Radiology. Imaging cancer最新文献

{"title":"Estimating the Lifetime Cancer Risk Associated with CT Imaging.","authors":"Saumya Gurbani, Meagan A Bechel","doi":"10.1148/rycan.259011","DOIUrl":"https://doi.org/10.1148/rycan.259011","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e259011"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187836","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":"Performance of Diagnostic Breast Imaging in Symptomatic Pregnant and Lactating Patients: Systematic Review and Meta-Analysis.","authors":"Benjamin W Weber, Lu Mao, Kelley Salem, Mary Hitchcock, Abigail H Keller, Mai A Elezaby, Lonie R Salkowski, Laura M Bozzuto, Amy M Fowler","doi":"10.1148/rycan.240281","DOIUrl":"https://doi.org/10.1148/rycan.240281","url":null,"abstract":"<p><p>Purpose To perform a systematic review of the literature and meta-analysis to summarize the diagnostic performance of breast imaging modalities for cancer detection in pregnant and lactating patients. Materials and Methods A systematic review of the literature in PubMed, Scopus, Web of Science, and Cochrane Library databases published up until March 3, 2023, was conducted. Included studies evaluated patients of any age who underwent breast imaging during pregnancy or lactation. The primary outcome of this review was sensitivity and specificity of each imaging modality. Meta-analysis was performed using a bivariate modeling approach, and summary receiver operating characteristic (ROC) analysis was used to generate a summary area under the ROC curve (AUC). Results Twenty-five studies met the eligibility criteria and included 1681 female patients (mean age, 33 years; range, 18-49 years). For US, seven of 24 studies had complete data yielding an AUC of 0.90 (95% CI: 0.85, 0.93), a sensitivity of 81% (95% CI: 56, 94), and a specificity of 85% (95% CI: 71, 92). For mammography, three of 21 studies had complete data yielding an AUC of 0.93 (95% CI: 0.75, 0.97), a sensitivity of 72% (95% CI: 47, 88), and a specificity of 93% (95% CI: 86, 97). For MRI, two of eight studies had complete data yielding an AUC of 95% (95% CI: 59, 96), a sensitivity of 91% (95% CI: 56, 99), and a specificity of 88% (95% CI: 48, 98). Conclusion US, mammography, and breast MRI showed high diagnostic performance for detection of pregnancy-associated breast cancer in symptomatic pregnant or lactating patients. <b>Keywords:</b> Meta-Analysis, Breast, Oncology, Pregnancy, Mammography, MR-Dynamic Contrast Enhanced, Ultrasound <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240281"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187839","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":"Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI.","authors":"Zonglin Liu, Runqi Meng, Qiong Ma, Zhen Guan, Rong Li, Caixia Fu, Yanfen Cui, Yiqun Sun, Tong Tong, Dinggang Shen","doi":"10.1148/rycan.240359","DOIUrl":"https://doi.org/10.1148/rycan.240359","url":null,"abstract":"<p><p>Purpose To develop and validate a deep multitask network, MultiRecNet, for fully automatic prediction of disease-free survival (DFS) in patients with neoadjuvant chemoradiotherapy (nCRT)-treated locally advanced rectal cancer (LARC). Materials and Methods This retrospective study collected clinical information and baseline multimodal MRI (T2, apparent diffusion coefficient [ADC], <i>D</i>_app, and <i>K</i>_app) data from patients with LARC after nCRT at three centers between October 2011 and May 2019. Patients from centers 1 and 2 were divided into training, validation, and internal testing sets, while patients from center 3 served as the external testing set. MultiRecNet is capable of simultaneously performing segmentation, classification, and survival prediction tasks within a single framework. Multiple combinations of data from different clinical stages (pretreatment and postoperative) were input into MultiRecNet to generate different models and identify the model with optimal performance. Evaluation metrics included the Dice similarity coefficient (DSC), the area under the receiver operating characteristic curve (AUC), and the Harrell concordance index (C-index) for the segmentation, classification, and survival prediction tasks, respectively. Results The study included 445 patients: 261 in the training set (median age, 60 years [IQR, 53-67 years]; 172 male), 37 in the validation set (median age, 61 years [IQR, 55-68 years]; 30 male), 75 in the internal testing set (median age, 60 years [IQR, 51-67 years]; 45 male), and 72 in the external testing set (median age, 55 years [IQR, 49-61 years]; 38 male). In the internal testing set, the best model based on MultiRecNet (the All model, with T2-weighted imaging, ADC, <i>D</i>_app, <i>K</i>_app, pretreatment clinical indicators, and postoperative pathologic indicators) achieved a DSC of 0.72 for tumor segmentation, an AUC of 0.97 (95% CI: 0.92, >.99) for recurrence or metastasis classification at 3 years, and a C-index of 0.92 for DFS prediction. In the external testing set, the model continued to perform well for survival prediction (C-index = 0.81, <i>P</i> < .001). Conclusion The MultiRecNet-based model enabled prognostic prediction in a fully automated end-to-end