Radiology. Imaging cancer最新文献

{"title":"TROP2-targeted NIR-II Peptide Probe for Intraoperative Detection of Tumor Margins and Sentinel Nodes in Breast-conserving Surgery.","authors":"Happy Agarwal, Rohit Singh, Lacey R McNally","doi":"10.1148/rycan.269014","DOIUrl":"10.1148/rycan.269014","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e269014"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148369702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular-Structural MRI Mismatch for Preoperative Risk Stratification of Pediatric Neuroblastoma.","authors":"Junjie Wen, Wenqi Wang, Xuan Jia, Xiaohui Ma, Hongxi Zhang, Yi Zhang","doi":"10.1148/rycan.250455","DOIUrl":"10.1148/rycan.250455","url":null,"abstract":"<p><p>Purpose To establish molecular-structural mismatch indexes and explore their potential for the preoperative risk stratification of individuals with pediatric neuroblastoma. Materials and Methods In this prospective study, 102 pediatric individuals with suspected neuroblastoma were initially enrolled and underwent structural MRI and amide proton transfer (APT) MRI from April 2019 to November 2023. A four-pool Lorentzian fitting model was employed to minimize motion artifacts and extract the APT signal from confounding factors, generating APT maps and APT-weighted (APTw) images. Novel molecular-structural mismatch images were then synthesized using APT maps, APTw images, and conventional structural MRI. The mean tumor region of interest values on APT, APTw, and mismatch images were compared across risk groups using Wilcoxon rank sum tests; receiver operating characteristic analysis was used to assess the performance of these values in risk stratification, and the DeLong test was used to evaluate differences in the area under the curve (AUC). Results The final analysis included 71 participants (mean ± SD age at examination, 45.8 months ± 31.6; 52 male individuals), comprising 42 participants at high risk and 29 at non-high risk. The mean tumor region of interest values on APT, APTw, and mismatch images were greater in those at high risk than those not at high risk (<i>P</i> < .05). The mean APTw value had an AUC of 0.68 (95% CI: 0.54, 0.80), and the APT map achieved an AUC of 0.76 (95% CI: 0.64, 0.87). The molecular-structural mismatch images had numerically higher AUC values, at 0.84 (95% CI: 0.75, 0.94) for APTw-T2w mismatch and 0.83 (95% CI: 0.74, 0.93) for APT-T2w mismatch, respectively, but there was no evidence of a difference (<i>P</i> > .05). Conclusion Molecular-structural mismatch demonstrates potential as a noninvasive approach for differentiating neuroblastoma risk groups among children. <b>Keywords:</b> Pediatrics, MRI, Statistics, Molecular Imaging-Cancer, Abdomen/GI <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250455"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148163642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic MRI and Delta Histogram Analysis: A Step Forward in Noninvasive Meningioma Grading.","authors":"Alvand Hassankhani","doi":"10.1148/rycan.260325","DOIUrl":"10.1148/rycan.260325","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e260325"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Generalizability and Clinical Integration of CT-based Deep Learning for Ovarian Tumor Classification.","authors":"Weihao Cheng, Zekai Yu, Feiwei Qin","doi":"10.1148/rycan.260141","DOIUrl":"10.1148/rycan.260141","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e260141"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13438797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Training to Independent Practice in Radiology: Reflections and Early-Career Lessons.","authors":"Xiaoyang Liu, Jorge A Abreu Gomez, Brian J Burkett","doi":"10.1148/rycan.260090","DOIUrl":"10.1148/rycan.260090","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260090"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Diffusion-weighted Imaging in Eosinophilic Solid and Cystic Renal Cell Carcinoma.","authors":"Carl G Glessgen, Jean-Christophe Tille, Daniel Benamran, Harriet C Thoeny","doi":"10.1148/rycan.260031","DOIUrl":"10.1148/rycan.260031","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260031"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurolymphomatosis: A Comprehensive Review of Clinical and Imaging Features.","authors":"Mona Gad, Greg Pommier, Rebecca Choi, Sasicha Manupipatpong, Jacob Schick, Dhairya A Lakhani, Francis Deng, Majid Khan","doi":"10.1148/rycan.250801","DOIUrl":"10.1148/rycan.250801","url":null,"abstract":"<p><p>Neurolymphomatosis (NL) is a rare condition characterized by infiltration of the peripheral nervous system by malignant lymphocytes. It most commonly occurs in the setting of hematologic malignancy, particularly non-Hodgkin lymphoma, where neural involvement represents a distinct form of extranodal disease. It may also occur as a manifestation of primary central nervous system lymphoma. This review aims to summarize the clinical presentation, diagnostic challenges, and imaging characteristics of NL and to highlight the role of MRI and fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/CT in detection, diagnosis, and