Academic Radiology最新文献

{"title":"Objective Structured Clinical Examinations in Radiology and Medical Imaging Education: Educational Effectiveness, Validity, and Implementation-A Systematic Review.","authors":"Yushi Jia, Lusi Liu","doi":"10.1016/j.acra.2026.09.044","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.044","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To synthesize evidence on objective structured clinical examinations (OSCEs) in radiology and medical imaging education, focusing on educational outcomes, validity evidence, reliability, scoring, feasibility, and implementation consequences.</p><p><strong>Materials and methods: </strong>PubMed/MEDLINE, ERIC, Embase, and Web of Science were searched from inception to July 20, 2026. Eligibility was limited to radiology, radiography, nuclear medicine, sonography, and medical student imaging curricula; clinician bedside POCUS courses outside radiology or medical imaging education were excluded. Educational-effect studies were appraised with MERSQI. Measurement evidence was appraised across seven validity and implementation domains. Meta-analysis was considered only for sufficiently comparable independent studies.</p><p><strong>Results: </strong>Forty-three reports met the narrowed scope: 22 measurement/implementation reports and 21 educational-effect reports. Educational studies comprised six randomized studies, nine nonrandomized comparisons, one pre-post study, and five post-course or implementation studies. Seven reports favored the intervention, one met a noninferiority criterion, one was mixed, six were uncertain, and six were not estimable as comparative effects. MERSQI scores ranged from 10.5 to 14.0. Feasibility was documented in all measurement reports, but internal-structure or reliability evidence was absent in ten, and consequence evidence was strong in only one.</p><p><strong>Conclusion: </strong>Imaging-focused OSCEs are feasible and useful for local assessment in radiology and medical imaging education, but evidence remains heterogeneous and incompletely supported by reliability, response-process, and consequence data.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185853","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":"Radhawk: An AI-driven Recommender for Pediatric Radiology Education.","authors":"Julian Lopez-Rippe, Manasa Reddy, Michael Welsh, Jennifer Vaughn, Raisa Amiruddin, Avin Khera, Ami Gokli, Michael Francavilla, Janet R Reid","doi":"10.1016/j.acra.2026.09.067","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.067","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The unprecedented growth of technology and unfiltered information has led to cognitive overload and educational gaps in radiology. Radiologists and trainees often require \"just-in-time\" educational content while interpreting unfamiliar or infrequently encountered examinations, yet identifying reliable and appropriately targeted resources within the time constraints of the reading room can be challenging. This need is particularly relevant in pediatric radiology, where normal imaging appearances vary with age and development. Radhawk was developed as a scalable, artificial intelligence-driven intelligent tutoring system that delivers personalized, real-time educational resources to learners and radiologists, creating a precision curriculum tailored to each user's needs and clinical context.</p><p><strong>Materials and methods: </strong>Radhawk integrates with existing picture archiving and communication systems and a custom learning management system (RADiology's Intelligent Adaptive Learning). It utilizes natural language processing and machine learning algorithms to analyze imaging study information and provides results based on user preferences. The system's scalable architecture is built on Amazon Web Services, ensuring adaptability across multiple hospital systems.</p><p><strong>Results: </strong>Radhawk successfully provides tailored recommendations for educational materials, including interactive courses, anatomical references, and case studies. Key features include: (1) Generation of personalized curricula based on learner performance and needs, (2) Presentation of age and gender-matched \"normal\" images for comparison, (3) Creation of user-specific heat maps to identify strengths and areas for improvement, and (4) Incorporation of expert subspecialist preferences to inform user choices.