Radiology. Imaging cancer最新文献

{"title":"Rosai-Dorfman Disease Mimicking Metastatic Disease.","authors":"Sofia Velasco, Santiago Aristizábal-Ortiz, Angela Guarnizo","doi":"10.1148/rycan.240109","DOIUrl":"10.1148/rycan.240109","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e240109"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036772","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":"AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.","authors":"Daphne Resch, Roberto Lo Gullo, Jonas Teuwen, Friedrich Semturs, Johann Hummel, Alexandra Resch, Katja Pinker","doi":"10.1148/rycan.230149","DOIUrl":"10.1148/rycan.230149","url":null,"abstract":"<p><p>Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. <b>Keywords:</b> Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e230149"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591225","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":"Calcified Primary Signet Ring Cell Carcinoma of the Colon with Metastases.","authors":"Sanil Garg, Amit Gupta, Krithika Rangarajan, Rajni Yadav, Mukesh Kumar","doi":"10.1148/rycan.240079","DOIUrl":"10.1148/rycan.240079","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240079"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591226","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":"Innovative Advances in Molecular Breast Imaging Biopsy.","authors":"Amy M Fowler","doi":"10.1148/rycan.240135","DOIUrl":"10.1148/rycan.240135","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240135"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470403","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":"Digital Breast Tomosynthesis for Nonimplant-displaced Views May Be Safely Omitted at Screening Mammography.","authors":"Brandon K K Fields, Bonnie N Joe","doi":"10.1148/rycan.249014","DOIUrl":"10.1148/rycan.249014","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e249014"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470401","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":"Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.","authors":"Xiang Zhang, Ya Qiu, Wei Jiang, Zehong Yang, Mengzhu Wang, Qin Li, Yeqing Liu, Xu Yan, Guang Yang, Jun Shen","doi":"10.1148/rycan.230165","DOIUrl":"10.1148/rycan.230165","url":null,"abstract":"<p><p>Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (<i>P</i> < .001 to <i>P</i> = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], <i>P</i> < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], <i>P</i> < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. <b>Keywords:</b> MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e230165"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318136","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":"AI Systems for Mammography with Digital Breast Tomosynthesis: Expectations and Challenges.","authors":"Masako Kataoka, Takayoshi Uematsu","doi":"10.1148/rycan.240171","DOIUrl":"10.1148/rycan.240171","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240171"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760545","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":"Patient Positioning by Online Adaptive Radiation Therapy.","authors":"Paolo Farace","doi":"10.1148/rycan.240120","DOIUrl":"10.1148/rycan.240120","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240120"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591227","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":"Infrared Fluorescence-guided Surgery for Tumor and Metastatic Lymph Node Detection in Head and Neck Cancer.","authors":"Haley W White, Abdullah Bin Naveed, Benjamin R Campbell, Yu-Jin Lee, Fred M Baik, Michael Topf, Eben L Rosenthal, Marisa E Hom","doi":"10.1148/rycan.230178","DOIUrl":"10.1148/rycan.230178","url":null,"abstract":"<p><p>In patients with head and neck cancer (HNC), surgical removal of cancerous tissue presents the best overall survival rate. However, failure to obtain negative margins during resection has remained a steady concern over the past 3 decades. The need for improved tumor removal and margin assessment presents an ongoing concern for the field. While near-infrared agents have long been used in imaging, investigation of these agents for use in HNC imaging has dramatically expanded in the past decade. Targeted tracers for use in primary and metastatic lymph node detection are of particular interest, with panitumumab-IRDye800 as a major candidate in current studies. This review aims to provide an overview of intraoperative near-infrared fluorescence-guided surgery techniques used in the clinical detection of malignant tissue and sentinel lymph nodes in HNC, highlighting current applications, limitations, and future directions for use of this technology within the field. <b>Keywords:</b> Molecular Imaging-Cancer, Fluorescence © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e230178"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470402","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":"Pharyngolaryngeal Venous Plexus Mimicking Recurrent Hypopharyngeal Cancer.","authors":"Sneh Brahmbhatt, Alok A Bhatt","doi":"10.1148/rycan.240039","DOIUrl":"10.1148/rycan.240039","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240039"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470404","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}