Radiology. Imaging cancer最新文献

{"title":"Quantitative 3-T Multiparametric MRI Parameters as Predictors of Aggressive Prostate Cancer.","authors":"Daniel Hyeong Seok Kim, Ida Sonni, Tristan Grogan, Anthony Sisk, Vishnu Murthy, William Hsu, KyungHyun Sung, David S Lu, Robert E Reiter, Steven S Raman","doi":"10.1148/rycan.240011","DOIUrl":"10.1148/rycan.240011","url":null,"abstract":"<p><p>Purpose To determine which quantitative 3-T multiparametric MRI (mpMRI) parameters correlate with and help predict the presence of aggressive large cribriform pattern (LCP) and intraductal carcinoma (IDC) prostate cancer (PCa) at whole-mount histopathology (WMHP). Materials and Methods This retrospective study included 130 patients (mean age ± SD, 62.6 years ± 7.2; 100% male) with 141 PCa lesions who underwent preoperative prostate 3-T mpMRI, radical prostatectomy, and WMHP between January 2019 and December 2022. Lesions at WMHP were matched to 3-T mpMRI lesions with American College of Radiology Prostate Imaging Reporting and Data System version 2.1 scores of at least 3 or higher, and the following parameters were derived: apparent diffusion coefficient (ADC), volume transfer constant, rate constant, and initial area under the curve (iAUC). Each lesion was categorized into three subcohorts with increasing aggressiveness: LCP negative and IDC negative (subcohort 1), LCP positive and IDC negative (subcohort 2), and LCP positive and IDC negative (subcohort 3). Analysis of variance was performed to assess differences, Jonckheere test was performed to establish trends, and a classification and regression tree (CART) was used to establish a prediction model. Results Of the 141 total lesions, there were 41 (29.1%), 49 (34.8%), and 51 (36.2%) lesions in subcohorts 1, 2, and 3, with mean ADCs of 892 × 10^-6 mm²/sec ± 20, 826 × 10^-6 mm²/sec ± 209, and 763 × 10^-6 mm²/sec ± 163 (<i>P</i> = .007) and mean iAUCs of 5.4 mmol/L/sec ± 2.5, 6.7 mmol/L/sec ± 3.0, and 6.9 mmol/L/sec ± 3.5 (<i>P</i> = .04), respectively. ADC was negatively correlated (<i>P</i> = .004), and rate constant and iAUC were positively correlated (<i>P</i> = .048 and <i>P</i> = .04, respectively) with increasing histologic PCa aggressiveness. The CART model correctly allocated 39.0%, 24.5%, and 84.3% of PCa lesions to subcohorts 1, 2, and 3, respectively. Conclusion Quantitative 3-T mpMRI parameters significantly correlated with and helped predict aggressive LCP and IDC PCa at WMHP. <b>Keywords:</b> Prostate, MRI, Pathology © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240011"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142922680","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 Imaging of Paragangliomas and Pheochromocytomas.","authors":"Brian J Burkett, Derek R Johnson","doi":"10.1148/rycan.240440","DOIUrl":"10.1148/rycan.240440","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240440"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143010602","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":"Improving Ovarian Cancer Detection: Can CEUS Refine the O-RADS System?","authors":"Deborah A Baumgarten","doi":"10.1148/rycan.240397","DOIUrl":"10.1148/rycan.240397","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240397"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865325","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":"Potential of AI System to Enhance Early Breast Cancer Detection in Screening Mammography.","authors":"Allison Dortilus, Maggie Chung","doi":"10.1148/rycan.249027","DOIUrl":"10.1148/rycan.249027","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e249027"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865328","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":"Recommendations from Imaging, Oncology, and Radiology Organizations to Guide Management in Prostate Cancer: Summary of Current Recommendations.","authors":"Andy Mew, Eva Chau, Kaustav Bera, Nikhil Ramaiya, Sree Harsha Tirumani","doi":"10.1148/rycan.240091","DOIUrl":"10.1148/rycan.240091","url":null,"abstract":"<p><p>Prostate cancer is the second most common malignancy among male individuals in the United States and requires careful imaging approaches because of its varied presentations. This review examines prostate cancer imaging guidelines from leading organizations, including the American College of Radiology, American Urological Association, European Association of Urology, American Society of Clinical Oncology, and National Comprehensive Cancer Network, and serves as a reference highlighting commonalities and divergences in current imaging recommendations across prostate cancer states. We outline these organizations and their methods, focusing on their approaches to panel expertise, guideline development, evidence grading, and revision schedules. We then compare and contrast the role of various imaging modalities across states of prostate cancer management in the following categories: clinically suspected prostate cancer, clinically established prostate cancer: active surveillance or staging, monitoring metastatic disease, and posttreatment follow-up: recurrent or residual disease. Overall, there is consensus on the importance of multiparametric MRI in diagnosis and staging prior to active surveillance and the emerging role of prostate-specific membrane antigen (PSMA) PET/CT in metastatic and recurrent disease. However, there is disparity in imaging recommendations for detecting