Purpose To compare safety and feasibility between a novel CT-guided robotic system and the conventional freehand technique for puncture biopsy of thoracoabdominal lesions. Materials and Methods In this prospective multicenter randomized trial, individuals with suspected lesions were enrolled between July 2023 and April 2024 across three university teaching hospitals and randomized to the robot-assisted group (n = 82) or the freehand group (n = 83). Procedure outcomes included the technical success rate, targeting error, number of CT scans and needle adjustments, puncture time, and complications. Descriptive and inferential statistics were calculated. Results A total of 165 participants (mean age, 60 years ± 10 [SD]; 83 male) were included. Compared with the freehand group, the robot-assisted group demonstrated a higher technical success rate (97.56% [80 of 82] vs 62.65% [52 of 83], P < .001), lower targeting error (mean Euclidean deviation: 1.7 mm ± 1.1 vs 4.5 mm ± 3.9, P < .001), and fewer CT scans (mean, 4.3 ± 1.9 vs 5.2 ± 2.3; P = .002) and needle adjustments (mean, 0.7 ± 0.7 vs 1.6 ± 1.6; P = .003). Despite differences in geometric precision, both groups achieved 100% (82 of 82 and 83 of 83) diagnostic yield. The median puncture time was comparable between groups (5.5 minutes ± 4.3 vs 4.8 minutes ± 7.0, P = .50). During lung biopsies, the robot-assisted approach yielded fewer complications compared with the freehand approach (4.88% [four of 82] vs 16.87% [14 of 83], P = .014). Conclusion Compared with the freehand approach, robot-assisted biopsy yielded greater precision and reduced adjustments and complications while demonstrating noninferior diagnostic efficacy and comparable duration. Keywords: Robotic Needle Insertion, Biopsy, Thoracoabdominal Lesions, Robot-assisted Biopsy, CT-guided Intervention, Percutaneous Needle Biopsy, Randomized Controlled Trial, Algorithm Development, CT, Clinical Testing, Interventional-Body, Biopsy/Needle Aspiration, Percutaneous, Thorax, Abdomen/GI, Liver, Lung, Kidney ©RSNA, 2026.
Purpose To establish and evaluate a nomogram based on quantitative spectral CT parameters for predicting microsatellite instability (MSI) and deficient mismatch repair (dMMR) status in patients with esophagogastric junction adenocarcinoma (EGJA). Materials and Methods In this study including retrospective and prospective datasets (enrollment period: May 2021 to January 2026), patients from two centers were divided into training, validation, external test, prospective test, and neoadjuvant chemotherapy cohorts. A nomogram was constructed integrating clinical characteristics with quantitative spectral CT parameters. Model efficacy in predicting MSI/dMMR and its associations with disease-free survival were evaluated. Primary statistical methods included logistic and Cox regression analyses. Results In total, 511 patients (median age, 67 years [IQR, 60-73 years]; 333 male) were included. The nomogram incorporated sex, clinical N stage, CT attenuation on 40-keV virtual monoenergetic images, and normalized iodine density during the venous phase. It achieved areas under the receiver operating characteristic curve of 0.87 (95% CI: 0.81, 0.93), 0.87 (95% CI: 0.78, 0.96), 0.91 (95% CI: 0.83, 0.99), 0.89 (95% CI: 0.80, 0.98), and 0.86 (95% CI: 0.76, 0.96) across the five respective cohorts: training, validation, external test, prospective test, and neoadjuvant chemotherapy. In the external test cohort, the nomogram correctly identified 9.76% (four of 41) of the patients misclassified with preoperative biopsy. Furthermore, it stratified patients into distinct disease-free survival risk groups in the training (hazard ratio, 2.04 [95% CI: 1.35, 3.09]; P < .001) and validation (hazard ratio, 2.97 [95% CI: 1.46, 6.05]; P = .003) cohorts. Conclusion The spectral CT-based nomogram enabled preoperative prediction of MSI/dMMR status and prognostic assessment in patients with EGJA. Keywords: Esophagogastric Junction Adenocarcinoma, Spectral CT, Nomogram, Primary Neoplasms, CT-Spectral, Neoplasms-Primary, Pathology, Tumor Immune Microenvironment, Pre-clinical Models Chinese Clinical Trial Registry identifier nos. ChiCTR2500097335 and ChiCTR2500101639 Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.
