Radiology. Imaging cancer最新文献

{"title":"Time-dependent Diffusion MRI for Predicting Response to Induction Chemotherapy in Nasopharyngeal Carcinoma.","authors":"Huanhuan Ren, Diwei Shi, Xiaoxia Wang, Qian Xu, Yao Huang, Haojie Song, Junhao Huang, Xinying Ren, Xiaosong Lan, Yong Tan, Hong Yu, Lisha Nie, Daihong Liu, Jiuquan Zhang","doi":"10.1148/rycan.250579","DOIUrl":"https://doi.org/10.1148/rycan.250579","url":null,"abstract":"<p><p>Purpose To validate the reliability of time-dependent diffusion MRI (td-dMRI)-derived indicators in nasopharyngeal carcinoma (NPC) and determine whether microstructural parameters derived from td-dMRI can help noninvasively predict response to induction chemotherapy. Materials and Methods In this prospective study, participants with locally advanced NPC underwent pretreatment td-dMRI between December 2023 and February 2025. The data were randomly stratified and divided into a training set and an internal test set at a 7:3 ratio. According to Response Evaluation Criteria in Solid Tumors 1.1, all participants were classified as responders or nonresponders. Four imaging microstructural parameters with the limited spectrally edited diffusion-derived microstructural parameters (cell diameter <i>d</i>, cellularity, intracellular volume fraction <i>f</i>_in, and extracellular diffusivity <i>D</i>_ex) and three apparent diffusion coefficient (ADC) values (the ADC with the pulsed gradient spin-echo sequence, the ADC at 20 Hz, and the ADC at 40 Hz) were calculated. Repeatability was evaluated. MRI-derived parameters were compared with pathologic microstructural metrics. In the training set, univariable and multivariable logistic regression analyses were performed to identify parameters associated with induction chemotherapy response; predictive models were constructed. Results In total, 220 participants with NPC were enrolled (median age, 53.0 years [IQR, 48.8-58.0 years]; 155 male participants), including 154 in the training set and 66 in the internal test set. Participants were divided into responders (<i>n</i> = 151) and nonresponders (<i>n</i> = 69). The repeatability study revealed within-subject coefficient of variation values of 4.56%-10.15% and repeatability coefficient values of 0.12-1.71. Moreover, the td-dMRI-derived parameters correlated well with pathologic measurements (<i>r</i> = 0.27-0.67; <i>P</i> < .05). In the training set, univariable and multivariable logistic regression analyses revealed that the tumor-stroma ratio (TSR) and td-dMRI-derived cellularity were independent predictors. The combined model integrating the TSR and cellularity achieved an AUC of 0.82 (95% CI: 0.75, 0.89) in the training set and 0.76 (95% CI: 0.63, 0.87) in the internal test set. Conclusion td-dMRI-derived microstructural parameters, particularly TSR and tumor cellularity, showed good performance in predicting response to induction therapy in individuals with NPC. <b>Keywords:</b> Nasopharyngeal Carcinoma, Response, MRI, Induction Chemotherapy <i>Supplemental material is available for this article.</i> © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250579"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842001","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":"CT-based Extranodal Extension for Nodal Staging and Prognostic Stratification in Surgically Resectable Esophageal Squamous Cell Carcinoma.","authors":"Xiaowen Xie, Ziyu Ning, Hengxiao Hu, Jiahui Chen, Yihuai Hu, Lujun Han, Ting Lin, Yunshi Liang, Kuanhong Wang, Yingying Guo, Yajun Liu, Jingfan Zhan, Yulu Hou, Xin Chen, Chenyi Xie, Lisha Lai","doi":"10.1148/rycan.250562","DOIUrl":"https://doi.org/10.1148/rycan.250562","url":null,"abstract":"<p><p>Purpose To develop and validate a structured CT-based grading system for imaging-identified extranodal extension (iENE) and assess its diagnostic accuracy and prognostic significance in esophageal squamous cell carcinoma. Materials and Methods In this retrospective multicenter study, preoperative CT examinations in patients with esophageal squamous cell carcinoma treated at four tertiary hospitals between January 2017 and December 2023 were reviewed. CT features evaluated included indistinct nodal margins, coalescent nodal mass, invasive encasement, halo sign, and black ring sign. Radiologic-pathologic node-by-node correlation was performed in the development cohort to assess diagnostic