Academic Radiology最新文献

{"title":"AI-driven and Traditional Radiomic Model for Predicting Muscle Invasion in Bladder Cancer via Multi-parametric Imaging: A Systematic Review and Meta-analysis.","authors":"Zhengyan Wang, Hongjin Shi, Qun Wang, Yu Huang, Mingyuan Feng, Lin Yu, Baonan Dong, Jianxiong Li, Xin Deng, Shi Fu, Guifu Zhang, Haifeng Wang","doi":"10.1016/j.acra.2025.08.035","DOIUrl":"https://doi.org/10.1016/j.acra.2025.08.035","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study systematically evaluates the diagnostic performance of artificial intelligence (AI)-driven and conventional radiomics models in detecting muscle-invasive bladder cancer (MIBC) through meta-analytical approaches. Furthermore, it investigates their potential synergistic value with the Vesical Imaging-Reporting and Data System (VI-RADS) and assesses clinical translation prospects.</p><p><strong>Methods: </strong>This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conducted a comprehensive systematic search of PubMed, Web of Science, Embase, and Cochrane Library databases up to May 13, 2025, and manually screened the references of included studies. The quality and risk of bias of the selected studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. We pooled the area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and their 95% confidence intervals (95% CI). Additionally, meta-regression and subgroup analyses were performed to identify potential sources of heterogeneity.</p><p><strong>Results: </strong>This meta-analysis incorporated 43 studies comprising 9624 patients. The majority of included studies demonstrated low risk of bias, with a mean RQS of 18.89. Pooled analysis yielded an AUC of 0.92 (95% CI: 0.89-0.94). The aggregate sensitivity and specificity were both 0.86 (95% CI: 0.84-0.87), with heterogeneity indices of I² = 43.58 and I² = 72.76, respectively. The PLR was 5.97 (95% CI: 5.28-6.75, I² = 64.04), while the NLR was 0.17 (95% CI: 0.15-0.19, I² = 37.68). The DOR reached 35.57 (95% CI: 29.76-42.51, I² = 99.92). Notably, all included studies exhibited significant heterogeneity (P < 0.1). Meta-regression and subgroup analyses identified several significant sources of heterogeneity, including: study center type (single-center vs. multi-center), sample size (<100 vs. ≥100 patients), dataset classification (training, validation, testing, or ungrouped), imaging modality (computed tomography [CT] vs. magnetic resonance imaging [MRI]), modeling algorithm (deep learning vs. machine learning vs. other), validation methodology (cross-validation vs. cohort validation), segmentation method (manual vs. [semi]automated), regional differences (China vs. other countries), and risk of bias (high vs. low vs. unclear).</p><p><strong>Conclusion: </strong>AI-driven and traditional radiomic models have exhibited robust diagnostic performance for MIBC. Nevertheless, substantial heterogeneity across studies necessitates validation through multinational, multicenter prospective cohort studies to establish external validity.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Multiomics Model for Risk Stratification of Postoperative Distant Metastasis in Colorectal Cancer.","authors":"Xiuzhen Yao, Xiaoyu Han, Danjiang Huang, Yongfei Zheng, Shuitang Deng, Xiaoxiang Ning, Li Yuan, Weiqun Ao","doi":"10.1016/j.acra.2025.08.040","DOIUrl":"https://doi.org/10.1016/j.acra.2025.08.040","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To develop deep learning-based multiomics models for predicting postoperative distant metastasis (DM) and evaluating survival prognosis in colorectal cancer (CRC) patients.</p><p><strong>Materials and methods: </strong>This retrospective study included 521 CRC patients who underwent curative surgery at two centers. Preoperative CT and postoperative hematoxylin-eosin (HE) stained slides were collected. A total of 381 patients from Center 1 were split (7:3) into training and internal validation sets; 140 patients from Center 2 formed the independent external validation set. Patients were grouped based on DM status during follow-up. Radiological and pathological models were constructed using independent imaging and pathological predictors. Deep features were extracted with a ResNet-101 backbone to build deep learning radiomics (DLRS) and deep learning pathomics (DLPS) models. Two integrated models were developed: Nomogram 1 (radiological + DLRS) and Nomogram 2 (pathological + DLPS).