Purpose To evaluate the added value of a synthetic radiomics model (S-Radiomics) that integrates topologic and Hessian-based features with traditional radiomic features for predicting a pathologic complete response (pCR) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Materials and Methods Patients with HER2-positive breast cancer from three centers were retrospectively included between January 2018 and January 2024. Patients from centers 1 and 2 (n = 201) were randomly assigned to training (n = 150) and internal testing (n = 51) sets, whereas patients from center 3 (n = 82) constituted external testing set I. An additional genomic set (n = 64, I-SPY 2) and external testing set II (n = 57, I-SPY 1) were used for independent validation. Multivariable logistic regression with all clinical variables was used to identify independent predictors (P < .05) and develop the clinical model, adjusting for potential confounders. Traditional radiomics model (T-Radiomics) and S-Radiomics were constructed. Performance was evaluated using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement, net reclassification index, and survival analysis. Results A total of 404 female patients with HER2-positive breast cancer (mean age ± SD, 50 years ± 9) were included. The S-Radiomics outperformed the T-Radiomics on both the internal (AUC, 0.86 vs 0.78; P = .37) and external test (AUC, 0.84 vs 0.73; P = .11) sets, with improvements in the integrated discrimination improvement (P = .24 and .01) and net reclassification index (P = .21 and .01). Shapley additive explanations analysis revealed topologic features as the most important predictors. For center 1, survival analysis revealed longer disease-free survival in predicted responders (P = .02). Conclusion The S-Radiomics outperformed the T-Radiomics in predicting pathologic complete responses in patients with HER2-positive breast cancer. Keywords: Radiogenomics, MRI, Breast, Human Epidermal Growth Factor Receptor 2, Breast Cancer, Neoadjuvant Chemotherapy, Pathologic Complete Response, Radiomics Supplemental material is available for this article. © RSNA, 2026.
Human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) is biologically and prognostically distinct from HPV-negative disease, and staging directly impacts treatment. The American Joint Committee on Cancer (AJCC) and Union for International Cancer Control version 9 TNM staging system incorporates unequivocal imaging-detected and/or clinically detected extranodal extension (iENE) into clinical nodal staging and definitive pathologic extranodal extension into pathologic nodal staging. Additionally, it provides more explicit anatomic definitions for advanced local disease (clinical T4) while maintaining T and M category definitions from the eighth edition. Radiologists play a central role in the multidisciplinary team by evaluating endophytic tumor extent, regional nodal disease and iENE, and distant metastasis using CT, MRI, and PET. This article reviews clinically relevant oropharyngeal anatomy and proposes a practical radiologic approach to assigning T, N, and M categories in HPV-associated OPSCC. It emphasizes cautious interpretation of iENE because of interreader variability and imperfect diagnostic performance, with further high-grade evidence needed to establish its consistently reproducible, incremental prognostic value as a clinical N category modifier. It also highlights multidisciplinary factors that guide selection of surgical resection versus nonsurgical therapy. Posttreatment evaluation is discussed in the context of blood-based biomarkers such as tumor tissue-modified viral HPV DNA. The goal of this article is to provide a practical, staging-focused imaging framework for evaluation of HPV-associated OPSCC. Keywords: CT, MR Imaging, Head/Neck, Neck, Pharynx, Surgery, Human Papillomavirus, Squamous Cell Carcinoma, Oropharynx, Transoral Robotic Surgery, Oropharyngeal Carcinoma, TTMV HPV DNA © RSNA, 2026.
