Radiology. Imaging cancer最新文献

{"title":"AI-based Triage in Breast Cancer Screening Reduces Workload with Noninferior Cancer Detection but Higher Recall.","authors":"Rebecca Breen, Maggie Chung","doi":"10.1148/rycan.269018","DOIUrl":"10.1148/rycan.269018","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e269018"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631386","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":"Preoperative MRI Model for Predicting Inferior Vena Cava Wall Invasion in Renal Cell Carcinoma with Tumor Thrombus.","authors":"Xue-Wei Wen, Shao-Peng Zhou, Huan-Huan Kang, Jian Zhao, Xu Bai, Mei-Feng Wang, Yi-Bo Chen, Si-Cheng Yi, Chao-Bo Li, Xiao-Hui Ding, Qing-Bo Huang, Hai-Yi Wang","doi":"10.1148/rycan.250648","DOIUrl":"10.1148/rycan.250648","url":null,"abstract":"<p><p>Purpose To develop and evaluate a preoperative MRI-based model for predicting inferior vena cava (IVC) wall invasion in renal cell carcinoma (RCC) with IVC tumor thrombus (IVCTT) and to compare its performance with that of individual MRI features and radiologists' subjective assessments. Materials and Methods This single-center study with retrospective and prospective components included individuals who underwent or were scheduled to undergo surgery for RCC with IVCTT (retrospective training set, <i>n</i> = 173, January 2005-December 2023; prospective temporal validation set, <i>n</i> = 44, January 2024-September 2025). Histopathology served as the reference standard. Quantitative (tumor, vessel, and thrombus measurements) and qualitative (signal and morphologic characteristics) MRI features were assessed. Two fellowship-trained abdominal radiologists independently provided subjective assessments of IVC wall invasion, and interobserver agreement was assessed. Variables significant at univariable analysis were entered into multivariable logistic regression to identify predictors of IVC wall invasion. Diagnostic performance was compared using receiver operating characteristic (ROC) curve analysis and DeLong tests. Results A total of 217 individuals were included (mean age, 57 years ± 12 [SD], 166 male). Four independent predictors of IVC wall invasion were identified: bland thrombus (odds ratio [OR] = 3.32 [95% CI: 1.38, 8.03]), lumbar vein diameter (>5.25 mm) (OR = 2.64 [95% CI: 1.23, 5.69]), ipsilateral renal vein ostium diameter (>19.20 mm) (OR = 3.64 [95% CI: 1.73, 7.63]), and thrombus craniocaudal length (>46.95 mm) (OR = 3.08 [95% CI: 1.43, 6.63]). The multivariable model incorporating these predictors achieved area under the ROC curve (AUC) values of 0.81 (95% CI: 0.75, 0.88) and 0.84 (95% CI: 0.73, 0.96) in the training and validation sets, respectively, significantly outperforming the best individual MRI predictor (AUC = 0.71) and radiologists' subjective assessments (AUC = 0.66) (all <i>P</i> < .05). Conclusion The multiparametric MRI-based model demonstrated good discriminatory performance for predicting IVC wall invasion and outperformed individual MRI features and subjective radiologist assessment. <b>Keywords:</b> MR Imaging, Urinary, Kidney, Renal Cell Carcinoma, Magnetic Resonance Imaging, Inferior Vena Cava Tumor Thrombus, Venous Wall Invasion <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250648"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272652","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":"Intratumoral Therapy with or without Pulsed Electric Field Ablation for Hepatocellular Carcinoma: A Proof-of-Concept Study.","authors":"Noreen S Siddiqi","doi":"10.1148/rycan.269017","DOIUrl":"10.1148/rycan.269017","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e269017"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631458","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":"Cross-Cancer Circulating Proteins for the Prognosis of Primary and Metastatic Liver Cancer Treated with Local Ablation: Post Hoc Exploratory Analysis of the ESTIMATE Trial.","authors":"Luigi Nardone, Elif Öcal, Lukas Salvermoser, Matthias Philipp Fabritius, Daniel Puhr-Westerheide, Matthias Stechele, Stefanie Corradini, Jens Ricke, Moritz Wildgruber, Marianna Alunni-Fabbroni, Max Seidensticker","doi":"10.1148/rycan.250685","DOIUrl":"10.1148/rycan.250685","url":null,"abstract":"<p><p>Purpose To identify a panel based on circulating proteins to predict clinical outcomes after local ablation across patients with primary and metastatic liver cancer to improve patient selection. Materials and Methods This exploratory post