Radiology. Imaging cancer最新文献

{"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":"https://doi.org/10.1148/rycan.260090","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260090"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","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":"https://doi.org/10.1148/rycan.260031","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260031"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","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":"https://doi.org/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":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Papillary Thyroid Carcinoma in Indeterminate Nodules.","authors":"Yulong Dou, Zhenyu Xiang, Hao Song, Yunfeng Wang, Sijin Li, Ping Wu","doi":"10.1148/rycan.260064","DOIUrl":"https://doi.org/10.1148/rycan.260064","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260064"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147841973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence as a Second Reader in a Simulation Study of Population-based Mammography Screening in the Netherlands.","authors":"Dillon Yeh, Maggie Chung","doi":"10.1148/rycan.269009","DOIUrl":"https://doi.org/10.1148/rycan.269009","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269009"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-based Multiple Arterial Phase MRI: A Step toward Improved Hepatocellular Carcinoma Diagnosis.","authors":"Mengchao Zhang","doi":"10.1148/rycan.260156","DOIUrl":"https://doi.org/10.1148/rycan.260156","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260156"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Model Based on Tumor and Visceral Adipose Tissue CT Features for Predicting Peritoneal Metastasis Risk after Radical Gastrectomy in Serosa-Invasive Gastric Cancer.","authors":"Yueyue Li, Ximiao Wang, Qiuying Chen, Hua Xiao, Yilong Huang, Liebin Huang, Lian Jian, Wansheng Long, Bao Feng, Shuixing Zhang","doi":"10.1148/rycan.250353","DOIUrl":"https://doi.org/10.1148/rycan.250353","url":null,"abstract":"<p><p>Purpose To develop and validate a deep learning model integrating tumor and visceral adipose tissue (VAT) CT scan features with clinical indicators to predict postoperative peritoneal metastasis in serosa-invasive gastric cancer. Materials and Methods This multicenter, retrospective study between April 2008 and January 2018 included patients with pathologically confirmed serosa-invasive gastric cancer. Patients were divided into training, internal test, and independent external test sets. Tumor and VAT regions were segmented at preoperative CT. Deep features were extracted using a ResNet18 network. A fused tumor-VAT deep learning signature (F-DLS) was generated, incorporating clinical variables into a multimodal deep learning radiomics model (MDLR) using a sparse Bayesian extreme learning machine. Model performance was assessed using receiver operating characteristic curve, integrated discrimination improvement, calibration, decision curve analysis, and recurrence-free survival. Results Among 416 patients (mean age, 56.6 years ± 11.6; 66.1% male patients), the F-DLS achieved area under the receiver operating characteristic curve (AUC) values of 0.81 (95% CI: 0.73, 0.88) in the internal test set and 0.79 (95% CI: 0.71, 0.86) in the external test set. Compared with the tumor tissue DLS and VAT-DLS, the F-DLS showed numerically higher AUCs without statistical significance. The MDLR achieved the strongest predictive performance, with AUCs of 0.86 (95% CI: 0.79, 0.92) in the internal test set and 0.86 (95% CI: 0.78, 0.92) in the external test set. The MDLR statistically significantly outperformed clinical and deep learning-only models (integrated discrimination improvement, <i>P</i> < .001), showed good calibration, and provided favorable net benefit on decision curve analysis. High-risk patients identified by the MDLR had significantly shorter recurrence-free survival (log-rank <i>P</i> < .001). Conclusion The MDLR integrating CT scan features and clinical indicators enabled noninvasive prediction of peritoneal metastasis risk in serosa-invasive gastric cancer and may facilitate postoperative risk stratification. <b>Keywords:</b> Gastric Cancer, Peritoneal Metastasis, CT, Visceral Adipose Tissue, Deep Learning <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250353"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interval Cancers after Negative Screening Contrast-enhanced Mammography.","authors":"Tali Amir, Carol H Lee, Sarah Eskreis-Winkler, Molly P Hogan, Noam Nissan, Varadan Sevilimedu, Victoria L Mango, Kimberly N