</p><p><strong>Conclusion: </strong>Radhawk replaces generic web searches with a knowledge-focused approach, providing instant access to high-quality, relevant resources. This precision education solution has the potential to improve diagnostic accuracy, enhance learning experiences, and address the shortage of specialized pediatric radiologists. Future plans include external testing across multiple pediatric radiology departments to validate Radhawk's scalability, acceptance, and value in real-world settings.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149166880","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":"Added Value of CT Bone Reading Software in the Detection of Subtle Bone Metastases From Breast Cancer: Comparison with Standard Thorax CT.","authors":"Derya Karabulut, Aykut Alkan","doi":"10.1016/j.acra.2026.09.046","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.046","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>The aim of this study is to evaluate the additional diagnostic value of CT Bone Reading software in detecting rib metastases on thoracic computed tomography in patients with breast cancer and to examine its effectiveness on lesion detection, evaluation time, and inter-reader agreement.</p><p><strong>Materials and methods: </strong>This retrospective study included 110 patients with breast cancer and bone metastases who underwent both thoracic computed tomography (CT) and positron emission tomography-CT scans between January 2022 and January 2026. For lesion-level assessment, suspected rib lesions were also evaluated using available longitudinal follow-up imaging. Two radiologists independently evaluated conventional CT and CT Bone Reading images in separate reading sessions at least 2 weeks apart. Inter-reader agreement was assessed using the intraclass correlation coefficient (ICC) and Cohen's κ statistic.</p><p><strong>Results: </strong>In lesion-based analysis with conventional CT, Reader 1 detected 510 lesions (81.6%) and Reader 2 detected 577 lesions (92.3%), while CT Bone Reading allowed for the detection of an additional 115 (22.5%) and 48 (8.3%) lesions for Readers 1 and 2, respectively. The additionally detected lesions demonstrated a non-uniform distribution, with a marked predominance in the mid-rib region (levels 5th-8th). CT Bone Reading-assisted evaluation enabled the detection of significantly smaller lesions compared to routine CT for both readers (Reader 1: 8.0 mm vs 5.0 mm; Reader 2: 7.0 mm vs 5.0 mm; P < 0.001 for both). Evaluation time was significantly decreased from 129.22 ± 27.22 s to 64.94 ± 17.16 s for Reader 1 and from 122.99 ± 26.04 s to 64.44 ± 18.71 s for Reader 2 (P < 0.001 for both). Inter-reader agreement was excellent for measurements obtained by CT Bone Reading-derived evaluation (ICC ≥0.965; κ ≥0.947).</p><p><strong>Conclusion: </strong>CT Bone Reading software increases the detection rates of rib lesions, reduces interpretation times, and demonstrates excellent inter-reader reproducibility compared to conventional CT assessment, with less experienced readers achieving the highest incremental throughput. By providing a comprehensive and continuous view of the ribs, this technique has the potential to improve diagnostic performance without additional radiation exposure, particularly in anatomically complex regions such as the mid ribs. Ultimately, this approach can enhance workflow efficiency during routine clinical practices.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169557","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 Response Assessment to Biological Insight: PET/CT and Artificial Intelligence in Metastatic Breast Cancer.","authors":"Yi Liang, Kun Wang","doi":"10.1016/j.acra.2026.09.045","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.045","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169317","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":"Utility of OpenEvidence and ChatGPT in Selecting Neuroimaging Based on Appropriateness Criteria.","authors":"Siddhant Kumarapuram, Medha Satti, Shirleen Xu, Jay Kavia, Ashwin Bhaskar, Aafreen Azmi, Alberto F Goldszal, Andrew Soliman, Srihari Sundararajan, Gaurav Gupta, Sudipta Roychowdhury","doi":"10.1016/j.acra.2026.09.049","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.049","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Large Language models (LLMs) are novel tools with promising applications in imaging selection for clinical scenarios, but information is limited on the accuracy of an LLM trained on a medically specific dataset compared to a generalized dataset. In this analysis, we assessed the accuracy of the imaging recommendations by LLMs ChatGPT-3.5, ChatGPT-5.0, and OpenEvidence for neuroradiology, using the American College of Radiology (ACR) Appropriateness Criteria as the reference standard.