Radiology. Imaging cancer最新文献

{"title":"Editor's Recognition Awards.","authors":"Gary D Luker","doi":"10.1148/rycan.250118","DOIUrl":"https://doi.org/10.1148/rycan.250118","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e250118"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143731386","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":"Osseous Abnormalities Associated with Phosphaturic Mesenchymal Tumor.","authors":"Andrés Vásquez, José David Cardona Ortegón, Laura Manuela Olarte Bermúdez, Angela Moreno, Karen Cifuentes Gaitán","doi":"10.1148/rycan.240391","DOIUrl":"10.1148/rycan.240391","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240391"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143731390","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 New Challenge in Prostate Cancer: Assessing Discrepant Results from Prostate MRI and PSMA PET/CT.","authors":"Govind S Mattay","doi":"10.1148/rycan.259007","DOIUrl":"10.1148/rycan.259007","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e259007"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625622","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":"Clinical and Imaging Predictors of False-Positive and False-Negative Results in Prostate Multiparametric MRI Using PI-RADS Version 2.","authors":"Bassel Salka, Jonathan P Troost, Sonia Gaur, Prasad R Shankar, Abdel Rahman Diab, Cindy Hakim, Benjamin M Mervak, Shokoufeh Khalatbari, Matthew S Davenport","doi":"10.1148/rycan.240019","DOIUrl":"10.1148/rycan.240019","url":null,"abstract":"<p><p>Purpose To evaluate predictors of false-positive (FP) and false-negative (FN) results for prostate cancer at prostate multiparametric MRI (mpMRI) using the Prostate Imaging and Reporting Data System version 2 (PI-RADS v2). Materials and Methods This was a single-center retrospective cohort study of 2548 consecutive patients who underwent prostate mpMRI examinations (October 2016-July 2022) containing zero or one PI-RADS v2 category 3-5 lesions. Prostate mpMRI examinations were interpreted by 13 radiologists. FP results were defined as prospective PI-RADS v2 score of 3 or higher but benign or grade group 1 prostate cancer at subsequent combined targeted and systematic biopsy. FN results were defined as prospective PI-RADS v2 score 2 or lower but grade group 2 or higher prostate cancer at subsequent combined targeted and systematic biopsy. Predictors of FP and FN results were assessed by logistic regression. Results Among the 2548 patients (mean age, 65.7 years ± 7.6 [SD]; all male) analyzed, 52.0% (831 of 1597) had FP results and 15.8% (150 of 951) had FN results at mpMRI. FP results were more likely for younger patients (odds ratio [OR], 0.95/y; <i>P</i> < .001), smaller lesions (OR, 0.62/mm; <i>P</i> < .001), transition zone lesions (OR, 1.74 vs peripheral zone; <i>P</i> = .006), and patients with low prostate-specific antigen (PSA) density (OR, 0.55 per 0.1 ng/mL² increase; <i>P</i> < .001). FN results were more likely for older patients (OR, 1.03/y; <i>P</i> = .01) and patients with high PSA density (OR, 2.05 per 0.1 ng/mL² increase; <i>P</i> < .001). Conclusion PSA density and patient age independently predicted FP and FN results for detection of prostate cancer at mpMRI using PI-RADS v2. These factors are not part of the PI-RADS v2 algorithm and may inform mpMRI interpretation to improve prostate cancer diagnosis. <b>Keywords:</b> MR Imaging, Prostate, PI-RADS, Prostate MRI, Prostate Cancer ^©RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240019"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415026","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":"MRI-based Supplemental Screening Improves Cancer Detection in Patients with Mammographically Dense Breast Tissue.","authors":"Brandon K K Fields, Bonnie N Joe","doi":"10.1148/rycan.259004","DOIUrl":"10.1148/rycan.259004","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e259004"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573700","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":"¹⁸F-FLT PET in Gastrointestinal Graft versus Host Disease: An Emerging Paradigm.","authors":"Austin R Pantel, David J