performance. The validation cohort consisted of an independent, multicenter patient population and was used to evaluate the prognostic value of iENE at the patient level. Logistic regression and receiver operating characteristic analyses were used to assess diagnostic performance, while overall survival was evaluated using Kaplan-Meier and Cox regression analyses. Results A total of 912 patients with esophageal squamous cell carcinoma were included, with 95 patients (mean age, 62 years ± 8; 75 male patients) in the development cohort and 817 patients (mean age, 62 years ± 9; 619 male patients) in the validation cohort. Coalescent nodal mass, invasive encasement, and black ring sign at CT demonstrated 100% specificity for pathologic ENE. An iENE score of 2 or higher achieved optimal diagnostic performance (sensitivity, 82% [36 of 44]; specificity, 90% [250 of 279]; accuracy, 89% [286 of 323]). Patients with high-risk iENE (score ≥2) had worse overall survival than those with low-risk disease in both the development cohort (hazard ratio, 8.83; 95% CI: 3.30, 23.64; <i>P</i> < .001) and validation cohort (hazard ratio range, 26.02-39.48; all <i>P</i> < .001). On multivariable analysis, iENE remained an independent prognostic factor (adjusted hazard ratio, 8.72; 95% CI: 6.88, 11.05; <i>P</i> < .001). Conclusion The CT-based iENE grading system demonstrated high diagnostic performance and provided independent prognostic stratification in esophageal squamous cell carcinoma. <b>Keywords:</b> CT, Abdomen/GI, Outcomes Analysis, Decision Analysis <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e250562"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842006","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":"Diagnostic Performance and Reliability of RADS Classification Systems for Solitary Bone Lesions.","authors":"Netanja I Harlianto","doi":"10.1148/rycan.269010","DOIUrl":"https://doi.org/10.1148/rycan.269010","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 3","pages":"e269010"},"PeriodicalIF":5.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147779429","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":"Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications.","authors":"Annie Singh, Fatima Ahmad Qureshy, Angelica Kurtz, Moinak Bhattacharya, Prateek Prasanna, Gagandeep Singh","doi":"10.1148/rycan.250567","DOIUrl":"10.1148/rycan.250567","url":null,"abstract":"<p><p>Purpose To perform a systematic review evaluating current digital twin (DT) implementations, highlighting clinical relevance and technical strategies, and identifying opportunities to advance personalized, predictive care in neuro-oncology. Materials and Methods PubMed, Scopus, and Web of Science databases were systematically screened for English-language original research articles published from inception through June 2025 focused on DT development, validation, or patient-specific computational models in neuro-oncology. Extracted variables included computational frameworks, data sources, clinical or predictive tasks, and reported outcomes. Risk of bias and applicability were assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), which revealed well-defined predictors and outcomes but frequent concerns regarding participants and analysis. Results Of the 73 articles reviewed, 21 met eligibility criteria. DTs simulated tumor growth, radiation response, immune interactions, and drug transport.Most models (<i>n</i> = 20) relied on mechanistic or biophysical frameworks, with increasing adoption of artificial intelligence-driven and hybrid approaches. A total of 12 studies focused on glioblastomas or high-grade gliomas, and 17 relied primarily on MRI data. Tumor-growth and treatment-response simulations were the most common DT applications. Only six studies provided publicly available code, and closed-loop calibration was reported in eight studies. Predictive accuracy and correlation with clinical data were generally high, but real-time integration, multimodal data fusion, and external validation were limited. Conclusion DTs showed promise for advancing personalized neuro-oncology, with demonstrated potential in modeling tumor behavior and optimizing therapies. Applications relied mainly on mechanistic artificial intelligence methods. Despite strong predictive performance, reproducibility, multimodal integration, and external