Radiology. Imaging cancer最新文献

{"title":"Impact of Contrast-enhanced Mammography on Positive Predictive Value in Patients Recommended for Biopsy after Standard-of-Care Diagnostic Imaging.","authors":"Brandon K K Fields, Bonnie N Joe","doi":"10.1148/rycan.259031","DOIUrl":"10.1148/rycan.259031","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e259031"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145459607","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":"Trends in Head and Neck Cancer: Oral Cavity Carcinoma and What the Radiologist Needs to Know.","authors":"Kristine M Mosier, Brian D Graner, Benjamin R Gray","doi":"10.1148/rycan.250154","DOIUrl":"10.1148/rycan.250154","url":null,"abstract":"<p><p>Decreased tobacco use has resulted in substantial declines in the prevalence of upper aerodigestive tract malignancies. However, the prevalence of oral cavity squamous cell carcinomas has been steadily increasing despite decreases in tobacco-related malignancies both within the United States and worldwide. The cause driving the increasing prevalence is unknown and may reflect a combination of viral, environmental, and genetic mechanisms. Radiologists must be familiar with the imaging appearance of oral cavity carcinomas to achieve proper staging and to guide surgical and/or radiation therapy management. This article will review the emerging trends in oral cavity carcinoma, the basics of oral cavity anatomy relevant to subsites of cancer involvement, the imaging appearance of this entity, and the information critical for appropriate staging to direct surgical management, medical treatment, and/or radiation therapy. <b>Keywords:</b> Ear/Nose/Throat, Head/Neck, Tongue, Neoplasms-Primary, Oncology, CT, MR Imaging, Diagnosis, PET/CT, PET/MRI © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e250154"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145459581","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 Habitat Imaging for Noninvasive Prediction of High-Grade Prostate Cancer.","authors":"Lei Yuan, Jingliang Zhang, Lina Ma, Yuwei Xia, Ye Han, Guorui Hou, Bin Yan, Xuxia Feng, Qiang Fu, Weijun Qin, Jing Zhang, Yi Huan, Jing Ren","doi":"10.1148/rycan.240395","DOIUrl":"10.1148/rycan.240395","url":null,"abstract":"<p><p>Purpose To evaluate the ability of habitat imaging to noninvasively assess high-grade prostate cancer (PCa). Materials and Methods This retrospective, multicenter study included patients with PCa undergoing MRI examination and subsequent radical prostatectomy (RP) between January 2018 and June 2024. Following the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into low- to medium-grade (ISUP ≤ 3) and high-grade (ISUP ≥ 4) groups, using RP results as the reference. After integrating multimodal imaging data of each voxel, lesions were clustered into <i>k</i> habitat subregions. RP specimens were matched to these subregions, and each subregion's ISUP grade was evaluated to calculate the detection rate of high-grade lesions. Logistic regression identified high-grade PCa-related variables, forming the habitat imaging-clinical imaging (HICI) predictive model. The model's performance was validated using the area under the receiver operating characteristic curve (AUC). Results This study enrolled 359 male patients with PCa (median age, 68 years) divided into training (159 patients), internal test (69 patients), and external test (131 patients) sets. Habitat 1, which featured high cellular density, blood perfusion, and tissue structural complexity, showed a 92.6% (87 of 94) detection rate for high-grade PCa. Logistic regression identified the proportion of habitat 1 (odds ratio [OR], 3.18; <i>P</i> < .001), the prostate-specific antigen level (OR, 2.71; <i>P</i> = .004), and the Prostate Imaging Reporting and Data System score (OR, 1.69; <i>P</i> = .04) as independent risk factors. The HICI model (AUC, 0.87) outperformed the clinical imaging model (AUC, 0.81; <i>P</i> = .01). Conclusion The HICI model can noninvasively assess high-grade PCa. <b>Keywords:</b> MR-Diffusion Weighted Imaging, Prostate, MR-Imaging <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e240395"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145422524","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":"Embedding Sustainability into the Imaging and Care of Patients with Cancer.","authors":"Benjamin E Northrup, Kate Hanneman, Katie Lichter, Andrea Rockall, Beth Zigmund, Gennaro D'Anna, Zhuoli Zhang, Joseph R Osborne, Genevieve S Silva, Kathleen Waeldner, Reed A Omary","doi":"10.1148/rycan.250054","DOIUrl":"10.1148/rycan.250054","url":null,"abstract":"<p><p>As the consequences of climate change have become increasingly evident and the environmental impact of cancer care continues to grow, there is a clear need for practice guidelines that integrate sustainability into the radiologic assessment and management of cancer. The use of imaging and image-guided procedures in cancer care has expanded substantially, contributing to improved patient outcomes, but also increased emissions and waste. This review examines the current environmental impact of cancer imaging and image-guided therapy, outlines a vision for sustainable cancer care, and proposes actionable steps to achieve a future that co-benefits patients and the planet. <b>Keywords:</b> Sustainability, Environmental Equity, Climate Resilience, Green Labs <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e250054"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145275672","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 Intra- and Peritumoral Heterogeneity in Hepatocellular Carcinoma for Microvascular Invasion Prediction and Prognostic Risk Stratification.","authors":"Yunfei Zhang, Shutong Wang, Mingyue Song, Ruofan Sheng, Zhijun Geng, Weiguo Zhang, Mengsu Zeng","doi":"10.1148/rycan.250066","DOIUrl":"10.1148/rycan.250066","url":null,"abstract":"<p><p>Purpose To evaluate an MRI-based strategy for quantifying intra- and peritumoral heterogeneity (ITH and PTH) in hepatocellular carcinoma (HCC) and develop ITH- and PTH-based models for diagnosing microvascular invasion (MVI) and stratifying prognostic risk. Materials and Methods Patients with HCC (≤5 cm) were retrospectively included from three different institutions from March 2012 to September 2023 and divided into internal training, internal testing, and external testing cohorts. Tumor and peritumoral tissues in MR images were categorized into distinct habitats using unsupervised clustering algorithms. High-throughput radiomic features were extracted from each habitat. The degree of feature variation within each habitat was quantified to derive characteristics representing ITH and PTH. Engineered features were developed to train machine learning models for MVI diagnosis. Kaplan-Meier survival curves and Cox regression analysis were used for survival analysis. Results A total of 432 patients (mean age, 54.31 years ± 11.15 [SD]; 371 male) were included. The TH_DNN model, constructed using ITH- and PTH-based quantitative features combined with a deep neural network (DNN), demonstrated the best predictive performance for MVI across the three datasets (area under the receiver operating characteristic curve range = 0.82-0.99). The subgroup predicted as MVI positive with the TH_DNN model exhibited a poorer prognosis than the MVI-negative subgroup. In terms of overall survival and postoperative recurrence, the hazard ratios for MVI diagnosis were 2.79 (95% CI: 1.35, 5.75; <i>P</i> = .006) and 2.17 (95% CI: 1.38, 3.43; <i>P</i> < .001), respectively. Conclusion This study developed a strategy for quantifying ITH and PTH, which was valuable for noninvasive and accurate identification of MVI and prognostic risk in patients with HCC. <b>Keywords:</b> Liver, MRI, Oncology, Hepatocellular Carcinoma, Microvascular Invasion, Tumor habitat, Intratumoral Heterogeneity, Peritumoral Heterogeneity <i>Supplemental material is available for this article.