metastases in unfavorable intermediate-risk prostate cancer and views on current applications of PSMA PET/CT. Ultimately, variations in radiologic expertise exist among guideline panels, and there continue to be inconsistencies in imaging recommendations in prostate cancer. <b>Keywords:</b> Prostate, Genital/Reproductive, Oncology <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240091"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142953971","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":"Radiologically Negative Colorectal Diaphragmatic Metastasis Mimicking Liver Lesion.","authors":"Xiaoying Fu, Zechuan Jin, Hai-Ning Chen","doi":"10.1148/rycan.240225","DOIUrl":"10.1148/rycan.240225","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e240225"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786879","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":"Assessing HCC Response to Locoregional Therapies: Diagnostic Performance of LI-RADS Nonradiation Treatment Response Algorithm Version 2024 and the Role of Ancillary Features.","authors":"Radhika Rajeev, Hero K Hussain","doi":"10.1148/rycan.259002","DOIUrl":"10.1148/rycan.259002","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 1","pages":"e259002"},"PeriodicalIF":5.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11791663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034062","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":"Deformable Mapping of Rectal Cancer Whole-Mount Histology with Restaging MRI at Voxel Scale: A Feasibility Study.","authors":"João Miranda, Jon S Heiselman, Canan Firat, Jayasree Chakraborty, Rami S Vanguri, Antonildes N Assuncao, Josip Nincevic, Tae-Hyung Kim, Lee Rodriguez, Nil Urganci, Mithat Gonen, Julio Garcia-Aguilar, Marc J Gollub, Jinru Shia, Natally Horvat","doi":"10.1148/rycan.240073","DOIUrl":"10.1148/rycan.240073","url":null,"abstract":"<p><p>Purpose To develop a radiology-pathology coregistration method for 1:1 automated spatial mapping between preoperative rectal MRI and ex vivo rectal whole-mount histology (WMH). Materials and Methods This retrospective study included consecutive patients with rectal adenocarcinoma who underwent total neoadjuvant therapy followed by total mesorectal excision with preoperative rectal MRI and WMH from January 2019 to January 2022. A gastrointestinal pathologist and a radiologist established three corresponding levels for each patient at rectal MRI and WMH, subsequently delineating external and internal rectal wall contours and the tumor bed at each level and defining eight point-based landmarks. An advanced deformable image coregistration model based on the linearized iterative boundary reconstruction (LIBR) approach was compared with rigid point-based registration (PBR) and state-of-the-art deformable intensity-based multiscale spectral embedding registration (MSERg). Dice similarity coefficient (DSC), modified Hausdorff distance (MHD), and target registration error (TRE) across patients were calculated to assess the coregistration accuracy of each method. Results Eighteen patients (mean age, 54 years ± 13 [SD]; nine female) were included. LIBR demonstrated higher DSC versus PBR for external and internal rectal wall contours and tumor bed (external: 0.95 ± 0.03 vs 0.86 ± 0.04, respectively, <i>P</i> < .001; internal: 0.71 ± 0.21 vs 0.61 ± 0.21, <i>P</i> < .001; tumor bed: 0.61 ± 0.17 vs 0.52 ± 0.17, <i>P</i> = .001) and versus MSERg for internal rectal wall contours (0.71 ± 0.21 vs 0.63 ± 0.18, respectively; <i>P</i> < .001). LIBR demonstrated lower MHD versus PBR for external and internal rectal wall contours and tumor bed (external: 0.56 ± 0.25 vs 1.68 ± 0.56, respectively, <i>P</i> < .001; internal: 1.00 ± 0.35 vs 1.62 ± 0.59, <i>P</i> < .001; tumor bed: 2.45 ± 0.99 vs 2.69 ± 1.05, <i>P</i> = .03) and versus MSERg for internal rectal wall contours (1.00 ± 0.35 vs 1.62 ± 0.59, respectively; <i>P</i> < .001). LIBR demonstrated lower TRE (1.54 ± 0.39) versus PBR (2.35 ± 1.19, <i>P</i> = .003) and MSERg (2.36 ± 1.43, <i>P</i> = .03). Computation time per WMH slice for LIBR was 35.1 seconds ± 12.1. Conclusion This study demonstrates feasibility of accurate MRI-WMH coregistration using the advanced LIBR method. <b>Keywords:</b> MR Imaging, Abdomen/GI, Rectum, Oncology <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e240073"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142506717","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":"Erratum for: Integrating Contrast-enhanced US to O-RADS US for Classification of Adnexal Lesions with Solid Components: Time-intensity Curve Analysis versus Visual Assessment.","authors":"Manli Wu, Ying Wang, Manting Su, Ruili Wang, Xiaofeng Sun, Rui Zhang, Liang Mu, Li Xiao, Hong Wen, Tingting Liu, Xiaotao Meng, Licong Huang, Xinling Zhang","doi":"10.1148/rycan.249024","DOIUrl":"10.1148/rycan.249024","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e249024"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142558605","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":"Low-Flip-Angle Dynamic Susceptibility Contrast MRI: A Promising Tool for Glioblastoma Tumor Mapping.","authors":"Michelle L Wegscheid","doi":"10.1148/rycan.249026","DOIUrl":"10.1148/rycan.249026","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 6","pages":"e249026"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142688595","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}