validation remained limited, reflecting method heterogeneity. <b>Keywords:</b> Digital Twins, Neuro-oncology, Computational Modeling, Mechanistic Models, Brain Tumor, Precision Medicine <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e250567"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147444686","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 Utility of Deep Learning-based Multiple Arterial Phase MRI in Hepatocellular Carcinoma.","authors":"Kai Liu, Beixuan Zheng, Yunfei Zhang, Bin Wang, Jun Yang, Lu Wang, Mengsu Zeng, Ruofan Sheng","doi":"10.1148/rycan.250538","DOIUrl":"10.1148/rycan.250538","url":null,"abstract":"<p><p>Purpose To evaluate the clinical utility of deep learning-based ultrafast multiple arterial phase (AP) MRI for diagnosing hepatocellular carcinoma (HCC), compared with conventional single AP imaging, using both extracellular agents (ECAs) and hepatobiliary agents (HBAs). Materials and Methods This prospective study included participants with suspected HCC who underwent either ECA- or HBA-enhanced MRI between September 2024 and March 2025. Outcomes included late AP capture rate, image quality (overall quality, motion artifacts, noise, liver and lesion edge sharpness, and lesion conspicuity), diagnostic performance (lesion, arterial phase hyperenhancement [APHE], and HCC detection rates), and hepatic arterial visualization scoring. Wilcoxon rank sum, Pearson χ², and Fisher exact tests compared characteristics from multiple and single AP MRI. Results The final analysis included 128 participants who underwent ECA-enhanced MRI (64 multiphase, 64 single-phase; median age, 61 years [IQR, 54-69 years]; 103 male) and 108 participants who underwent HBA-enhanced MRI (54 multiphase, 54 single-phase; median age, 62 years [IQR, 56-69 years]; 83 male). In the ECA group, multiple AP MRI resulted in greater late AP capture (98% vs 81%; <i>P</i> = .001); higher scores for overall image quality (<i>P</i> = .03), motion artifacts (<i>P</i> < .001), lesion edge sharpness (<i>P</i> < .001), and lesion conspicuity (<i>P</i> = .007); and higher lesion (98% vs 90%; <i>P</i> = .01), APHE (96% vs 88%; <i>P</i> = .03), and HCC (96% vs 81%; <i>P</i> < .001) detection rates. In the HBA group, multiple AP MRI examinations also resulted in greater late AP capture (98% vs 85%; <i>P</i> = .04); higher scores for overall image quality (<i>P</i> = .01), motion artifacts (<i>P</i> = .04), and lesion edge sharpness (<i>P</i> = .005); and higher lesion (97% vs 88%; <i>P</i> = .04) and APHE (97% vs 86%; <i>P</i> = .02) detection rates. Multiphase imaging consistently achieved satisfactory hepatic arterial visualization in both contrast agent groups (mean scores > 3 on a four-point Likert scale for all categories). Conclusion Deep learning-based ultrafast multiphase arterial MRI improved late AP capture, image quality, and HCC diagnosis and enabled reliable hepatic arterial visualization within a single scan compatible with ECA and HBA. <b>Keywords:</b> MR-Imaging, Abdomen/GI, Liver <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e250538"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147487191","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":"TumorSynth: Integrated Brain Tumor and Tissue Segmentation on Brain MRI Scans of Any Resolution and Contrast.","authors":"Jiaming Wu, Benjamin Billot, Fenqiang Zhao, Junjie Li, Yaou Liu, Zhizheng Zhuo, Giuseppe Pontillo, Xing Liu, Song Lin, Xiaokang Zhang, Jun Qiu, Jinyuan Weng, Ling Zhang, Le Lu, Juan Eugenio Iglesias, Frederik Barkhof, Ferran Prados Carrasco","doi":"10.1148/rycan.250222","DOIUrl":"10.1148/rycan.250222","url":null,"abstract":"<p><p>Purpose To develop and validate a deep neural network that simultaneously segments brain tumors and anatomic structures, regardless of the contrast and resolution of the input scans, and can effortlessly adapt to unseen modalities. Materials and Methods The authors included various MRI scans from patients with and without brain tumors from four different datasets. Patient data were divided into a training set and a test set. The authors' method, TumorSynth, combines a Bayesian generative model and a deep learning segmentation model. The generative model creates paired synthetic labels and images with simulated tumors and brain tissues, providing a rich dataset for training the segmentation model. The authors quantitatively