Academic Radiology最新文献

{"title":"Evaluating the Diagnostic Accuracy of Artificial Intelligence in Spondylolisthesis Detection: A Systematic Review and Meta-analysis","authors":"Mohammad-Taha Pahlevan-Fallahy MD, MPH ,&nbsp;Amir-Mohammad Asgari MD ,&nbsp;Alireza Soltani Khaboushan MD ,&nbsp;Majid Chalian ,&nbsp;Farhad Shaker MD ,&nbsp;Parnian Yari MD ,&nbsp;Sara Haseli","doi":"10.1016/j.acra.2025.11.002","DOIUrl":"10.1016/j.acra.2025.11.002","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Spondylolisthesis, a vertebral displacement condition affecting 5–26% of adults, poses a significant health risk to the population. Artificial Intelligence (AI), has emerged as a tool for enhancing diagnostic accuracy. However, the heterogeneity in model performances requires a synthesis of existing evidence.</div></div><div><h3>Materials and Methods</h3><div>This study evaluated the diagnostic accuracy of AI models for the detection of spondylolisthesis across multiple imaging modalities. Following PRISMA PubMed, Scopus, Embase, Web of Science, including 24 studies (21 for meta-analysis) with 8029 observations. Inclusion criteria focused on original studies using standalone deep learning (DL) models with reported diagnostic metrics. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies-2, and statistical analysis employed random-effects meta-analysis.</div></div><div><h3>Results</h3><div>AI models demonstrated high diagnostic performance, with a pooled sensitivity of 94.7% (95% CI: 92.6–96.2%) and specificity of 97.1% (95% CI: 95.0–98.4%). The area under the curve (AUC) was 0.979, indicating robust discriminative ability. MRI-based models slightly outperformed radiography models (sensitivity: 95.71% vs. 94.95%; specificity: 98.38% vs. 96.80%), though differences were nonsignificant (<em>p</em> = 0.651). Classification models significantly surpassed detection-focused models (<em>p</em> = 0.026), while biomechanical feature-based models and DL image processing models showed comparable performance (<em>p</em> = 0.264). Notably, models like FAR networks and YOLOv8 achieved high accuracy (89–98%) in grading and localization tasks.</div></div><div><h3>Conclusions</h3><div>AI models show considerable diagnostic accuracy for spondylolisthesis, underscoring their potential as clinical adjunctive tools. However, considerable heterogeneity highlights the need for standardized studies. These findings advocate for integrating AI into diagnostic workflows, particularly in resource-limited settings, while urging further research to ensure real-world applicability.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 1034-1048"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145607077","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":"Enhancing Pediatric Fracture Detection: Multicenter Evaluation of a Deep Learning AI Model and Its Impact on Radiologist Performance","authors":"Sean Raj MD, MBA, Chief Medical Officer and Chief Innovation Officer,&nbsp;Barry Sadegi MD,&nbsp;John Simon MD","doi":"10.1016/j.acra.2025.11.022","DOIUrl":"10.1016/j.acra.2025.11.022","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study investigates the efficacy of a deep learning-based artificial intelligence (AI) model in detecting pediatric fractures on musculoskeletal (MSK) radiographs and assesses the impact of AI-assistance on the performance of radiologists.</div></div><div><h3>Materials and Methods</h3><div>In Phase 1, the performance of the AI model was evaluated on 3016 MSK pediatric radiographs from 4 imaging centers in the US. Ground truth was established by consensus of pediatric radiologists. Phase 2 was a retrospective multi-reader, multi-center (MRMC) study using 189 cases. Twenty readers participated in two separate reading sessions evaluating for fracture, with and without AI assistance, with a one-month washout period.</div></div><div><h3>Results</h3><div>The AI model achieved a high standalone performance with accuracy (0.94), sensitivity (0.96), and specificity (0.86). Subgroup analysis revealed that the model maintained high performance across study types and confounders, including age (Se&gt;0.94), gender (Se&gt;0.96), anatomical region (Se&gt;0.93), and fracture types (Se&gt;0.93). With AI assistance, reader accuracy increased significantly from 0.93 to 0.96 (p &lt; 0.05), sensitivity significantly improved from 0.86 to 0.93 (p &lt; 0.05), and specificity improved from 0.94 to 0.95. The average reading time per exam was shortened by 26.1% with AI assistance.