hoc analysis is derived from ESTIMATE, a prospective controlled trial (DRKS registration no. 00010587) evaluating progression-free survival and overall survival (OS) after local ablation of liver malignancies between 2017 and 2023. Baseline plasma samples from patients with hepatocellular carcinoma, intrahepatic cholangiocarcinoma, or metastatic breast or colorectal cancer treated with interstitial brachytherapy were profiled using an immunoassay panel. Group comparisons used <i>t</i> tests or Mann-Whitney <i>U</i> tests. Survival was assessed using Kaplan-Meier, log-rank, and Cox regression analyses. Multivariable Cox models were adjusted for tumor type, number of lesions, and treatment response. Model discrimination was assessed using time-dependent areas under the receiver operating characteristic curves (AUCs) for OS, with bootstrap resampling used to estimate optimism-corrected performance. Results In total, 103 patients (62 male; mean age, 67.5 years; range, 36-87 years) were included. Elevated chemokine (C-C motif) ligand 20, interleukin-15, and interleukin-8 levels were associated with poor clinical outcomes across various liver tumors and were therefore incorporated into an immune-inflammatory prognostic model (all <i>P</i> < .05). AUC analysis revealed the predictive accuracy of the model for OS at 6, 12, and 18 months (AUCs: 0.70, 0.72, and 0.74, respectively), and bootstrap optimism-corrected performance ranged from 0.61 to 0.68. Conclusion The findings revealed a cross-cancer panel of circulating proteins that reliably and effectively predicts treatment response and prognosis after local ablation of liver malignancies. <b>Keywords:</b> Prognosis & Prediction, Treatment Response Assessment, Ablation Techniques, Interventional Oncology, Molecular Imaging-Clinical Translation, Molecular Imaging-Cancer, Liquid Biopsy, Abdomen/GI, Decision Analysis, Evidence-based Medicine, Outcomes Analysis, Liver Cancer, Interstitial Brachytherapy, Cross-Cancer Biomarkers, Proximity Extension Assay German Clinical Trials Register no. DRKS 00010587 <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250685"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148423516","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-assisted Radiomic Model for Cervical Cancer Recurrence Prediction: A Multicenter Retrospective Study with Experimental Validation.","authors":"Shuqing Chen, Yu Zhang, Dong Chen, Jun Dang, Lei Chen, Wei Yang","doi":"10.1148/rycan.250618","DOIUrl":"10.1148/rycan.250618","url":null,"abstract":"<p><p>Purpose To evaluate the value of a nomogram model incorporating clinical parameters, hematologic inflammatory biomarkers, and MRI radiomic features in predicting postoperative disease-free survival (DFS) in patients with cervical cancer and to explore the biologic mechanism underlying the radiomic signature. Materials and Methods This multicenter retrospective study enrolled 804 patients with cervical cancer (2016-2023), with a median follow-up of 43.7 months. Three-dimensional radiomic features were extracted from pretreatment MRI tumor and 5-mm peritumoral regions. Five machine learning algorithms were tested to construct the optimal radiomics score (Radscore). Multivariable Cox regression was used to develop the nomogram model, with bioinformatic analysis and in vitro experiments for biologic mechanism validation. Results In the clinical prediction cohort (<i>n</i> = 751; median age, 52 years [IQR, 47-58 years]), 164 patients (21.8%) experienced recurrence during follow-up. The random survival forest-based radiomics model achieved optimal predictive performance in the external test set, with 1-, 3-, and 5-year DFS areas under the receiver operating characteristic curves (AUCs) of 0.88 (95% CI: 0.83, 0.94), 0.82 (95% CI: 0.75, 0.90), and 0.89 (95% CI: 0.83, 0.95), respectively. International Federation of Gynecology and Obstetrics stage, squamous cell carcinoma antigen level, systemic inflammation response index, and Radscore were identified as independent prognostic factors for DFS, which were incorporated into the nomogram model, with 1-, 3-, and 5-year DFS AUCs of 0.93 (95% CI: 0.90, 0.97), 0.84 (95% CI: 0.78, 0.90), and 0.86 (95% CI: 0.80, 0.92) in the external test set. A radiomic signature-TRIM29-cell cycle regulatory axis was identified and validated via transcriptomic analysis and in vitro experiments. Conclusion The integrated clinical-radiomic nomogram developed in this study enables accurate noninvasive prediction