Radiology. Imaging cancer最新文献

{"title":"A Global Partnership to Address Breast Cancer Challenges in Indonesia through a Breast Radiology Training Program.","authors":"Stefanie Woodard, Eniola Oluyemi, Andita Hidayati, Haidyan Wahyu Anugrha, Reyhan Eddy Yunus, Windu Cahyaningrum Handayani Notonagoro Suryaningrat, Ufara Zuwasti, Sidhvi Reddy, Rohan Badve, Zaurayze Rehman, Wei T Yang","doi":"10.1148/rycan.260103","DOIUrl":"10.1148/rycan.260103","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260103"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988823","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":"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":"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":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13261105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147820063","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":"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":"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":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779431","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":"Long-standing Large Nonfunctional Cardiac Paraganglioma.","authors":"Thomas Martin Schwedhelm, Jeeban Paul Das","doi":"10.1148/rycan.250595","DOIUrl":"10.1148/rycan.250595","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250595"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147949451","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":"Delta Histogram Analysis of Synthetic MRI for Evaluating Grade and Cellular Proliferation of Meningioma.","authors":"Ye Li, Yizhu Chen, Mingwei Sun, Xiaofang Zhou, Danjie Lin, Xiaodan Chen, Dingfu Wei, Kai Wang, Jialu Zhang, Yunjing Xue, Lin Lin","doi":"10.1148/rycan.250419","DOIUrl":"10.1148/rycan.250419","url":null,"abstract":"<p><p>Purpose To investigate the potential of delta histogram analysis of synthetic MRI (SyMRI) for predicting meningioma grade and cellular proliferation. Materials and Methods In this prospective study from February 2022 to October 2023, consecutive participants scheduled for conventional MRI and SyMRI were enrolled. Histogram parameters (HPs) were obtained with whole-tumor volume analysis. Delta histogram parameters, including absolute (dα = postcontrast HP - precontrast HP) and relative delta HPs (dα% = [postcontrast HP - precontrast HP]/precontrast HP), where α = T1, T2, or proton density (PD), were calculated. The Mann-Whitney <i>U</i> test was used to compare HPs between high- and low-grade groups. Receiver operating characteristic curve and multiple logistic regression analysis, the DeLong test, and integrated discrimination improvement were used to evaluate diagnostic efficacy of the HPs and models. Spearman rank correlation coefficients analyzed the correlations between HPs and Ki-67 expression. Results Ninety-five participants with meningioma (mean age, 58 years ± 13 [SD]; 28 male) were enrolled in the study. Participants with high-grade meningiomas had greater PD skewness; postcontrast T1 energy and total energy; postcontrast T2 IQR, mean absolute deviation (MAD), and robust MAD (rMAD); postcontrast PD skewness; dPD variance, IQR, MAD, and rMAD; and dPD% variance, IQR, MAD, and rMAD and lower PD median and 90th percentile (<i>P</i> < .05). These single SyMRI HPs demonstrated moderate diagnostic accuracy in grading meningioma (area under the receiver operating characteristic curve [AUC], range: 0.65-0.79). Among the combined models, the SyMRI model achieved the best performance (AUC = 0.88), outperforming single models (<i>P</i> < .05). Multiple SyMRI HPs were correlated with the Ki-67 index (<i>P</i> < .05). Conclusion Delta HPs from SyMRI demonstrate potential as biomarkers for meningioma grading and proliferation assessment. <b>Keywords:</b> CNS, Brain/Brain Stem, Meningioma, Synthetic MRI, Delta Histogram Analysis, Neoplasm Grading, Ki-67 Antigen <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250419"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148056604","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":"A PET Radiotracer for Mapping VISTA in the Tumor Microenvironment.","authors":"Rohit Singh, Happy Agarwal, Lacey R McNally","doi":"10.1148/rycan.269011","DOIUrl":"10.1148/rycan.269011","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269011"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988815","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":"Virchow Node in Prostate Cancer.","authors":"Adrien Holzgreve, Jeremie Calais","doi":"10.1148/rycan.250681","DOIUrl":"10.1148/rycan.250681","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250681"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609969","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":"Beyond Histology: Evaluating Ultrafast DCE MRI Texture Analysis for Breast DCIS Grading.","authors":"Yiming Wang, Kaixuan Zhao","doi":"10.1148/rycan.260123","DOIUrl":"10.1148/rycan.260123","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260123"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609996","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":"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":"10.1148/rycan.269008","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269008"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779413","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":"Diagnostic Performance and Reliability of RADS Classification Systems for Solitary Bone Lesions.","authors":"Netanja I Harlianto","doi":"10.1148/rycan.269010","DOIUrl":"10.1148/rycan.269010","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269010"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779429","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}