</i> © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e250066"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145355916","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":"Quantitative MRI Assessment of Bone Marrow Disease in Myelofibrosis: A Prospective Study.","authors":"Tanner H Robison, Annabel Levinson, Winston Lee, Kristen Pettit, Dariya Malyarenko, Malathi Kandarpa, Timothy D Johnson, Thomas L Chenevert, Brian D Ross, Moshe Talpaz, Gary D Luker","doi":"10.1148/rycan.240501","DOIUrl":"10.1148/rycan.240501","url":null,"abstract":"<p><p>Purpose To evaluate quantitative MRI parameters for assessing bone marrow composition and fibrosis in individuals with myelofibrosis (MF), as a noninvasive alternative to biopsy. Materials and Methods This prospective, single-site study (ClinicalTrials.gov identifier no. NCT01973881) included participants with MF and with non-MF myeloproliferative neoplasms (MPNs) and healthy controls who underwent MRI scans from November 2016 to January 2024. Different MRI sequences assessed fat content (proton density fat fraction), cellularity (apparent diffusion coefficient, ADC), and cellularity/macromolecular structure (magnetization transfer ratio, MTR) across lumbar vertebrae, ilium, and femoral heads. The authors used linear discriminant analysis to classify the extent of bone marrow fibrosis for each participant based on ADC values. Results This study included 66 participants (45 with MF and 15 with other MPNs [34 female] and six healthy controls (four male)]. The median age was 63 years among participants with MF and other MPNs and 62 years among healthy controls. Participants in the MF subgroup showed elevated ADCs and MTRs with lower bone marrow fat than healthy controls. Individual bone marrow MRI metrics generally correlated across anatomic sites (Pearson <i>r</i> = 0.57-0.89). ADC in the ilium showed the highest correlation with pathologic grade of bone marrow fibrosis (Kendall τ<i>_B</i> = 0.44, <i>P</i> = .01). ADC values near the linear discriminant analysis threshold in two to three anatomic sites correlated with increased risk of overt bone marrow fibrosis (odds ratio = 5.81, <i>P</i> = .01). Conclusion Quantitative bone marrow MRI parameters, particularly ADC, correlated with bone marrow fibrosis and disease severity in MF. <b>Keywords:</b> MR Imaging, Hematologic <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e240501"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145513627","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 Detection of Intrahepatic Cholangiocarcinoma with a Contrast-enhanced US-based Deep Learning Model.","authors":"WenZhen Ding, Bing Li, Ling Zhao, Lin Zheng, Xin Li, Shuaiqi Liu, Jie Yu, Ping Liang","doi":"10.1148/rycan.250078","DOIUrl":"10.1148/rycan.250078","url":null,"abstract":"<p><p>Purpose To develop a deep learning (DL) model based on contrast-enhanced US (CEUS) to help radiologists diagnose intrahepatic cholangiocarcinoma (iCCA). Materials and Methods In this retrospective study (July 2017-December 2023), CEUS examinations from 49 centers were used to train and validate a DL model using four algorithms (BNInception, MobileNet-v2, ResNet-50, and VGG-19). External test set A, collected from two independent centers, was used to evaluate model performance. External test set B, collected from 51 centers, was used to compare the DL model's performance on iCCA diagnosis with that of three CEUS radiologists and one MRI radiologist and to assess the effect of DL-assisted interpretation on radiologist performance. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results A total of 1148 CEUS examinations were divided into training (<i>n</i> = 804) and validation (<i>n</i> = 344) sets. External test sets A and B included 153 (mean age, 55.52 years ± 11.12 [SD]; 120 male patients) and 240 (mean age, 55.03 years ± 11.25; 184 male patients) CEUS examinations, respectively. Among the four evaluated algorithms, ResNet-50 achieved the best performance (AUC, 0.92) and robustness (coefficient of variation, 5.1) in external test set A. In external test set B, the DL model achieved a higher AUC than did the junior (0.91 vs 0.72, <i>P</i> < .01) and midlevel (0.91 vs 0.78, <i>P</i> < .01) CEUS radiologists and performance similar to that of the senior CEUS radiologist (0.91 vs 0.87, <i>P</i> = .32) and senior MRI radiologist (0.91 vs 0.89, <i>P</i> = .56). With DL assistance, diagnostic performance of the junior and midlevel CEUS radiologists improved significantly (from 0.72 to 0.89 [<i>P</i> < .01] and from 0.78 to 0.90 [<i>P</i> < .01], respectively), reaching performance similar to that of the senior CEUS radiologist (<i>P</i> = .50 for junior radiologist and <i>P</i> = .94 for midlevel radiologist). Conclusion A CEUS-based DL model demonstrated diagnostic performance similar to that of a senior CEUS radiologist and improved the performance of junior and midlevel CEUS radiologists. <b>Keywords:</b> Applications-Ultrasound, Deep Learning, Ultrasound-Contrast, Abdomen/GI, Liver, Oncology ClinicalTrials.gov identifier no. NCT04682886 <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e250078"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145513518","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":"Two-Pattern Imaging Features of Low-Grade Vascular Neoplasia of the Liver: Insights from a Two-Center Retrospective Study.","authors":"Fiona Mankertz","doi":"10.1148/rycan.259037","DOIUrl":"10.1148/rycan.259037","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e259037"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145638012","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":"7-T MRSI Mapping of Glutamate and Glutamine in Diffuse Gliomas.","authors":"Brian J Burkett, John Port","doi":"10.1148/rycan.250464","DOIUrl":"10.1148/rycan.250464","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e250464"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145275678","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 Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy Using Bimodal US and MRI.","authors":"Xue-Yan Wang, Jia-Xin Huang, Feng-Tao Liu, Hui-Ning Huang, Jing-Si Mei, Gui-Ling Huang, Yu-Ting Zhang, Mei-Qin Xiao, Yan-Fen Xu, Ming-Jie Wei, Xiao-Qing Pei","doi":"10.1148/rycan.240493","DOIUrl":"10.1148/rycan.240493","url":null,"abstract":"<p><p>Purpose To determine whether bimodal US improves prediction of pathologic complete response following neoadjuvant chemotherapy (NAC) compared with MRI, as well as to assess the diagnostic value of a combined imaging model. Materials and Methods In this prospective two-center study, participants with primary breast cancer undergoing NAC between January 2020 and January 2024 were enrolled. Preoperative bimodal US (grayscale and shear wave) and MRI data were collected. Complete response on US (uCR) and MR images (mCR) were defined by radiologists. Diagnostic models based on uCR, mCR, and their combination were evaluated using postsurgical pathology as the reference standard. Pathologic complete response was defined as no residual tumor (ypT0) or no invasive cancer with possible ductal carcinoma in situ (ypT0/Tis). Model performance was evaluated by area under the receiver operating characteristic curve (AUC), with sensitivity, specificity, positive and negative likelihood ratios, and comparisons by the DeLong test. All tests were two-sided (<i>P</i> < .05). Results A total of 224 female participants (median age, 46 years; IQR, 38.75-56) with breast cancer undergoing NAC were included. Overall, 82 of 224 (37%) achieved ypT0/Tis, and 62 of 224 (28%) achieved ypT0. Using ypT0/Tis as the pathologic complete response standard, the uCR model achieved an AUC of 0.76 (95% CI: 0.67, 0.83), while the mCR model achieved an AUC of 0.80 (95% CI: 0.71, 0.87). Using ypT0, the uCR model achieved an AUC of 0.79 (95% CI: 0.70, 0.86), and the mCR model an AUC of 0.75 (95% CI: 0.65, 0.82) in the test set. The combined imaging model (ypT0/Tis, AUC = 0.87; ypT0, AUC = 0.87) outperformed both the uCR (ypT0/Tis, <i>P</i> = .002; ypT0, <i>P</i> = .002) and mCR models (ypT0/Tis, <i>P</i> = .04; ypT0, <i>P</i> = .004). Conclusion Bimodal US effectively predicted pathologic response to NAC in breast cancer with accuracy comparable to MRI. A combined US/MRI model demonstrated higher diagnostic performance than either modality alone. <b>Keywords:</b> Breast, Ultrasound Chinese Clinical Trial Registry (ChiCTR2400085035) <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Horvat and Fazzio in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 6","pages":"e240493"},"PeriodicalIF":5.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145308805","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}