</p><p><strong>Materials and methods: </strong>Prompts from the ACR Appropriateness Criteria priority clinical areas within neurology were used to build open-ended (OE) or select-all-that-apply (SATA) questions. Each question was posed three times, and the responses were scored against ACR guidelines. The performance of each LLM on OE and SATA was compared, and then was further analyzed by acuity. Mean accuracy scores were calculated by averaging performance across OE and SATA questions for each model. Analysis of variance was then conducted to assess differences in performance among the models.</p><p><strong>Results: </strong>A total of 78 prompts were assessed in 11 priority clinical areas. These areas included cerebrovascular conditions, neurological pain, and spine pathologies. Amongst SATA, Open Evidence performed the best (79.5%) compared to GPT5 (71.2%) and GPT3.5 (44.4%). For the OE prompts, Open Evidence was more accurate (86.3%) than GPT3.5 (69.2%) but not GPT5 (80.6%). Amongst the models, no differences were found based on acuity.</p><p><strong>Conclusion: </strong>Overall, OpenEvidence performed the best for both OE and SATA prompts. ChatGPT-5 performed better than ChatGPT3.5 for SATA prompts. LLM performance was unaffected by the acuity of the case.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171974","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":"Hyperintense Vessel Sign on High-Resolution Vessel Wall MRI and Perfusion Decompensation in Intracranial Atherosclerotic Disease: Burden Versus Proximal Extent.","authors":"Feng Ouyang, Qin Wu, Jie Liu, Shasha Lai, Lan Luo, Wenchao Xiong, Yixin Liu, Lu Sun, Laisheng Pan, Fuqing Zhou, Xianjun Zeng","doi":"10.1016/j.acra.2026.09.052","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.052","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To investigate the relationship between hyperintense vessel sign (HVS) on high-resolution magnetic resonance vessel wall imaging (HR-VWI) and cerebral perfusion decompensation in patients with intracranial atherosclerotic stenosis or occlusion.</p><p><strong>Materials and methods: </strong>This retrospective cohort included 128 patients with unilateral intracranial atherosclerotic stenosis or occlusion undergoing HR-VWI and computed tomography perfusion. HVS was scored by vessel count and classified by extent (type 0, none; type 1, M3-M4; type 2, M1-M2); local collaterals were assessed on time-of-flight magnetic resonance angiography. Prespecified Firth-penalized regression, bootstrap-validated threshold modeling, hierarchical likelihood-ratio tests, and collateral-phenotype analysis compared grade 2 versus grade 1.</p><p><strong>Results: </strong>The study cohort comprised 128 patients (92 males, 36 females), with a mean age of 55.08 ± 10.58 years. 85 patients were grade 1, 43 grade 2. HVS score was independently associated with decompensation (odds ratio [OR], 1.09, P = 0.04), with an exploratory cutoff at 8 (bootstrap 95% confidence interval [CI], 6-8; adjusted OR, 6.93, P < 0.01). Grade-2 proportions differed across type 0/1/2 (25.00%/22.67%/63.64%); type 2 exceeded type 1 (adjusted OR 8.06, P < 0.01). Classification, not the score, added incremental value (likelihood ratio P < 0.01 vs reverse P = 0.15). Type 1 with collateral present marked compensation (5.88%; negative predictive value 0.94); type 2 with collateral absent marked decompensation (66.67%; adjusted OR = 33.90, P < 0.01). Score ≥8 gave 0.65 sensitivity and 0.80 specificity; combined-model area under the curve was 0.83 (95% CI, 0.75-0.90; optimism-corrected 0.79).</p><p><strong>Conclusion: </strong>HVS score provides an exploratory screening cutoff, whereas proximal HVS extension into M1-M2 is strongly associated with decompensation; distal-limited HVS with local collateral formation marks compensation. Prospective multicenter validation is required before clinical implementation.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148324","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":"Retrospective Evaluation of the D-Dimer in Patients Undergoing CTPA for PE Evaluation.","authors":"Hannah E Johnson, Christian J Konopka, Kristin Delfino, Teresa Wilson, Scott Long","doi":"10.1016/j.acra.2026.09.014","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.014","url":null,"abstract":"<p><strong>Objective: </strong>Computed tomography pulmonary angiogram (CTPA) is the choice imaging modality for diagnosis of pulmonary embolism (PE). Studies have demonstrated more appropriate use of CTPA following implementation of the Wells or Revised Geneva scores and/or D-dimer level thresholds into clinical frameworks. During the study period, these measures were not required prior to ordering a CTPA at our institution. The aim of this study was to retrospectively evaluate the institutional utilization and ordering patterns preceding CTPA for evaluation of PE, with focus on the laboratory D-dimer test.