compared its performance with that of other widely used methods by calculating Dice similarity coefficients (DSCs). Results A total of 1971 patients with and without tumors were included in the study (training set, <i>n</i> = 351 patients; test set, <i>n</i> = 1620 patients). The median DSCs for segmentation (authors' method vs reference standard) were 0.89 (IQR, 0.83-0.95; <i>P</i> < .001) for the unaffected brain volume and 0.89 (IQR, 0.84-0.94; <i>P</i> < .001) for the tumor region. There were no differences in parcellation performance when an MRI sequence was missing (<i>P</i> = .07). In cross-modality validation, the authors' method achieved DSC values of 0.88 for apparent diffusion coefficient, 0.85 for diffusion-weighted imaging, 0.80 for susceptibility-weighted imaging, and 0.79 for fractional anisotropy images. The authors observed a 4% false-positive rate when processing tumor-free MR images. Conclusion The authors developed a deep neural network for brain tumor and tissue segmentation, validated its performance across standard structural MRI sequences, and determined its generalizability to unseen data. <b>Keywords:</b> Segmentation, Neuro-Oncology, CNS, Deep Learning, Neurosurgery <i>Supplemental material is available online for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e250222"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147532272","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":"Propensity Score Matching and Clinical Interpretation.","authors":"Kenichiro Okumura","doi":"10.1148/rycan.250603","DOIUrl":"10.1148/rycan.250603","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e250603"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146132960","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 Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.","authors":"Anna H Koch, Cris H B Claessens, Ingrid Nies, Eloy W R Schultz, Terese A E Hellström, Tim Boers, Eveline M L Dekker, Caroline L P Muntinga, Janneke S Hoogstad-van Evert, Ilse Niers-Stobbe, Annemarie Bruining, Christianne A R Lok, Joost Nederend, Fons van der Sommen, Jurgen M J Piek","doi":"10.1148/rycan.250027","DOIUrl":"10.1148/rycan.250027","url":null,"abstract":"<p><p>Purpose To develop a deep learning-based, computer-aided diagnosis (CADx) model for preoperative classification of ovarian tumors (OTs) on CT scans and to compare its performance with current US models and radiologist assessments. Materials and Methods This retrospective multicenter study (January 2021-November 2023) included patients with indeterminate OTs. The dataset comprised training, internal (<i>n</i> = 360), and external test (<i>n</i> = 27) sets. Final histopathology served as the reference standard. The CADx model was trained using self-supervised learning on public and institutional CT datasets. Performance of the CADx model was compared with that of two current US-based models (Risk of Malignancy [RMI] and Assessment of Different NEoplasias in the adneXa [ADNEX] models) and with radiologist reports. Metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values, with comparisons assessed using 95% CI overlap. Results The dataset contained 387 OT images from 344 patients (226 benign and 118 malignant OTs). The model achieved a median AUC of 0.84 (95% CI: 0.65, 0.92) on the internal test set and 0.61 (95% CI: 0.59, 0.65) on the external test set. The CADx model performed comparably with the two US models and radiologists. On the internal test set, AUCs for RMI, ADNEX, and radiologists were 0.77 (95% CI: 0.72, 0.83), 0.68 (95% CI: 0.51, 0.84), and 0.76 (95% CI: 0.69, 0.83), respectively. On the external test set, corresponding AUCs were 0.66 (95% CI: 0.44, 0.88), 0.86 (95% CI: 0.60, >0.99), and 0.67 (95% CI: 0.26, >0.99), respectively. The CADx model yielded the highest sensitivity (94.7%). Conclusion Despite disease and data variability, this CT-based deep learning model for preoperative OT classification achieved comparable performances to US models and radiologists on internal and external test sets, but further refinement is needed before clinical implementation. <b>Keywords:</b> Ovarian Tumor Classification, Ovarian Cancer, Computer-aided Diagnostics, Multicenter Trial Clinical trial registration no. NTC05174377 <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e250027"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146181856","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 Look Behind the Paper: Glypican-3-targeted US Molecular Imaging of Hepatocellular Carcinoma.","authors":"Xiaoxin Liang, Lingling Li, Yuanyuan Wang, Shilin Lu, Xu Han, Fei Yan, Jianhua Zhou","doi":"10.1148/rycan.260003","DOIUrl":"10.1148/rycan.260003","url":null,"abstract":"<p><p><b>Editor's Note.