Tischfield","doi":"10.1148/rycan.240412","DOIUrl":"10.1148/rycan.240412","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240412"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625618","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 Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.","authors":"Yixing Yu, Lixiu Cao, Binqing Shen, Mingzhan Du, Wenhao Gu, Chunyan Gu, Yanfen Fan, Cen Shi, Qian Wu, Tao Zhang, Mo Zhu, Ximing Wang, Chunhong Hu","doi":"10.1148/rycan.240213","DOIUrl":"10.1148/rycan.240213","url":null,"abstract":"<p><p>Purpose To develop deep learning (DL) radiopathomics models based on contrast-enhanced MRI and pathologic imaging to predict vessels encapsulating tumor clusters (VETC) and survival in hepatocellular carcinoma (HCC). Materials and Methods In this retrospective, multicenter study, 578 patients with HCC (mean age [±SD], 59 years ± 10; 442 male, 136 female) were divided into the training (<i>n</i> = 317), internal (<i>n</i> = 137), and external (<i>n</i> = 124) test sets. DL radiomics and pathomics models were developed to predict VETC using gadoxetic acid-enhanced MR and pathologic images. Deep radiomics score (DRS) and handcrafted and deep pathomics scores were compared between the group with VETC pattern in HCC (VETC+) and group without VETC pattern in HCC (VETC-). Multivariable Cox regression analyses were performed to identify independent prognostic factors, and the radiopathomics nomogram models were developed for early recurrence and progression-free survival (PFS). The prognostic power was evaluated using the concordance index (C index) and time-dependent receiver operating characteristic (ROC) curves. Results In the external test set, the Swin Transformer showed good performance for predicting VETC in both DL radiomics (area under the ROC curve [AUC], 0.77-0.79) and pathomics (AUC, 0.79) models. Patients with VETC+ HCC had significantly higher DRS and handcrafted and deep pathomics scores compared with patients with VETC- HCC in all datasets (all <i>P</i> < .001). The radiopathomics nomogram model incorporating DRS in the arterial phase and the handcrafted and deep pathomics scores achieved C indexes of 0.69, 0.60, and 0.67 for early recurrence and time-dependent AUCs of 0.83 (95% CI: 0.76, 0.91), 0.81 (95% CI: 0.68, 0.94), and 0.78 (95% CI: 0.67, 0.88) for 3-year PFS in the training, internal, and external test sets, respectively. Early recurrence and PFS rates statistically significantly differed between the high- and low-risk patients stratified by the radiopathomics nomogram model (all <i>P</i> < .05). Conclusion DL radiopathomics models effectively helped to predict VETC in HCC and assess the risk for early recurrence and PFS. <b>Keywords:</b> Hepatocellular Carcinoma, Deep Learning, MRI, Radiopathomics, Survival <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240213"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625625","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":"Nodule-in-Nodule Hepatocellular Carcinoma.","authors":"Zakaryae Essouni, Amine Naggar, Kaoutar Imrani, Nabil Moatassim Billah, Ittimade Nassar","doi":"10.1148/rycan.240278","DOIUrl":"10.1148/rycan.240278","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240278"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365719","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":"Nomograms Integrating MRI-derived Apparent Diffusion Coefficient and Clinicopathologic Features for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.","authors":"Huifang Chen, Xiaoxia Wang, Yao Huang, Ying Cao, Meimei Cao, Xiaofei Hu, Fangsheng Mou, Xueqin Gong, Sun Tang, Lu Wang, Lan Li, Tao Yu, Yue Cheng, Jiuquan Zhang","doi":"10.1148/rycan.240202","DOIUrl":"10.1148/rycan.240202","url":null,"abstract":"<p><p>Purpose To develop three nomograms integrating apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging to predict the status of pretreatment axillary lymph nodes (ALNs) (task 1), nonsentinel lymph nodes (task 2), and ALNs after neoadjuvant chemotherapy treatment (task 3) in patients with breast cancer. Materials and Methods Pretreatment MRI scans, including diffusion-weighted images, were retrospectively acquired from patients with