manner in patients with LARC following nCRT. <b>Keywords:</b> MR-Imaging, Abdomen/GI, Rectum, Oncology <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240359"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187840","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":"Benign and Malignant Breast Lesions: Differentiation Using Microstructural Metrics Derived from Time-Dependent Diffusion MRI.","authors":"Yun Su, Ya Qiu, Xingke Huang, Yuqin Peng, Zehong Yang, Miamiao Ding, Lanxin Hu, Yishi Wang, Chen Zhao, Wenshu Qian, Xiang Zhang, Jun Shen","doi":"10.1148/rycan.240287","DOIUrl":"https://doi.org/10.1148/rycan.240287","url":null,"abstract":"<p><p>Purpose To investigate the diagnostic performance of microstructural metrics from time-dependent diffusion MRI (T_d-dMRI) in distinguishing between benign and malignant breast lesions. Materials and Methods This prospective study (ClinicalTrials.gov identifier: NCT05373628) enrolled participants with breast lesions confirmed with US, mammography, or both from January 2022 to June 2023. Participants underwent oscillating and pulsed gradient encoded T_d-dMRI and conventional diffusion-weighted imaging (DWI). T_d-dMRI data were fitted using the imaging microstructural parameters using limited spectrally edited diffusion model. Lesions were classified as benign or malignant based on pathology. Diagnostic performances of T_d-dMRI metrics and apparent diffusion coefficients (ADCs) from DWI in distinguishing between benign and malignant tumors were assessed using receiver operating characteristic analysis and compared using the DeLong test. Results The study included 102 female participants (mean age: 48 years ± 12 [SD]) with 105 breast lesions (three participants had two lesions), including 31 benign and 74 malignant lesions. The cell diameter, cell density, and intracellular volume fraction from T_d-dMRI were higher and the ADC was lower in malignant lesions compared with benign lesions (<i>P</i> < .001 to <i>P</i> = .001). Among microstructural metrics from T_d-dMRI, the cell density had the highest area under the receiver operating characteristic curve, which was higher than that of the ADC (0.93 [95% CI: 0.88, 0.98] vs 0.79 [95% CI: 0.70, 0.88], <i>P</i> = .03). Conclusion A single microstructural metric derived from T_d-dMRI, cell density, had higher performance than conventional ADC in distinguishing benign and malignant breast lesions. <b>Keywords:</b> MR-Diffusion Weighted Imaging, Breast Clinical trial registration no. NCT05373628 <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240287"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014135","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":"Beyond Conventional CT: Role of Dual-Energy CT in Monitoring Response to Therapy in Abdominal Malignancies.","authors":"Ali Pourvaziri, Nayla Mroueh, Rory L Cochran, Shravya Srinivas Rao, Avinash Kambadakone","doi":"10.1148/rycan.240142","DOIUrl":"https://doi.org/10.1148/rycan.240142","url":null,"abstract":"<p><p>In the era of precision medicine, imaging plays a critical role in evaluating treatment response to various oncologic therapies. For decades, conventional morphologic assessments using cross-sectional imaging have been the standard for monitoring the effectiveness of systemic and locoregional therapies in patients with cancer. However, the development of new functional imaging tools has widened the scope of imaging from mere response assessment to patient selection and outcome prediction. Dual-energy CT (DECT), known for its superior material differentiation capabilities, shows promise in enhancing treatment response evaluation. DECT-based iodine quantification methods are increasingly being investigated as surrogates for assessing tumor vascularity and physiology, which is particularly important in patients undergoing emerging targeted therapies. The purpose of this review article is to discuss the current and emerging role of DECT in assessing treatment response in patients with malignant abdominal tumors. <b>Keywords:</b> CT-Dual Energy, Transcatheter Tumor Therapy, Tumor Response, Iodine Uptake, Therapeutic Response © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240142"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014137","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":"Accuracy of Post-Neoadjuvant Therapy MRI for the Assessment of Anal Sphincter Involvement in Patients with Rectal Cancer.","authors":"Maria El Homsi, Louis Fuqua, Tae-Hyung Kim, Maria Clara Fernandes, Jinru Shia, Maria Widmar, Charlie White, Marinela Capanu, Lee Rodriguez, Iva Petkovska","doi":"10.1148/rycan.240208","DOIUrl":"https://doi.org/10.1148/rycan.240208","url":null,"abstract":"<p><p>Purpose To assess the accuracy of post-neoadjuvant therapy (NAT) MRI, as compared with that of pathologic evaluation, to determine anal sphincter involvement in patients with rectal cancer. Materials and Methods This retrospective study included patients diagnosed with rectal cancer between January 2015 and December 2017 whose baseline MRI showed anal sphincter involvement and who then underwent NAT, post-NAT MRI, and abdominoperineal resection. Four radiologists (with 20 years, 5 years, 2 