</p><p><strong>Results: </strong>CT- reported T (cT) stage (OR=2.00, P=0.006) and CT-reported N (cN) stage (OR=1.63, P=0.023) were identified as independent radiologic predictors for building the radiological model; pN stage (OR=1.91, P=0.003) and perineural invasion (OR=2.07, P=0.030) were identified as pathological predictors for building the pathological model. DLRS and DLPS incorporated 28 and 30 deep features, respectively. In the training set, area under the curve (AUC) for radiological, pathological, DLRS, DLPS, Nomogram 1, and Nomogram 2 models were 0.657, 0.687, 0.931, 0.914, 0.938, and 0.930. DeLong's test showed DLRS, DLPS, and both nomograms significantly outperformed conventional models (P<.05). Kaplan-Meier analysis confirmed effective 3-year disease-free survival (DFS) stratification by the nomograms.</p><p><strong>Conclusion: </strong>Deep learning-based multiomics models provided high accuracy for postoperative DM prediction. Nomogram models enabled reliable DFS risk stratification in CRC patients.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Providing Radiology Services at Another Institution: Cultural and Contractual Issues.","authors":"Jeffrey W Dunkle, Kevin L Smith, Richard B Gunderman","doi":"10.1016/j.acra.2025.08.023","DOIUrl":"https://doi.org/10.1016/j.acra.2025.08.023","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ionizing Radiation Exposure: What are the Risks Today?","authors":"Saumya S Gurbani, Ichiro Ikuta, Mina S Makary, Muhammad Akram, Dyutika Kantamneni, Shahrzad Azizaddini, Dallin Judd, Joseph Sotelo, Erica M Lanser, Alison Chetlan, Chloe Chhor","doi":"10.1016/j.acra.2025.08.030","DOIUrl":"https://doi.org/10.1016/j.acra.2025.08.030","url":null,"abstract":"<p><p>Medical imaging plays an increasingly central role in the diagnostic workup and management of patients. As imaging technologies evolve, the radiology community faces the challenge of balancing the diagnostic benefits of medical imaging with the potential risks associated with ionizing radiation. As part of the Association of Academic Radiology's 2025 Radiology Research Alliance task force, we present an updated review of the current literature on the risks associated with ionizing radiation in medical imaging, discuss technological advances focused on dose reduction, and present best practices for safety protocols.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can States Authorize Autonomous Artificial Intelligence Algorithms to Combat Radiology Workforce Crises? A Legal Analysis","authors":"Sagar Kulkarni ,&nbsp;Gyan Moorthy ,&nbsp;Atul Agarwal","doi":"10.1016/j.acra.2024.07.034","DOIUrl":"10.1016/j.acra.2024.07.034","url":null,"abstract":"<div><div>Many states are experiencing a shortage of radiologists. An autonomous artificial intelligence algorithm could assist in improving access to diagnostic<span> care, however, few have been approved by the Food and Drug Administration. In this article, we explore whether states can authorize the use of artificial intelligence within their jurisdiction, drawing on the United States Constitution and tort law cases. We also explore questions on liability, data protection and the current status of artificial intelligence legislation at the state level.</span></div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 ","pages":"Pages S83-S89"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Laws That Have Shaped Radiology: Part I","authors":"Gyan Moorthy MS ,&nbsp;Leah Bush BA ,&nbsp;Anne Zimmerman JD, MS ,&nbsp;Saurabh Jha MBBS, MRCS, MS","doi":"10.1016/j.acra.2024.08.054","DOIUrl":"10.1016/j.acra.2024.08.054","url":null,"abstract":"<div><div><span><span>Radiology<span> began as a translation of quantum physics to clinical medicine. Advances in computing and engineering enabled the differentiation of the field into </span></span>diagnostic radiology<span><span>, interventional radiology, and </span>radiation oncology as practical responses to rapidly proliferating medical knowledge. Radiology has itself transformed modern medicine, helping clinicians identify, track, and intervene on multiple once deadly diseases. It is practiced in academic departments and hospital based, outpatient center based, or fully remote private groups of varying sizes, often with direct physicist support to optimize the use of complicated equipment. Importantly, radiology was guided to its current form not just by scientific advances, but by the interplay of cultural and governmental forces, as well as hard lessons, the results of constantly shifting balances of competing interests as follows: insurance, pharmaceutical, </span></span>medical device<span>, hospital, physician, physician extender, and patient. The purpose of this review is to describe the historical legal landscape of diagnostic radiology in the context of ethics, public health initiatives, and patient protections. For clarity, the review is divided into two parts.