Radiology最新文献

{"title":"Comparison of Second-line Hepatic Arterial Infusion Chemotherapy and Tyrosine Kinase Inhibitors in Advanced Hepatocellular Carcinoma.","authors":"Jae-Sung Yoo, Kwon Yong Tak, Hee Sun Cho, Ji Won Han, Jeong Won Jang, Jaejun Lee, Hyun Yang, Chang Wook Kim, Hae Lim Lee, Hee Yeon Kim, Suho Kim, Jung Suk Oh, Ho Jong Chun, Heechul Nam, Pil Soo Sung","doi":"10.1148/radiol.253280","DOIUrl":"10.1148/radiol.253280","url":null,"abstract":"<p><p>Background With the advent of immune checkpoint inhibitor-based combination therapy, treatment sequencing for advanced hepatocellular carcinoma (HCC) has become increasingly complex. The Barcelona Clinic Liver Cancer (BCLC) 2026 recommendations emphasize individualized decision-making. Purpose To compare clinical outcomes of hepatic arterial infusion chemotherapy (HAIC) and tyrosine kinase inhibitors (TKIs) in patients with advanced HCC after atezolizumab-bevacizumab (AB) failure, within the BCLC 2026 treatment paradigm. Materials and Methods This multicenter retrospective study included patients who received AB and subsequently received either HAIC or a TKI between March 2022 and August 2025. HAIC used a cisplatin-fluorouracil regimen, and TKIs were given at standard doses. Tumor response and progression-free survival (PFS) were assessed using contrast-enhanced multiphase CT or liver MRI according to routine clinical practice. Imaging was reviewed by radiologists blinded to treatment allocation. Inverse probability of treatment weighting (IPTW) was applied for age, sex, Eastern Cooperative Oncology Group performance status, Child-Pugh class, portal vein tumor thrombosis, and tumor burden based on the up-to-seven criteria. Results This study included 90 patients (mean age, 62 years ± 10.9 [SD]; 73 men; HAIC group, <i>n</i> = 51; TKI group, <i>n</i> = 39). Baseline tumor burden was higher in the HAIC group than in the TKI group (percentage of patients with tumors beyond the up-to-seven criteria: 88% [45 of 51] vs 64% [25 of 39]; <i>P</i> = .01). In unweighted analyses, median overall survival (OS) was similar between the HAIC and TKI groups (10.4 vs 6.4 months; <i>P</i> = .62), whereas PFS favored HAIC over TKIs (median, 5.3 vs 3.6 months; <i>P</i> = .008). Objective response rate and disease control rate were higher with HAIC than with TKIs (objective response rate: 35% [18 of 51] vs 5% [two of 39], <i>P</i> = .002; disease control rate: 67% [34 of 51] vs 26% [10 of 39], <i>P</i> < .001). After IPTW, HAIC remained associated with longer PFS than TKIs (median, 7.1 vs 3.4 months; <i>P</i> < .001), and OS remained similar between groups (median, 10.5 vs 6.3 months; <i>P</i> = .32). Conclusion In patients with advanced HCC, after AB failure, second-line HAIC yielded higher response rates and longer PFS than TKIs. © RSNA, 2026 <i>Supplemental material is available for this article.</i> See also the editorial by Deyirmendjian and Tang in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253280"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case 349: Venous Infarction Secondary to Hypoglossal Canal Dural Arteriovenous Fistula.","authors":"Matheus Hideki Taborda, Matheus Kahakura Franco Pedro, Bernardo Corrêa de Almeida Teixeira","doi":"10.1148/radiol.252118","DOIUrl":"https://doi.org/10.1148/radiol.252118","url":null,"abstract":"<p><p>History A 76-year-old man presented to the emergency department with a 1-month history of worsening binocular diplopia on left horizontal gaze, along with 1 week of progressive dysarthria and dizziness. Additionally, he had a chronic history of right-sided cervicobrachialgia related to degenerative disk disease, which had been improving with oral analgesia. The patient's past medical history included hypertension, dyslipidemia, insomnia, and hypothyroidism. He also had postpolio syndrome affecting the right lower limb. He reported no fever, chills, cough, shortness of breath, abdominal pain, or chest pain. There was no history of rash, intravenous drug use, immunosuppressive medication use, or documented congenital abnormalities. He reported no recent or recurrent bacterial or viral infections. At clinical examination, the patient appeared tired and had a decreased level of alertness. He was hypertensive (165/91 mm Hg) but had a normal body temperature. Other vital signs were unremarkable. Neurologic assessment revealed left abducens nerve palsy, right lower facial palsy, horizontal nystagmus during right eye adduction, and right lower limb paresis with ipsilateral patellar hyperreflexia. Brain MRI performed the day after admission showed imaging abnormalities. Laboratory testing showed an elevated C-reactive protein level (135 mg/L; upper limit of normal, 1 mg/L); other blood tests were within normal limits. Extensive additional diagnostic workup-including serologic testing for anti-aquaporin-4 antibody, anti-DNA antibody, lupus anticoagulant, antinuclear antibody, anti-cardiolipin antibody, and rheumatoid factor-yielded negative results. Lumbar puncture revealed a normal cerebrospinal fluid opening pressure (18 cm H₂O; upper limit of normal, 25 cm H₂O). The cerebrospinal fluid was clear, with a normal total nucleated cell count (3.75/mm³; reference range, 0-5/mm³), normal glucose level (53 mg/dL [2.9 mmol/L]; reference range, 50-77 mg/dL [2.8-4.3 mmol/L]), and elevated total protein level (750 mg/L; reference range, 100-450 mg/L). Cerebrospinal fluid oligoclonal bands were positive.