.</p><p><strong>Conclusion: </strong>The AI system significantly improved diagnostic efficiency and accuracy, particularly for junior radiologists, highlighting its potential clinical utility in breast ultrasound diagnostics.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1643-1651"},"PeriodicalIF":2.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}