Radiology. Imaging cancer最新文献

{"title":"New Standardized PSMA PET Reporting Framework for Long-Term Survival Prediction in Prostate Cancer.","authors":"Farzana Z Ali","doi":"10.1148/rycan.269012","DOIUrl":"10.1148/rycan.269012","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269012"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988849","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 Deformable Registration and Three-dimensional Margin Assessment for Predicting Local Recurrence after Lung Thermal Ablation.","authors":"Krishna Nand Keshavamurthy, Robert Salkin, Anirudha Shastri, Vlasios Sotirchos, Constantinos T Sofocleous, Ruben Geevarghese, Erica Alexander, Stephen B Solomon, Etay Ziv","doi":"10.1148/rycan.250501","DOIUrl":"10.1148/rycan.250501","url":null,"abstract":"<p><p>Purpose To develop a lung-specific deformable image registration algorithm optimized for lung thermal ablation and evaluate whether three-dimensional (3D) margin assessment predicts time to local recurrence. Materials and Methods This institutional review board-approved, single-institution retrospective study evaluated patients who underwent lung thermal ablation with available pre- and postprocedural CT scans suitable for deformable registration. Images were preprocessed with segmentation of tumor, ablation zone, and lung. A four-stage deformable image registration framework was applied: <i>(a)</i> affine registration, <i>(b)</i> deformable image registration to the cropped lung, <i>(c)</i> lung mask-guided deformable image registration, and <i>(d)</i> local deformable image registration focused on the neighborhood adjacent to the ablation zone. Registrations were performed using free-form B-spline transformations with cost function masking of the ablation zone. Registration accuracy was assessed using target registration error (TRE). The 3D ablation margins were quantified using a distance-transform-based analysis of the spatial relationship between the tumor surface and ablation zone boundary. Associations between margin size and time to local recurrence were evaluated using competing-risks regression, and time-dependent receiver operating characteristic analysis was performed. Results A total of 69 patients (median age, 59 years [IQR, 50-69 years]; 38 female) with 108 ablated lung tumors were included. Mean TRE ± SD was 0.4 mm ± 0.3 and mean ablation margin was 1.6 mm ± 2.1. Larger margins were associated with longer time to local recurrence (subdistribution hazard ratio, 0.5 per millimeter increase [95% CI: 0.4, 0.6]; <i>P</i> < .001) and remained independently associated on multivariate analysis. Using a 2-mm margin threshold, the 2-year local recurrence rate was 3% (95% CI: 1, 8). The area under the receiver operating characteristic curve for predicting 2-year local recurrence was 0.86. Conclusion The four-step lung-optimized deformable image registration framework enabled accurate automated 3D tumor ablation margin quantification, and margin size was associated with time to local recurrence. <b>Keywords:</b> Ablation Techniques, Interventional-Oncology, Percutaneous, Thorax, Lung, Computer Applications-3D, Computational Studies, CT © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250501"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147646321","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-based Multiple Arterial Phase MRI: A Step toward Improved Hepatocellular Carcinoma Diagnosis.","authors":"Mengchao Zhang","doi":"10.1148/rycan.260156","DOIUrl":"10.1148/rycan.260156","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260156"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779386","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":"Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.","authors":"Jasper J Twilt, Anindo Saha, Joeran S Bosma, Gianluca Giannarini, Anwar R Padhani, Derya Yakar, Mattijs Elschot, Jeroen Veltman, Jurgen Fütterer, Henkjan Huisman, Maarten de Rooij","doi":"10.1148/rycan.250461","DOIUrl":"10.1148/rycan.250461","url":null,"abstract":"<p><p>Purpose To simulate an artificial intelligence (AI)-driven triaging workflow in which an AI system, using high-confidence thresholds, assesses a subset of prostate MRI examinations for clinically significant prostate cancer (csPCa), compare the assessment with stand-alone radiologists, and evaluate the number of examinations triaged by the AI to estimate potential