</div></div><div><h3>Conclusion</h3><div>The AI model's high accuracy in detecting pediatric fractures underscores its significant clinical utility. The integration of this tool enhanced overall radiologist performance and boosted the diagnostic confidence among non-specialist readers.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 1121-1129"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649731","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":"On Clinical Interpretation of Tumor-infiltrating Lymphocytes in Breast Cancer Prediction Models and Future Studies","authors":"Deniz Esin Tekcan Sanli MD, MSc ,&nbsp;Ahmet Necati Sanli MD, MSc","doi":"10.1016/j.acra.2025.11.031","DOIUrl":"10.1016/j.acra.2025.11.031","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Page 918"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670756","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":"From Imaging to Intervention: A Multicenter-Validated Radiomics Pipeline for Guiding Femoral Neck Fracture Surgical Management","authors":"Lin Mu ,&nbsp;Yao Liu ,&nbsp;Yunming Xie ,&nbsp;Haoyu Liu ,&nbsp;Ke Liu ,&nbsp;Zheng Miao ,&nbsp;Han Xue ,&nbsp;Mingyang Li ,&nbsp;Dong Dong ,&nbsp;Huimao Zhang","doi":"10.1016/j.acra.2025.11.024","DOIUrl":"10.1016/j.acra.2025.11.024","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>To develop and evaluate a femoral neck fracture (FNF) pipeline model for diagnosing fracture stability and aiding surgical decision-making.</div></div><div><h3>Materials and Methods</h3><div>Patients with confirmed FNFs were enrolled in the study. An automatic segmentation algorithm was employed to initially delineate fracture-displaced regions revealed using CT images, with subsequent manual refinement. A logistic-regression model was first trained on selected radiomic features to generate a Rad-score for fracture-stability classification. The Rad_score was then fed into a downstream model to guide surgical decision-making. The internal and external validation with multi-center data were used to assess the generalizability of the pipeline model.</div></div><div><h3>Results</h3><div>The internal dataset for fracture stability and surgical decision-making included 624 and 410 patients, respectively. The corresponding external test sets, included 364 and 186 patients enrolled from 32 centers. The radiomics model for FNF stability exhibited robust performance, achieving an area under the curve (AUC) of 0.905 (95% confidence interval [CI]: 0.853–0.944) and 0.821 (95% CI: 0.778–0.859) for the internal and external test sets, respectively. The AUCs for the surgical decision-making models were 0.881 (95% CI: 0.810–0.932) and 0.820 (95% CI: 0.757–0.873) for the internal and external test sets, respectively.</div></div><div><h3>Conclusion</h3><div>The radiomics pipeline model exhibited robust performance in classifying fracture stability and aiding surgical decision-making in the test sets across 33 centers. Our model incorporates explainable artificial intelligence in fracture quantification analysis, supporting doctors in making objective clinical decisions.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 1049-1059"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662641","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":"Generalizable Deep Learning for Prostate Cancer Risk Stratification: Multicenter Study Integrating 18F-PSMA-1007 PET/CT and mpMRI","authors":"Cunke Miao ,&nbsp;Houzhang Sun ,&nbsp;Fei Yao ,&nbsp;Tianle Hong ,&nbsp;Zedong Ren ,&nbsp;Yuandi Zhuang ,&nbsp;Qi Lin ,&nbsp;Shuying Bian ,&nbsp;Yunjun Yang ,&nbsp;Yezhi Lin","doi":"10.1016/j.acra.2025.12.050","DOIUrl":"10.1016/j.acra.2025.12.050","url":null,"abstract":"<div><h3>Background</h3><div>Prostate cancer is the second most common cancer in men, with rising mortality rates necessitating precise risk stratification. High-invasive biological features—specifically International Society of Urological Pathology (ISUP) grade, extracapsular extension (EPE), and positive surgical margins (PSM)—are critical for guiding treatment but are difficult to detect due to tumor heterogeneity. Current imaging modalities, including 18F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), have limitations in fully capturing these features. This study aims to develop a few-shot deep learning model (CL-MGNET) that integrates multimodal imaging and clinical data to predict high-risk biological features, optimizing performance even with limited training data.