assessment of disease extent. Two clinical patterns are recognized: Primary NL manifests with neuropathy as the initial disease manifestation, with or without concomitant nodal or extranodal disease at the time of lymphoma diagnosis, whereas secondary NL occurs as a site of progression or relapse in patients with a prior history of lymphoma. NL demonstrates diverse clinical presentations, which often leads to misdiagnosis. Clinical red flags, particularly in patients presenting with polyneuropathy, include severe pain, asymmetric distribution, subacute onset, and rapid clinical progression. The brachial plexus, lumbosacral plexus, sciatic nerve, and trigeminal nerve are the most commonly involved sites. Although nerve biopsy remains the diagnostic reference standard, it carries a high risk of irreversible nerve damage. MR neurography and ¹⁸F-FDG PET/CT provide high diagnostic yield and facilitate early detection and diagnosis of NL, particularly when interpreted in the appropriate clinical context. In addition, they are essential for evaluating disease extent, guiding targeted biopsy when needed, and supporting clinical management and treatment monitoring. <b>Keywords:</b> PET/CT, Nervous-Peripheral, Lymphoma, MR Imaging, Neuro-Oncology, Neurolymphomatosis, Lymphoma, Peripheral Nervous System, Neuropathy, Nerve Biopsy, MRI, FDG/PET © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250801"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ONCO-RADS-guided Large Language Models for Extraction and Classification of Incidental Findings on Whole-Body Imaging Reports.","authors":"Mickael Tordjman, Murat Yuce, Zelong Liu, Anis Meribout, Ian Bolger, Amine Geahchan, Francisco Restrepo, Himanshu Joshi, Hao-Chih Lee, Timothy Deyer, Zahi Fayad, Sara Lewis, Giuseppe Petralia, Xueyan Mei, Bachir Taouli","doi":"10.1148/rycan.250484","DOIUrl":"10.1148/rycan.250484","url":null,"abstract":"<p><p>Purpose To evaluate large language model (LLM)-based strategy performance for extraction and classification of incidental findings from whole-body (WB) imaging reports, particularly strategies incorporating Oncologically Relevant Findings Reporting and Data System (ONCO-RADS). Materials and Methods In this retrospective bicenter study, authors included all WB MRI reports from January 2016 to December 2023 at a referral center (internal dataset). Two observers extracted all incidental findings, and patient records were used to confirm final diagnoses. First, authors evaluated ONCO-RADS performance and the reproducibility of its incidental finding classifications by six radiologists. Then, authors evaluated the accuracy of three LLM-based strategies: <i>(a)</i> a fine-tuned DeBERTa/medical named entity recognition (NER) model; <i>(b)</i> zero-shot LLMs (ChatGPT-o1 [OpenAI], Gemini-2.5-Pro [Google]); and <i>(c)</i> reference-guided prompting of these LLMs using ONCO-RADS. Authors then expanded these strategies to an external dataset of 605 reports with multiple imaging techniques (405 WB MRI; 100 fluorodeoxyglucose PET/CT; and 100 chest-abdomen-pelvis CT acquisitions) from January 2022 to January 2025. Results The internal dataset included 823 patients (mean age, 63.7 years ± 11.7 [SD]; 457 male patients) with 1488 WB MRI reports. The average interobserver reproducibility of ONCO-RADS incidental finding classifications was excellent (Cohen κ, 0.87). The per-report accuracies of ONCO-RADS-guided LLMs (95.6% [151 of 158] and 86.7% [137 of 158] for ChatGPT-o1 and Gemini-2.5-Pro, respectively) were higher than those of the medical NER (69.0% [109 of 158]) and zero-shot LLMs (57.0% [90 of 158] and 70.9% [112 of 158] for ChatGPT-o1 and Gemini-2.5-Pro, respectively) (<i>P</i> < .001). In the external test set (mean age, 60.6 years ± 12.9; 330 male patients), the per-report accuracies of ONCO-RADS-guided ChatGPT-o1 (83.5% [505 of 605]) and Gemini-2.5-Pro (82.0% [496 of 605]) were higher than those of the models without ONCO-RADS prompting (63.1% [382 of 605] and 61.2% [370 of 605], respectively) and the medical NER (55.7% [337 of 605]) (<i>P</i> < .001). Conclusion Reference-guided prompting of the LLMs ChatGPT-o1 and Gemini-2.5-Pro with ONCO-RADS improved their performance in extracting and classifying incidental findings on WB imaging reports compared with zero-shot prompting and medical NER. <b>Keywords:</b> Large Language Models, Incidental Findings, Whole-Body MRI <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250484"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Primary Bilateral Breast Rhabdomyosarcoma with Subcutaneous Metastases.","authors":"Anitha Mandava, Kamala Sannapareddy, Vijaya Tejaswini, Manasi Mundada, Pratyusha Bikkina, Veeraiah Koppula","doi":"10.1148/rycan.250759","DOIUrl":"10.1148/rycan.250759","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250759"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Standardized PSMA PET Reporting Framework for Long-Term Survival Prediction in Prostate Cancer.","authors":"Farzana Z Ali","doi":"10.1148/rycan.269012","DOIUrl":"10.1148/rycan.269012","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269012"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}