Feigin, Maxine S Jochelson, Christopher E Comstock, Janice S Sung","doi":"10.1148/rycan.250559","DOIUrl":"https://doi.org/10.1148/rycan.250559","url":null,"abstract":"<p><p>Purpose To determine the interval cancer rate (ICR) after negative screening contrast-enhanced mammography (CEM) and compare the characteristics of interval cancers (ICs) with those of CEM screen-detected cancers. Materials and Methods This retrospective, single-institution study included consecutive screening CEM examinations performed from January 2015 through December 2021. ICs diagnosed within 1 year of a negative screening CEM and all CEM screen-detected cancers were identified. Two breast radiologists independently reviewed prior negative CEM examinations to classify ICs as missed, misinterpreted, or occult. Patient- and lesion-level characteristics were compared between ICs and screen-detected cancers using the Wilcoxon rank sum test for continuous variables and the Fisher exact or χ² tests for categorical variables. Results The study included 6911 screening CEM examinations in 2756 female patients (median age, 53 years; IQR, 47-60 years). Among 6120 negative screening examinations, 14 ICs were diagnosed in 14 patients. The overall ICR was 2.29 cancers per 1000 examinations, and the symptomatic ICR was 0.82 per 1000 examinations (five of 6120). ICs accounted for 13% (14 of 106) of all cancers diagnosed (interval and screen detected). Invasive ICs occurred more frequently in the setting of moderate or marked background parenchymal enhancement than screen-detected cancers (six of eight, 75% vs 17 of 57, 30%; <i>P</i> = .02). Most ICs (10 of 14, 71%) were occult on prior screening CEM. Conclusion The ICR after CEM was 2.29 cancers per 1000 examinations, representing 13% of all cancers diagnosed. Most ICs were occult at prior CEM, and invasive ICs were more frequently associated with moderate or marked background parenchymal enhancement when compared with CEM screen-detected cancers. <b>Keywords:</b> Mammography, Breast, Interval Cancers <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250559"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147820063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Percutaneous Thermal Ablation for Early-Stage Breast Cancer: A Randomized Phase II \"Pick-the-Winner\" Trial.","authors":"Chang Shu, Bonnie Joe","doi":"10.1148/rycan.269008","DOIUrl":"https://doi.org/10.1148/rycan.269008","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269008"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications.","authors":"Min Jae Cha, Hyewon Choi, Boda Nam, Chung Ryul Oh, Se-Hoon Lee, Joon Young Choi, Semin Chong, Joungho Han, Kyung Soo Lee","doi":"10.1148/rycan.250644","DOIUrl":"https://doi.org/10.1148/rycan.250644","url":null,"abstract":"<p><p>Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite recent therapeutic advances, including immune checkpoint inhibitors and emerging targeted agents, survival outcomes remain poor. Recent molecular insights have identified four transcription factor-driven subtypes-SCLC-A, SCLC-N, SCLC-P, and the inflamed subtype SCLC-I-providing a framework for precision and immunotherapy-based strategies. This review summarizes the evolving scope of imaging in SCLC and highlights emerging approaches that support personalized medicine. Conventional imaging with CT, MRI, and fluorine 18 fluorodeoxyglucose PET/CT remains essential for diagnosis, staging, and treatment response assessment. Semiquantitative, volume-based PET/CT metrics, such as metabolic tumor volume and total lesion glycolysis, correlate with tumor proliferation and provide stronger prognostic value than maximum standardized uptake value. Emerging imaging approaches, including radiomics, radiogenomics, and machine learning, may further enable noninvasive tumor characterization and outcome prediction. Recent advances in molecular imaging, including delta-like ligand 3- and somatostatin receptor-targeted immune-PET, represent key steps toward biomarker-guided and personalized therapy. Together, integration of structural, functional, and molecular imaging with biologic insights is expected to shape the next phase of precision oncology in this highly aggressive malignancy. <b>Keywords:</b> Lung, Imaging Modality, PET, MRI, Molecular Imaging, Oncology, Neoplasms-Primary, CT, MR Imaging, Diagnosis, PET/CT, Small Cell Lung Cancer, Radiogenomics, Machine Learning, Radiolabeled Tracer, Personalized Medicine <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250644"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147820018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}