Spectral CT and photon-counting detector (PCD) CT are recent innovations in imaging that have gained increasing use as diagnostic tools. Both modalities leverage advanced detector technologies-PCD CT uses detectors that count individual x-ray photons and measure their energies, while spectral CT separates x-ray energies at dual or multiple levels. These approaches provide higher spatial resolution, improved contrast-to-noise ratio, and reduced noise and enable multienergy imaging from a single acquisition. These techniques enhance visualization of tissue characteristics and reduce artifacts compared with conventional energy-integrating detector CT. This review article introduces the fundamental principles of spectral CT and PCD CT, their advantages in neuro-oncologic applications, and their potential to improve diagnostic imaging through enhanced image quality and quantitative accuracy. Keywords: Physics, CT-Photon-counting, Pediatrics, CT-Spectral © RSNA, 2026.
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 (n = 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; P = .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]; P = .06). Conclusion The automated ADC measurement method was reproducible, and tumor ΔADC could predict pCR early. External validation will be included in future work. Keywords: 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
Purpose To explore the diagnostic performance of time-dependent diffusion MRI (td-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 td-dMRI between August 2023 and July 2025. Histogram features from td-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 td-dMRI metrics and the Ki-67 index were evaluated. Results In meningioma grading, the cMRI-td-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 P = .009, .002-.009) and similar to the td-dMRI model (AUC, 0.83 [95% CI: 0.74, 0.89]; corrected P = .47). The combined model achieved a higher IDI than the cMRI and single-parameter models (0.16-0.27, all corrected P < .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 P < .01). Weak correlations were observed between cell diameter, intracellular volume fraction, and cellularity and the Ki-67 index (r = -0.231 to -0.194 and 0.203-0.342; all P < .05). Conclusion The use of td-dMRI combined with histogram analysis performed well in assessing meningioma grade, subtype, and proliferative activity. Keywords: CNS, MRI, MR-Diffusion Weighted Imaging, Time-dependent Diffusion MRI, Meningioma, Histogram Analysis, World Health Organization Grading, Histological Subtyping Supplemental material is available for this article. © RSNA, 2026.
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 (n = 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. Keywords: MRI, Machine Learning, Radiomics, Prognosis and Prediction, Cervical Cancer, Recurrence, Inflammatory Markers Supplemental material is available for this article. © RSNA, 2026.
Purpose To perform a systematic review and meta-analysis of liver background uptake across four prostate-specific membrane antigen (PSMA)-targeting PET radiotracers (fluorine 18 [18F]-piflufolastat, gallium 68 [68Ga]-PSMA-11, 18F-flotufolastat, and 18F-PSMA-1007) and evaluate the potential impact on patient selection for PSMA-targeted therapy. Materials and Methods A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and Cochrane through May 12, 2025, to identify human studies reporting quantitative liver background uptake for the four PSMA-targeted PET radiotracers of interest. For each eligible study, liver background uptake was extracted as standardized uptake values (SUVs) and summarized and compared by radiotracer using random effects models with robust variance estimation. Results Among 652 unique records, 17 studies with 1497 total patients met inclusion criteria and reported liver background SUVs. Summary mean liver background SUV was 5.0 (95% CI: 3.6, 6.3) for 18F-piflufolastat, 5.1 (95% CI: 4.3, 5.9) for 68Ga-PSMA-11 (P = .579 compared with 18F-piflufolastat), 7.2 (95% CI: 6.3, 8.2) for 18F-flotufolastat (P = .005), and 12.1 (95% CI: 11.4, 12.9) for 18F-PSMA-1007 (P < .001). Conclusion Liver background uptake differed across PSMA-targeted PET radiotracers and may influence patient eligibility for PSMA-targeted therapy when liver activity is used as a reference for defining PSMA-positive metastatic prostate cancer. Keywords: PET, Genital/Reproductive, Prostate, 68Ga-PSMA-11, 18F-piflufolastat, 18F-flotufolastat, 18F-PSMA-1007, PSMA-targeted PET, PSMA-targeted Therapy Supplemental material is available for this article. © RSNA, 2026.
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, n = 173, January 2005-December 2023; prospective temporal validation set, n = 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 P < .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. Keywords: MR Imaging, Urinary, Kidney, Renal Cell Carcinoma, Magnetic Resonance Imaging, Inferior Vena Cava Tumor Thrombus, Venous Wall Invasion Supplemental material is available for this article. © RSNA, 2026.