workload reduction. Materials and Methods Data from an international AI confirmatory study (February 2022-November 2023) were used in this retrospective study. MRI examinations of 500 men with suspected csPCa from four European centers were included. Exclusion criteria were prior prostate treatment, prior csPCa, or considerable imaging artifacts. AI-triaging thresholds were calibrated on 100 examinations. The AI system assessed examinations exceeding high-specificity or high-sensitivity thresholds, with the remaining examinations deferred to radiologists. The workflow was simulated on 400 examinations, including examinations from an external site, incorporating assessments from 62 radiologists. Reference standards were histopathology and/or 3 or more years of follow-up. Sensitivity and specificity of the triaging workflow were compared with the conventional workflow using multireader, multicase analysis of variance. Results Among the 400 patients (median age, 66 years; IQR, 60-69 years) included for testing, radiologists achieved a sensitivity of 89.4% (95% CI: 85.8, 93.1) and specificity of 57.7% (95% CI: 52.3, 63.1). The AI-driven pathway maintained comparable sensitivity (89.0%; 95% CI: 85.0, 93.0; <i>P</i> = .36) but improved specificity by 11.5%, reaching 69.2% (95% CI: 64.4, 74.0; <i>P</i> < .001). The AI system triaged and diagnosed 195 of 400 (49%; 95% CI: 173, 216) examinations with sensitivity of 94.7% (95% CI: 89.5, 99.9) and specificity of 94.7% (95% CI: 90.5, 98.9). Conclusion Triaging by this AI system improved simulated diagnostic workflow efficiency without compromising diagnostic accuracy for csPCa. <b>Keywords:</b> Prostate, MRI, Localization, Oncology, Comparative Studies, Diagnosis <i>Supplemental material is available for this article.</i> ClinicalTrials.gov registration no. NCT05489341 © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250461"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147646334","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":"Artificial Intelligence as a Second Reader in a Simulation Study of Population-based Mammography Screening in the Netherlands.","authors":"Dillon Yeh, Maggie Chung","doi":"10.1148/rycan.269009","DOIUrl":"10.1148/rycan.269009","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269009"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779372","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":"Multiparametric MRI-based Deep Learning and Radiomics for Evaluating Lymph Node Metastasis in Early-Stage Cervical Cancer.","authors":"Yu Hao Bao, Mei Ling Xiao, Yong Ai Li, Feng Hua Ma, Yan Chen, Zi Jing Lin, Jing Yan Wu, Guo Fu Zhang, Jin Wei Qiang","doi":"10.1148/rycan.250345","DOIUrl":"10.1148/rycan.250345","url":null,"abstract":"<p><p>Purpose To develop a multiparametric MRI-based radiomics model and deep learning-radiomics (DLR) fusion model for preoperative prediction of lymph node metastasis (LNM) in early-stage cervical cancer. Materials and Methods In this multicenter retrospective study (January 2020-December 2022), preoperative MRI data from patients with early-stage cervical cancer were split into training, internal testing, and external testing cohorts. Radiomic and deep learning (DL) features of both the tumor and lymph node were extracted separately from the MRI scans. Multivariable logistic regression was used to construct predictive models for LNM based on tumor and lymph node radiomic features (Rad_T+LN) and based on radiomic and DL features from both the tumor and lymph node (DLR_T+LN). The models' effectiveness and clinical applicability were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. A two-tailed <i>P</i> value of <.05 was considered statistically significant. Results The overall dataset included 862 patients (median age, 53 years [IQR, 45-60 years]). Rad_T+LN resulted in areas under the receiver operating characteristic curve (AUCs) of 0.81 (95% CI: 0.76, 0.86), 0.79 (95% CI: 0.72, 0.87), and 0.77 (95% CI: 0.71, 0.82) in the training, internal testing, and external testing cohorts, respectively. DLR_T+LN achieved AUCs of 0.83 (95% CI: 0.76, 0.91) and 0.79 (95% CI: 0.74, 0.84) in the internal and external testing cohorts, respectively, and did not improve over Rad_T+LN (<i>P</i> > .05). Both models demonstrated good calibration and positive net benefit on decision curve analysis. Conclusion Rad_T+LN and DLR_T+LN exhibited robust diagnostic