</p><p><strong>Methods: </strong>This retrospective chart review included patients that underwent CTPA for suspected PE between January 1, 2022 and December 31, 2022. Data included clinical factors, D-dimer levels, CTPA results, and subspecialty of ordering providers. Statistical analyses were performed via SAS v9.4. P values < 0.05 were considered statistically significant.</p><p><strong>Results: </strong>A total of 660 patients were included in this study, with 54 positives for PE (8.2%). The D-dimer was ordered for 332 patients, and elevated ( ≥ 500 ng/mL) in 20 patients with, and 299 without PE. When analyzed using the institutional threshold as a binary (elevated vs non-elevated), D-dimer levels were not associated with PE on CTPA (p = 0.2099). However, when analyzed as a continuous variable using the standard and age-adjusted thresholds, greater values were associated with PE (p < 0.05). Patients with retrospective high-risk Wells or Revised Geneva scores were more likely to proceed directly to CTPA.</p><p><strong>Discussion: </strong>When analyzed as a continuous variable, elevated D-dimer levels were associated with PE. Our findings also suggest that the D-dimer is more widely used in the diagnostic workup in those at low risk of PE, likely in a \"rule out\" manner. Future work should evaluate D-dimer utilization, laboratory levels, and clinical probability scores of those with suspected PE in groups that did and did not undergo CTPA.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149478","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 Adaptation to Integration: Redesigning Academic Radiology for a Transformed Profession.","authors":"Diego Avila Lessa Garcia, Marilia Paes Fortes Diniz Ferreira","doi":"10.1016/j.acra.2026.09.047","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.047","url":null,"abstract":"<p><p>Over the past three decades, radiology has transformed more profoundly than perhaps any other medical specialty. Imaging volumes have multiplied, examinations have grown in complexity, and radiologists have moved from isolated interpreters of static images to central participants in multidisciplinary decision-making. Academic organizations responded to this transformation continuously-but largely through local, successful adaptations layered onto existing structures, rather than through periodic redesign of those structures themselves. This Perspective proposes that many of today's most visible challenges-burnout, workforce instability, fragile subspecialty coverage, and the progressive loss of protected academic time-are not independent problems. They are manifestations of a common underlying pattern, which this Perspective names explicitly as organizational accumulation outpacing organizational integration-decades of local adaptation layered onto existing structures without a matching cycle of structural redesign. Work relative value units, still indispensable for measuring reimbursable clinical activity, illustrate the pattern well: they remain valid for what they were built to measure, while increasingly capturing only a portion of what the modern academic radiologist actually does (1). None of the contemporary pressures facing academic radiology created this imbalance. They exposed it. For decades, an adaptive reserve absorbed the growing gap between professional and organizational evolution-but that reserve resided in people, not in institutions, and it is not unlimited. This Perspective does not propose a specific organizational remedy. It argues that naming this pattern is the necessary first step toward redesigning academic work for the profession radiology has already become. The scarcest resource in academic radiology today may no longer be technology, volume, or expertise. It may be protected intellectual time.