of postoperative recurrence in patients with cervical cancer. <b>Keywords:</b> MRI, Machine Learning, Radiomics, Prognosis and Prediction, Cervical Cancer, Recurrence, Inflammatory Markers <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250618"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148339978","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 Mapping in Meningioma: A Clinical Perspective on Presentation and Management.","authors":"Ramona-Alexandra Todea","doi":"10.1148/rycan.260299","DOIUrl":"10.1148/rycan.260299","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e260299"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148163786","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":"Automated Apparent Diffusion Coefficient Measurement of Breast Tumors in Diffusion-weighted MRI for Early Neoadjuvant Chemotherapy Response Assessment.","authors":"Nu N Le, Wen Li, Lisa J Wilmes, Natsuko Onishi, Jessica E Gibbs, Deep Hathi, Pouya Metanat, Bonnie N Joe, John Kornak, Dariya Malyarenko, Thomas L Chenevert, Patrick Bolan, Savannah C Partridge, Nola M Hylton","doi":"10.1148/rycan.250606","DOIUrl":"10.1148/rycan.250606","url":null,"abstract":"<p><p>Purpose To develop a fully automated method for measuring apparent diffusion coefficient (ADC) for breast tumors in diffusion-weighted MRI that is objective, repeatable, and reproducible for assessing early response to neoadjuvant chemotherapy. Materials and Methods This study was a retrospective analysis of American College of Radiology Imaging Network 6698 trial data (August 2012-January 2015). Regions of interest (ROIs) were automatically derived by transferring the tumor ROI from dynamic contrast-enhanced MRI to diffusion-weighted MRI via image registration, and the ΔADC were calculated from baseline to early treatment. The ΔADC performance was assessed for predicting a pathologic complete response (pCR) using receiver operating characteristic curve analysis. The analysis was performed in all participants and in subgroups defined by tumor human epidermal growth factor receptor 2 (HER2) status. The reproducibility of automated tumor ADC measurements was assessed in a test-retest subcohort. Results The analysis cohort included 226 participants with breast cancer (mean age, 48 years ± 10 [SD]). Automated ADC measurements were reproducible in the test-retest subcohort (<i>n</i> = 71; estimated agreement index, 0.82 [95% CI: 0.77, 0.85]). The ΔADC from the automated ROI predicted pCR, with an area under the receiver operating characteristic curve (AUC) of 0.61 (95% CI: 0.53, 0.69; <i>P</i> = .008). The AUC in the HER2-positive subcohort (0.69 [95% CI: 0.54, 0.93]) was higher than that in the HER2-negative subcohort (0.52 [95% CI: 0.41, 0.62]; <i>P</i> = .06). Conclusion The automated ADC measurement method was reproducible, and tumor ΔADC could predict pCR early. External validation will be included in future work. <b>Keywords:</b> Breast, MRI, DWI, Diffusion-weighted Imaging, Tumor Response, DCE, Dynamic Contrast-enhanced, MR Imaging, MR-Diffusion-weighted Imaging, MR-Dynamic Contrast-enhanced Clinical trial registration no. NCT01564368</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250606"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148579760","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":"Time-dependent Diffusion MRI-based Microstructural Mapping Value for Predicting Meningioma Grade, Subtype, and Proliferative Activity.","authors":"Hua Zhang, Dongmei Jiang, Junhuan Hong, Yuwei Pan, Minxuan Tian, Guoqi Lin, Lukui Xiong, Xiance Zhao, Dejun She","doi":"10.1148/rycan.250526","DOIUrl":"10.1148/rycan.250526","url":null,"abstract":"<p><p>Purpose To explore the diagnostic performance of time-dependent diffusion MRI (t_{<i>_d</i>}-dMRI) combined with histogram analysis in predicting meningioma grade, subtype, and proliferative activity. Materials and Methods A total of 107 participants were prospectively enrolled (mean age ± SD, 55.7 years ± 10.2; 78 female) with meningiomas who underwent presurgical t<i>_d</i>-dMRI between August 2023 and July 2025. Histogram features from t_<i>d</i>-dMRI-derived parameters and semantic features from conventional MRI (cMRI) were estimated. Area under the receiver operating characteristic curve (AUC) and DeLong and integrated discrimination improvement (IDI) tests were used to evaluate model performance. Spearman rank correlations between t<i>_d</i>-dMRI metrics and the Ki-67 index were evaluated. Results