performance for LNM prediction. <b>Keywords:</b> MR-Diffusion Weighted Imaging, MR Imaging, Genital/Reproductive, Cervix, Metastases, Decision Analysis, Segmentation, Radiomics, Diagnosis, Uterine Cervical Neoplasms, Lymphatic Metastasis, Magnetic Resonance Imaging, Deep Learning <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250345"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609999","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":"Advancing Lung Cancer Imaging with Self-Supervised Foundation Models.","authors":"Diego Varona Porres","doi":"10.1148/rycan.260078","DOIUrl":"10.1148/rycan.260078","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260078"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609966","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":"Biologically Guided Radiotherapy with Dual-Tracer PET in Head and Neck Cancer.","authors":"Ida Azizkhanian","doi":"10.1148/rycan.269013","DOIUrl":"10.1148/rycan.269013","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269013"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988852","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":"Radiomics-based Differentiation of Recurrent Brain Metastases from Treatment Effects: A Multi-Institutional Comparative Study with Advanced Imaging.","authors":"Hyemin Um, Marwa Ismail, Virginia B Hill, Sushant Puri, Jennifer S Yu, Lan Lu, Ameya P Nayate, Anthony Higinbotham, Lisa R Rogers, Prateek Prasanna, Mainak Bardhan, Chengnan Li, Mustafa M Basree, Andrew M Baschnagel, Alan B McMillan, Ankush Bhatia, Manmeet S Ahluwalia, Michael C Veronesi, Pallavi Tiwari","doi":"10.1148/rycan.250435","DOIUrl":"10.1148/rycan.250435","url":null,"abstract":"<p><p>Purpose To compare the ability of radiomics, advanced imaging modalities, and Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria to distinguish tumor recurrence from treatment effects in patients with brain metastasis. Materials and Methods Posttreatment MRI (gadolinium-enhanced T1-weighted, T2-weighted, fluid-attenuated inversion recovery [FLAIR]) data of patients from three institutions, obtained between January 2001 and March 2023, were retrospectively analyzed. Reference standards were established by histopathology or according to clinical and imaging courses. Following preprocessing and segmenting tumors into subcompartments of enhancing lesion, edema, or necrotic core, 1104 radiomic features were extracted, and classification was conducted using random forest models. A comparative assessment with advanced imaging and RANO-BM was performed using a subset of studies with available modalities (dynamic susceptibility contrast MRI, fluorine 18 [¹⁸F] fluorodeoxyglucose [FDG] PET/MRI, ¹⁸F-FDG PET/CT, and longitudinal gadolinium-enhanced T1-weighted MRI). Statistical analyses were performed using the McNemar test. Results The study included data from 242 patients (mean age, 59 years ± 11.5; 114 female patients). Models including T2-weighted MRI features from the enhancing lesion, FLAIR features from edema, and FLAIR features from necrotic core subcompartments yielded accuracies of 70.6%, 71.4%, and 72.0%, respectively. The average accuracies for the enhancing lesion, edema, and necrotic subcompartments across different study sites were 68.9%, 67.9%, and 70.7%, respectively. In comparative subset analyses, radiomics outperformed other techniques in distinguishing tumor recurrence from treatment effects (eg, 76.5% vs 39.2% accuracy using RANO-BM, <i>P</i> < .001). Conclusion Radiomics outperformed RANO-BM criteria and advanced imaging modalities in differentiating tumor recurrence from treatment effects, with consistently higher accuracies across lesion subcompartments. <b>Keywords:</b> Brain Metastases, Treatment Effects, Radiomics, RANO-BM, Advanced Imaging <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250435"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717882","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":"Methodologic Considerations in Whole-Body Diffusion-weighted MRI for Pediatric Sarcoma Staging.","authors":"Kojiro Onohara, Jiro Ichikawa","doi":"10.1148/rycan.260168","DOIUrl":"10.1148/rycan.260168","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e260168"},"PeriodicalIF":7.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13234773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988872","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}