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149130","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":"Multimodal Carotid Ultrasound-based Artificial Intelligence Model for Predicting Acute Ischemic Stroke Risk in Patients with Type 2 Diabetes Mellitus.","authors":"Yaning Sun, Bojian Feng, Yanming Zhang, Di Ou, Dangin Pan, Saisai Wang, Fengqin Shen, Xinchen Li, Dong Xu","doi":"10.1016/j.acra.2026.09.036","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.036","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Patients with type 2 diabetes mellitus (T2DM) are at high risk of acute ischemic stroke (AIS), yet accurate risk stratification remains challenging. This study aimed to develop and validate a multimodal model based on carotid ultrasound for improved AIS risk prediction.</p><p><strong>Materials and methods: </strong>In this multicenter retrospective study, a total of 480 patients with T2DM who underwent carotid ultrasound were recruited from two centers, with one center (n=394) used for model development and the other (n = 86) for independent external testing. A dual-scale deep learning framework based on a Swin Transformer was developed to capture both local plaque features and surrounding vascular context. Deep learning features were integrated with radiomic and clinical features to construct a multimodal model. Model performance was evaluated using the area under the curve (AUC), along with calibration and decision curve analysis.</p><p><strong>Results: </strong>The multimodal model achieved AUCs of 0.952 (95% CI: 0.910-0.984) in the internal cohort and 0.939 (95% CI: 0.838-0.996) in the external cohort, showing improved performance compared to single-modality models. In the reader study, AI-assisted interpretation improved diagnostic performance across all readers, with AUC increases ranging from 0.760 to 0.857 and from 0.868 to 0.929 for senior readers, and from 0.450 to 0.700 and from 0.548 to 0.659 for junior readers. Generalized estimating equation analyses showed significant improvements in diagnostic correctness among both senior and junior readers, with P values of 0.00235 and 0.000343, respectively.</p><p><strong>Conclusion: </strong>A multimodal framework integrating carotid ultrasound with clinical, radiomic, and deep learning features enables improved AIS risk stratification in patients with T2DM.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149531","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":"Asymmetric Complementarity: A Subtype-Specific Strategy via Visual Assessment of DCE-MRI and DWI for Evaluating NAT Efficacy in Breast Cancer.","authors":"Xiuyun Ge, Bin Zheng, Zhen Zhang, Jianle Liang, Wenjing Yuan, Jiarui Xie, Guanxun Cheng, Peng Sun, Rong Huang","doi":"10.1016/j.acra.2026.09.026","DOIUrl":"https://doi.org/10.1016/j.acra.2026.09.026","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To evaluate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI), based on clinical visual assessment for predicting pathological complete response (pCR) and measuring residual tumor size after neoadjuvant therapy (NAT) across breast cancer subtypes.</p><p><strong>Materials and methods: </strong>This retrospective study included 248 patients. Two radiologists, blinded to pathological results, visually assessed DCE-MRI and DWI (including ADC maps) for radiological complete response (rCR) and measured residual tumor size. Diagnostic performance (vs. pCR) and size agreement (intraclass correlation coefficient, ICC) were compared between sequences and across three molecular subtypes: HR+/HER2-, HER2+, and triple-negative breast cancer (TNBC). Bonferroni correction was applied.</p><p><strong>Results: </strong>DCE-MRI showed higher specificity, positive predictive value (PPV), and accuracy, DWI showed higher sensitivity (all P < 0.05). Subtype analysis: HER2+ exhibited a trend toward symmetric complementarity (DWI-high NPV, DCE-MRI-high PPV; sensitivity difference not significant after correction, adjusted P = 0.066); HR+/HER2- showed DCE-MRI dominance (DWI very low PPV/specificity); TNBC showed no significant differences(n = 30, preliminary). Interobserver κ: 0.77 (DCE-MRI), 0.85 (DWI). Interobserver ICC for size:0.96 (both). DCE-MRI demonstrated superior size concordance with pathology (ICC: 0.91 vs. 0.51, P < 0.001).</p><p><strong>Conclusion: </strong>DCE-MRI and DWI exhibit subtype-dependent asymmetric complementarity. For HER2+, both are complementary (DWI high NPV, DCE-MRI high PPV). For HR+/HER2-, DCE-MRI is the primary anchor (DWI limited). For TNBC, findings are preliminary (larger studies needed). DCE-MRI is more reliable for size measurement. This framework provides a molecular subtype-specific visual interpretation guide for clinical practice.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118248","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}