</b> This issue of <i>Radiology: Imaging Cancer</i> brings a new feature that we term \"A Look Behind the Paper.\" We invite authors of selected manuscripts to provide more details about their research and the thought process that led to the final manuscript. In this inaugural Look Behind the Paper, Liang and Li and colleagues describe the motivation for the published research, any unexpected challenges they encountered, and future directions for their study developing biosynthetic gas vesicles to detect glypican-3 in hepatocellular carcinoma. Their description of the critical decision to switch the targeting molecule for glypican-3 from an RNA aptamer to a peptide helps readers understand the barriers researchers must overcome to move a project forward. We hope you enjoy the authors' description of their research goals. We look forward to bringing you more insights from A Look Behind the Paper in future issues of the journal.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e260003"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147366254","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":"Evaluation, Optimization, and Validation of a Multiparametric CT Algorithm for Solid Renal Masses: CT-Score Version 2.0.","authors":"Satheesh Krishna, Mayooran Kandasamy, Rajesh Bhayana, Bipin Nanda, Kanika Diwan, Ameen Kamona, Sabah Sairafi, Susan Prendeville, Yangqing Deng, Antonio Finelli, Matthew S Davenport, Nicola Schieda","doi":"10.1148/rycan.250145","DOIUrl":"10.1148/rycan.250145","url":null,"abstract":"<p><p>Purpose To compare published CT-based systems for small solid renal mass (SoRM) assessment, propose modifications that may increase specificity and interreader agreement, and validate the revised system. Materials and Methods Our retrospective study included patients with histologically confirmed SoRMs measuring ≤4 cm who underwent CT imaging (single-institution internal dataset, <i>n</i> = 194; external dataset from The Cancer Imaging Archive, <i>n</i> = 55). Two blinded radiologists (readers 1 [R1] and 2 [R2]) compared four CT systems (CT score, modified CT score, abbreviated CT score, and UCLA CT score) for diagnostic accuracy in clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC) and for interreader agreement (Gwet agreement coefficient [AC1]). We also evaluated the addition of two decision rules to the best-performing algorithm (noncontrast CT [NCCT] attenuation ≤ 20 HU and corticomedullary phase-NCCT attenuation at two thresholds, ≤20 HU and ≤30 HU) to create a modified algorithm (CT-Score version 2.0). Results The abbreviated CT score had the best combination of accuracy for ccRCC (R1: 85% [95% CI: 79, 89], R2: 72% [95% CI: 65, 78]) and pRCC (R1: 86% [95% CI: 80, 91], R2: 86% [95% CI: 80, 91]) and interreader agreement (Gwet AC1 = 0.53). CT-Score version 2.0 (derived by adding decision rules to the abbreviated CT score) demonstrated substantial agreement (Gwet AC1 = 0.63). Specificity of CT-Score version 2.0 was higher for ccRCC (R1: 99% [95% CI: 94, 100], R2: 99% [95% CI: 94, 100] vs R1: 92% [95% CI: 84, 96], R2: 81% [95% CI: 72, 89]; <i>P</i> = .02, <i>P</i> < .001) and pRCC (R1: 100% [95% CI: 98, 100], R2: 100% [95% CI: 98, 100] vs R1: 93% [95% CI: 87, 96], R2: 93% [95% CI: 87, 96]; <i>P</i> = .003, <i>P</i> = .003) when compared with the abbreviated CT score. Validation in the external dataset showed similar results: Gwet AC1 = 0.53; specificity for ccRCC (R1: 100% [95% CI: 83, 100], R2: 100% [95% CI: 83, 100]); and specificity for pRCC (R1: 100% [95% CI: 82, 100], R2: 100% [95% CI: 92, 100]). Conclusion Application of CT-Score version 2.0 resulted in modest improvements in interreader agreement and high specificity for ccRCC and pRCC diagnosis. <b>Keywords:</b> CT, Kidney, Urinary, Oncology, Renal Mass, Algorithm, Clear Cell RCC, Papillary RCC <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"8 2","pages":"e250145"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146258924","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}