</div></div><div><h3>Materials and Methods</h3><div>This retrospective, multicenter study analyzed data from 377 patients: 341 from a primary medical center (Center A) and 36 from an independent external validation cohort (Center B). The study utilized multimodal inputs (PET/CT, mpMRI) and clinical variables to predict ISUP grade, EPE, and PSM. A specialized few-shot deep learning network, CL-MGNET, was designed to fuse these data sources. The model was trained using a restricted subset of 30 patients and subsequently evaluated on both internal and external test sets to assess generalizability across different centers.</div></div><div><h3>Results</h3><div>CL-MGNET demonstrated excellent performance in predicting high-invasive biological features (defined as the presence of at least one high-risk feature: ISUP ≥ 3, EPE, or PSM), achieving an internal test AUC of 0.877 and an external validation AUC of 0.872, which significantly outperformed the clinical model with an AUC of 0.792. The model surpassed both single-modality models (PET/CT, mpMRI) and the clinical model. Furthermore, CL-MGNET exhibited strong generalization capability, effectively predicting various high-risk biological features. When clinical variables were integrated, the model's performance improved significantly, exceeding traditional methods.</div></div><div><h3>Conclusion</h3><div>The CL-MGNET model, leveraging multimodal imaging data and clinical variables with a few-shot learning approach, successfully predicts high-invasive biological features of prostate cancer with high accuracy, even with limited data. The model's performance across different biological features and medical centers shows its robust generalizability. This method holds great promise for improving prostate cancer diagnosis and risk prediction in data-limited environments.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 1107-1120"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994724","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 ,&nbsp;Xiaoyu Han ,&nbsp;Danjiang Huang ,&nbsp;Yongfei Zheng ,&nbsp;Shuitang Deng ,&nbsp;Xiaoxiang Ning ,&nbsp;Li Yuan ,&nbsp;Weiqun Ao","doi":"10.1016/j.acra.2025.08.040","DOIUrl":"10.1016/j.acra.2025.08.040","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To develop deep learning-based multiomics models for predicting postoperative distant metastasis (DM) and evaluating survival prognosis in colorectal cancer (CRC) patients.</div></div><div><h3>Materials and Methods</h3><div>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).</div></div><div><h3>Results</h3><div>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&lt;.05). Kaplan–Meier analysis confirmed effective 3-year disease-free survival (DFS) stratification by the nomograms.</div></div><div><h3>Conclusion</h3><div>Deep learning-based multiomics models provided high accuracy for postoperative DM prediction. Nomogram models enabled reliable DFS risk stratification in CRC patients.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 858-871"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","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":"Leading with C.A.R.E: A Framework to Foster Belonging and Well-Being in Radiology","authors":"Lily M. Belfi M.D. FACR ,&nbsp;Sarah Averill M.D. ,&nbsp;Constantine Burgen M.D. ,&nbsp;Reni Butler M.D. ,&nbsp;Michele Retrouvey M.D. ,&nbsp;Lori A. Deitte M.D. FACR FAUR","doi":"10.1016/j.acra.2025.10.046","DOIUrl":"10.1016/j.acra.2025.10.046","url":null,"abstract":"<div><div>The field of radiology is experiencing profound shifts driven by technological advancements, evolving workplace structures, and changing generational expectations, contributing to increased rates of burnout, professional isolation, and diminished purpose among radiologists. To address these challenges and promote workforce sustainability, we propose the C.A.R.E. framework—encompassing Community, Advocacy, Recognition, and Empowerment—as