</span></div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 ","pages":"Pages S34-S42"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Malpractice in Pennsylvania — Then and Now","authors":"Andrew Wilmot MD","doi":"10.1016/j.acra.2025.06.060","DOIUrl":"10.1016/j.acra.2025.06.060","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 ","pages":"Pages S9-S10"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Story of Electronic Fetal Monitoring – A Cautionary Tale for Radiologists","authors":"Saurabh Jha MBBS MRCS MS","doi":"10.1016/j.acra.2025.08.021","DOIUrl":"10.1016/j.acra.2025.08.021","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To understand how an imperfect surrogate can lead to defensive medicine.</div></div><div><h3>Materials and Methods</h3><div>Historical analysis was performed on electronic fetal monitoring.</div></div><div><h3>Results</h3><div>Electronic Fetal Monitoring is an example of a flawed surrogate.</div></div><div><h3>Conclusion</h3><div>Radiologists should disclose the limitations of using imperfect information.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 ","pages":"Pages S159-S162"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunistic Quantitative Analysis of Bone Mineral Density and Vertebral Height in Cardiac Transplant Recipients Using Routine Chest CT Scans.","authors":"Seyed Ali Nabipoorashrafi, Arash Azhideh, Farbod Khosravi, Peyman Mirghaderi, Shin Lin, Sanaz Asadian, Arash Bedayat, Majid Chalian, Hamid Chalian","doi":"10.1016/j.acra.2025.08.024","DOIUrl":"https://doi.org/10.1016/j.acra.2025.08.024","url":null,"abstract":"<p><strong>Background: </strong>Osteoporosis is a common complication in cardiac transplant recipients. While dual-energy X-ray absorptiometry (DEXA) is the standard method for assessing bone mineral density (BMD), it has limitations. This study aimed to evaluate the potential of using routine non-contrast chest CTs in cardiac transplant patients to assess BMD and vertebral body height.</p><p><strong>Methods: </strong>98 cardiac transplant patients who had undergone CTs before and after transplantation, along with DEXA scans, were included in this observational study. The CTs were used to measure vertebral bone density and height at thoracic vertebral levels. CT-based bone density before and after transplantation was compared and correlated with DEXA scan bone density.</p><p><strong>Results: </strong>The findings revealed a significant decrease in vertebral bone density in the middle thoracic region (T5-T8) from 175 to 163 Hounsfield Units (HU) and in the lower thoracic region (T9-T12) from 163 to 146 HU after transplantation (P<.05). A strong correlation was observed between the bone density values derived from CTs and those from DEXA scans (P<.05). Furthermore, vertebral body height significantly changed at specific levels (T3, T6, T11, and T12) (P<.05). The anterior and middle portions of vertebral body showed a significant reduction in median height of 0.02 cm and 0.03 cm, respectively (P<.05), while changes in the posterior portion were not significant (P>.05).</p><p><strong>Conclusion: </strong>Routine non-contrast chest CTs commonly performed for cardiac transplant patients, can effectively assess bone density, presenting a potential alternative to DEXA scans in this patient population.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Litigation and Defensive Medicine: In Conversation with Dr Richard B. Gunderman","authors":"Sagar Kulkarni ,&nbsp;Atul Agarwal","doi":"10.1016/j.acra.2024.12.069","DOIUrl":"10.1016/j.acra.2024.12.069","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 ","pages":"Pages S104-S105"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}