years, and 1 year of experience) independently reviewed MRI findings. Resected specimens were reviewed by a gastrointestinal pathologist. Interreader agreement between the radiologists and pathologist was assessed using the Cohen κ statistic. Conditional sensitivity, specificity, and positive predictive value (PPV) of the radiologists were calculated among patients for whom the radiologists and the pathologist agreed that the anal canal was involved. Results Thirty-two patients were included (mean age ± SD, 60 years ± 15; 19 male, 13 female). For the post-NAT assessment of anal sphincter involvement, agreement between readers 1, 2, and 4 and the pathologist was moderate (κ = 0.55 [95% CI: 0.18, 0.91], 0.45 [95% CI: -0.06, 0.82], and 0.53 [95% CI: 0, 0.89], respectively). There was fair agreement between reader 3 and the pathologist (κ = 0.30 [95% CI: -0.09, 0.67]). Radiologists had high sensitivity for the detection of anal sphincter involvement (88%-100%), high PPV (88%-96%), and moderate to high specificity (50%-80%); the senior radiologist had the highest sensitivity, PPV, and specificity. Conclusion Radiologists had fair to moderate interreader agreement with the pathologist for post-NAT assessment of anal sphincter involvement in patients with rectal cancer and showed high conditional sensitivity regardless of their level of experience. <b>Keywords:</b> Abdomen/GI, Rectum, Oncology, Post-Neoadjuvant Therapy MRI <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240208"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009495","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 Anatomy-Aware Morph Model for Registration of Prostate Whole-Mount Histopathology to MRI.","authors":"Fatemeh Zabihollahy, Holden H Wu, Anthony E Sisk, Robert E Reiter, Steven S Raman, Neil E Fleshner, George M Yousef, KyungHyun Sung","doi":"10.1148/rycan.240336","DOIUrl":"https://doi.org/10.1148/rycan.240336","url":null,"abstract":"<p><p>Purpose To develop and evaluate a novel deep learning-based approach for registering presurgical MR and whole-mount histopathology (WMHP) images of the prostate. Materials and Methods This retrospective study included patients who underwent prostate MRI before radical prostatectomy between July 2016 and June 2020. High-resolution ex vivo MRI was used as a reference to assess the structural relationship between in vivo MRI and WMHP. An Anatomy-Aware Morph model, a hybrid attention and convolutional neural network-based approach, was developed for multimodality prostate image registration. The pipeline included a module to estimate and correct distortion and motion between the prostate specimen and outside the human body. The dataset was divided into 270 and 45 patients for training and testing, respectively. Registration accuracy was evaluated using Dice similarity coefficient (DSC), Hausdorff distance, and target registration error. Results The proposed approach was validated using 160 images extracted from 45 male patients in the testing dataset with the average age ± SD of 64.0 years ± 6.6. The method achieved a DSC and Hausdorff distance of 0.95 ± 0.06 and 1.84 mm ± 0.38. The two-dimensional target registration errors between 90 sets of landmarks on in vivo MR images and WMHP images were 3.93 mm ± 0.80 and 1.18 mm ± 0.28 before and after registration (<i>P</i> < .001). The developed algorithm significantly outperformed the state-of-the-art VoxelMorph method for multimodality prostate image registration (<i>P</i> < .0001 for both DSC and Hausdorff distance). Conclusion The developed registration method successfully aligned presurgical prostate MR and histopathology images, facilitating automated mapping of prostate cancer from WMHP to MRI. <b>Keywords:</b> Affine Transformation, Deformable Registration, Prostate Magnetic Resonance Imaging, Prostate Whole-Mount Histopathology <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240336"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187835","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":"Phyllodes Tumor with Skull and Mediastinal Osteosarcomatous Metastases.","authors":"Matheus H Taborda, Nathalia Luiza Ferri Bonmann, Dante Luiz Escuissato","doi":"10.1148/rycan.250028","DOIUrl":"https://doi.org/10.1148/rycan.250028","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e250028"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030665","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":"Beyond the Black Box: Explainable AI Transforms Hepatocellular Carcinoma Diagnosis at MRI.","authors":"Yashbir Singh, Gregory J Gores, Bradley J Erickson","doi":"10.1148/rycan.250198","DOIUrl":"https://doi.org/10.1148/rycan.250198","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e250198"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187834","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":"Rising Rates of Distant-Stage Breast Cancer: Trends Among U.S. Women.","authors":"Eric A Davis, Bonnie N Joe","doi":"10.1148/rycan.259012","DOIUrl":"https://doi.org/10.1148/rycan.259012","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e259012"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187841","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}