In meningioma grading, the cMRI-t_<i>d</i>-dMRI combined model was superior to cMRI and single-parameter models (AUC, 0.86 [95% CI: 0.78, 0.92] vs 0.76 [95% CI: 0.67, 0.84] vs 0.64-0.71; corrected <i>P</i> = .009, .002-.009) and similar to the t_<i>d</i>-dMRI model (AUC, 0.83 [95% CI: 0.74, 0.89]; corrected <i>P</i> = .47). The combined model achieved a higher IDI than the cMRI and single-parameter models (0.16-0.27, all corrected <i>P</i> < .001). In meningioma subtyping, the combined model achieved the highest diagnostic performance (AUC, 0.86 [95% CI: 0.77, 0.93]) and a higher IDI than cMRI and single-parameter models (AUC, 0.14-0.31, all corrected <i>P</i> < .01). Weak correlations were observed between cell diameter, intracellular volume fraction, and cellularity and the Ki-67 index (<i>r</i> = -0.231 to -0.194 and 0.203-0.342; all <i>P</i> < .05). Conclusion The use of t_<i>d</i>-dMRI combined with histogram analysis performed well in assessing meningioma grade, subtype, and proliferative activity. <b>Keywords:</b> CNS, MRI, MR-Diffusion Weighted Imaging, Time-dependent Diffusion MRI, Meningioma, Histogram Analysis, World Health Organization Grading, Histological Subtyping <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250526"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148472394","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":"Deciphering Colorectal Liver Metastasis Perfusion Heterogeneity: Identification of Distinct Vascular Phenotypes through High-Lateral-Resolution Photoacoustic Microscopy.","authors":"Kuanhong Wang, Chenyi Xie, Manqi Wu, Shihan Kang, Kedi Xiong, Yingying Guo, Xue Wei, Hengxiao Hu, Yajun Liu, Jingfan Zhan, Yulu Hou, Zaiyi Liu, Ting Guo, Xin Chen","doi":"10.1148/rycan.250523","DOIUrl":"10.1148/rycan.250523","url":null,"abstract":"<p><p>Purpose To develop a photoacoustic microscopy (PAM) method for classifying colorectal liver metastasis (CRLM) microvascular networks as angiogenesis-type or vessel co-option-type at different metastatic stages and to evaluate their responses to bevacizumab. Materials and Methods Seventy-two BALB/c nude mice received intrasplenic injections of HCT116-luc or HT29-luc cells to establish CRLM models. PAM was used to visualize CRLM vascular architecture, and its diagnostic accuracy was validated through histopathologic analysis. Intergroup differences in PAM parameters were assessed with one-way analysis of variance or independent-samples <i>t</i> tests. Results PAM differentiated angiogenesis-type from vessel-co-option-type CRLMs according to distinct vascular architectures: Angiogenesis-type CRLMs exhibited lower vessel density (15.66% ± 4.38 vs 31.44% ± 6.95; <i>P</i> < .001) and vessel junction density (1.19 × 10^-3 n/px² ± 0.52 vs 3.12 × 10^-3 n/px² ± 1.11; <i>P</i> = .001) and higher vessel diameter (31.45 µm ± 4.59 vs 13.68 µm ± 1.61; <i>P</i> < .001) and lacunarity (0.294 arbitrary units [au] ± 0.095 vs 0.148 au ± 0.059; <i>P</i> = .003) than vessel co-option-type CRLMs. These differences persisted throughout the 8-week progression period. A 33.3% reduction in vessel density at 3 weeks following bevacizumab treatment was observed in angiogenesis-type CRLMs (untreated, 15.66% ± 4.38 vs treated at 3 weeks, 10.44% ± 4.91; <i>P</i> = .047), with no evidence of a difference in vessel co-option-type CRLMs (untreated, 31.44% ± 6.95 vs treated at 3 weeks, 28.82% ± 3.56; <i>P</i> = .56). Conclusion PAM quantified microvascular signatures differentiating CRLM blood supply patterns and identified vessel co-option-type metastases as resistant to bevacizumab. <b>Keywords:</b> Angiogenesis, Bevacizumab, Colorectal Cancer, Histopathologic Growth Pattern, Liver Metastases, Photoacoustic Imaging, Photoacoustic Microscopy, Vessel Co-Option <i>Supplemental material is available for this article.</i> © RSNA, 2026 See also editorial by Wang and Chen in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250523"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148423633","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":"Melorheostosis Incidentally Detected during Staging of Colon Carcinoma.","authors":"Priyanka Kallagotru Sheshadri, Tej Pal, Sanjay Kumar Meena, Amit Gupta, Sanjay Thulkar","doi":"10.1148/rycan.250807","DOIUrl":"10.1148/rycan.250807","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 4","pages":"e250807"},"PeriodicalIF":7.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148272675","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}