a comprehensive, human-centered model to foster belonging and well-being in radiology. This framework emphasizes intentional strategies to build meaningful professional relationships in hybrid and remote environments; offers pathways to engage in advocacy at personal, interpersonal, and systemic levels; underscores the importance of recognition and appreciation in cultivating engagement and combating burnout; and highlights mentorship, coaching, and sponsorship as pivotal tools for professional and personal growth. By implementing the C.A.R.E. framework, radiology departments and organizations can create inclusive, supportive environments that enhance individual fulfillment, professional resilience, and organizational success. Future initiatives should focus on operationalizing and evaluating these practices to ensure sustained improvements in workforce well-being and patient care outcomes.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 718-725"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558193","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 Differentiation of DCIS and IDC from Mammographic Microcalcifications","authors":"Deniz Esin Tekcan Sanli MD ,&nbsp;Ahmet Necati Sanli","doi":"10.1016/j.acra.2025.12.002","DOIUrl":"10.1016/j.acra.2025.12.002","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 919-920"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821968","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":"Pixel-level Radiomics and Deep Learning for Predicting Ki-67 Expression in Breast Cancer Based on Dual-modal Ultrasound Images","authors":"Wei Wei ,&nbsp;Fei Xia ,&nbsp;Di Zhang ,&nbsp;Wang Zhou ,&nbsp;Xinjin Wang ,&nbsp;Yu Gao ,&nbsp;Wenwu Lu ,&nbsp;Huijun Feng ,&nbsp;Chaoxue Zhang","doi":"10.1016/j.acra.2025.12.047","DOIUrl":"10.1016/j.acra.2025.12.047","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study aimed to develop a deep learning model using a novel pixel-level radiomics approach based on two-dimensional (2D) and strain elastography (SE) ultrasound images to predict Ki-67 expression in breast cancer (BC).</div></div><div><h3>Methods</h3><div>This multicenter study included 1031 BC patients, who were divided into training (n = 616), internal validation (n = 265), and external test (n = 150) cohorts. An additional 63 patients were prospectively enrolled for further validation. The deep learning model, termed Vision-Mamba, predicts Ki67 expression by integrating ultrasound (2D and SE) images with pixel-level radiomics feature maps (RFMs). A combined model was subsequently constructed by incorporating independent clinical predictors. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were applied to enhance interpretability.</div></div><div><h3>Results</h3><div>We developed a Vision-Mamba-US-RFMs-Clinical (V-MURC) model that integrates ultrasound images, RFMs, and clinical data for accurate prediction of Ki-67 expression in BC. The area under the ROC curve (AUC) values for the internal validation, external test, and prospective validation cohorts were 0.954 (95% CI, 0.929 - 0.975), 0.941 (95% CI, 0.903 - 0.975), and 0.945 (95% CI, 0.883 - 0.989), respectively, demonstrating excellent discrimination and calibration. Compared with individual models, the V-MURC model achieved significantly superior performance across all datasets (Delong test, <em>P</em> &lt; 0.05). Calibration curves and DCA further supported its clinical applicability. SHAP analysis provided visual interpretability of the model's decision-making process.</div></div><div><h3>Conclusion</h3><div>The V-MURC model based on pixel-level RFMs can accurately predict Ki-67 expression in BC and may serve as a valuable tool for individualized treatment decision-making in clinical practice.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 900-917"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145991799","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":"Automated Cardiac MRI Planning: From Localizers to Cine Images","authors":"Soheil Kooraki MD,&nbsp;Arash Bedayat MD","doi":"10.1016/j.acra.2026.01.026","DOIUrl":"10.1016/j.acra.2026.01.026","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 3","pages":"Pages 922-923"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357005","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}