breast cancer at multiple centers from May 2019 to May 2023. ADC values and clinicopathologic features were measured. Uni- and multivariable logistic regression analyses were performed to identify independent predictors of ALN metastasis. These predictors were incorporated into nomogram models for each of the three tasks. Model performance was assessed with area under the receiver operating characteristic curve (AUC) analysis in training and two external testing datasets. Results The study included 961 female patients (mean age ± SD, 50 years ± 10) with breast cancer from three hospitals. In the three tasks, the ADC values of the ALN metastasis groups were lower than those of the nonmetastasis groups (all <i>P</i> < .05). The nomogram models combining ADC values and clinicopathologic features demonstrated high predictive performance for each task in the training cohort (task 1: AUC, 0.90; task 2: AUC, 0.74; task 3: AUC, 0.75), external testing cohort 1 (task 1: AUC, 0.86; task 3: AUC, 0.82), and external testing cohort 2 (task 1: AUC, 0.90; task 3: AUC, 0.84). Conclusion Nomograms incorporating ADCs and clinicopathologic features demonstrated good performance in predicting ALN metastasis in patients with breast cancer. <b>Keywords:</b> Breast, MR-Functional Imaging, MR-Diffusion Weighted Imaging, Apparent Diffusion Coefficient, Axillary Lymph Node Metastasis, Nonsentinel Lymph Node Metastasis, Neoadjuvant Chemotherapy, Nonogram <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240202"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674283","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":"Predicting Extranodal Extension with Preoperative Contrast-enhanced CT in Patients with Oropharyngeal Squamous Cell Carcinoma.","authors":"Ryan T Hughes, Christopher M Lack, Jeffrey R Sachs, Kevin D Hiatt, Sydney Smith, Cole R Steber, Fatima Z Aly, Ralph B D'Agostino, Paul M Bunch","doi":"10.1148/rycan.240127","DOIUrl":"10.1148/rycan.240127","url":null,"abstract":"<p><p>Purpose To develop a practical, easily implementable risk stratification model based on preoperative contrast-enhanced CT (CECT) nodal features to predict the probability of pathologic extranodal extension (pENE) in patients with oropharyngeal squamous cell carcinoma (OPSCC). Materials and Methods Preoperative CECT studies in consecutive patients with OPSCC who underwent surgical resection between October 2012 and October 2020 were examined by four neuroradiologists, blinded to the pathologic outcome, for imaging features of pENE. The pathology report was queried for the presence of pENE. Decision tree analysis with cost-complexity pruning was performed to identify a clinically pragmatic model to predict pENE. Results A total of 162 patients (median age, 60 years [IQR, 54-67 years]; 134 male, 28 female) with 208 dissected heminecks were included. The primary OPSCC site for most patients was tonsil (67%, 109 of 162) or base of tongue (31%, 50 of 162). Most patients had early-stage disease (American Joint Committee on Cancer Staging Manual eighth edition category T0-T2, 93% [151 of 162]; N0-N1, 90% [145 of 162]). Pathologically confirmed pENE was reported in 28% (45 of 162) of patients. CECT features that were significantly associated with pENE on univariable analysis included size, necrosis, spiculation, perinodal stranding, and infiltration of adjacent structures. Decision tree analysis identified a predictive model including spiculation or irregular margins, matted nodes, and infiltration of adjacent structures. The model had a sensitivity of 41% (19 of 46) and specificity of 96% (157 of 162) for predicting pENE. Conclusion The developed model for predicting pENE using preoperative CECT features is practical and had high specificity in patients with OPSCC. Further prospective study is warranted to determine impact on clinical management and outcomes. <b>Keywords:</b> Head/Neck, CT, Radiation Therapy/Oncology, Neoplasms-Primary, Oncology, Decision Analysis, Observer Performance <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 2","pages":"e240127"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365665","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}