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e252118"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrast-enhanced CT Radiomics in Lung Adenocarcinoma: Beyond Visual Enhancement.","authors":"Yuki Arita, Burak Kocak","doi":"10.1148/radiol.261951","DOIUrl":"https://doi.org/10.1148/radiol.261951","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e261951"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"⁶⁸Ga-NK224 PET/CT Reveals Intra- and Intertumoral Heterogeneity of PD-L1 in Non-Small Cell Lung Cancer.","authors":"Liang Zhao, Hui Zhou, Lingyu Yu, Dan Ruan, Zhenyu Wu, Yin Li, Yaqing Dai, Jiancheng Li, Long Sun, Hua Wu, Yanjun Mi, Haojun Chen","doi":"10.1148/radiol.260372","DOIUrl":"10.1148/radiol.260372","url":null,"abstract":"<p><p>Background Programmed death ligand 1 (PD-L1) expression assessed via immunohistochemical analysis of single-site biopsies is standard for guiding immunotherapy in non-small cell lung cancer (NSCLC). However, this approach fails to capture intra- and intertumoral heterogeneity. Purpose To investigate whether gallium 68 (⁶⁸Ga) NK224 PET/CT enables whole-body assessment of PD-L1 expression and heterogeneity in NSCLC and whether lesion-specific treatment response is associated with PET-defined PD-L1 uptake. Materials and Methods In this prospective study conducted from December 2023 to July 2025, participants with newly diagnosed or recurrent or metastatic NSCLC underwent ⁶⁸Ga-NK224 PET/CT and PD-L1 immunohistochemical analysis. Tumor uptake of ⁶⁸Ga-NK224 was quantified using maximum standardized uptake value (SUV_max) from biopsy-plane regions of interest (ROIs) and whole-lesion ROIs. PD-L1 tumor proportion score (TPS) was the reference standard. Intratumoral heterogeneity was assessed using the normalized range of SUV_max, and intertumoral heterogeneity was evaluated using the SUV_max coefficient of variation across lesions. Results This study included 48 participants (median age, 68 years [IQR, 59-75 years]; 31 men). Across 52 lesions, ⁶⁸Ga-NK224 uptake differed among PD-L1 TPS categories (<i>P</i> < .001), and biopsy-plane SUV_max correlated strongly with TPS (Spearman ρ = 0.80; <i>P</i> < .001). Receiver operating characteristic analysis identified an optimal SUV_max cutoff of 5.6 for discriminating tumors with high PD-L1 expression. Among 12 participants classified as PD-L1 negative via immunohistochemical analysis, five had one or more lesions with SUV_max above the cutoff (15 of 68 lesions). Intratumoral heterogeneity was substantially higher when assessed using whole-lesion ROIs (median normalized range, 1.0) versus biopsy-plane ROIs (median normalized range, 0.4) (<i>P</i> < .001). Intertumoral heterogeneity within individual participants was pronounced (median SUV_max coefficient of variation, 23.6% [IQR, 17.9%-36.7%]). In participants receiving immunotherapy (24 lesions), lesions with partial or complete response (shrinkage ≥30%; median shrinkage, 47.5%) showed greater ⁶⁸Ga-NK224 uptake than lesions with progressive or stable disease (shrinkage <30%; median shrinkage, 7.4%) (median SUV_max, 5.9 vs 1.9; <i>P</i> = .01). Conclusion ⁶⁸Ga-NK224 PET/CT enabled whole-body assessment of PD-L1 heterogeneity in NSCLC and revealed lesion-level heterogeneity not captured through biopsy, and uptake was associated with immunotherapy response. Clinical trial registration no. NCT06754345 © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. <i>Supplemental material is available for this article.</i> See also the editorial by Lopci in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e260372"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Call to Action: Accelerating AI-driven Transformation in Medical Imaging and the Broader Health Care System.","authors":"Linda Moy, Alberto Vargas, Judy Wawira Gichoya, Matthew P Lungren, Luis Martí-Bonmatí, Christopher P Hess, Miriam A Bredella, Mitchell Schnall","doi":"10.1148/radiol.253974","DOIUrl":"10.1148/radiol.253974","url":null,"abstract":"<p><p>Global imaging demand now exceeds capacity due to an aging population, rising comorbidities, expanded indications for imaging, and workforce shortages. These pressures are reshaping national health care policy. At the same time, the competing demands to expand access to imaging services while reducing costs are creating momentum to reimagine imaging delivery through the integration of transformative technologies like artificial intelligence (AI). This consensus statement from the International Society for Strategic Studies in Radiology (IS3R) is a call to action, outlining promising AI solutions that increase workflow efficiency and align radiology with health system and payer priorities. Reflecting expert consensus from the IS3R meeting in Dublin, Ireland, in August 2025, this statement defines strategic directions for radiology amid accelerating digitalization, rapid innovation, and data-driven patient care. This article covers four domains: image interpretation, capacity building, data governance, and systemic digital transformation. This article examines how AI can address rising imaging demand and costs while addressing challenges related to improving data quality, standardization, and interoperability. It also highlights the need to re-engineer workflows and adapt to new care models while maintaining high-quality services. Finally, it presents consensus recommendations outlining how AI can enable multimodal health data integration and advance alignment with value-based care.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253974"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hepatic Arterial Infusion Chemotherapy as Second-line Therapy in Advanced Hepatocellular Carcinoma: An Early Career Perspective.","authors":"Claudia Deyirmendjian, An Tang","doi":"10.1148/radiol.262083","DOIUrl":"https://doi.org/10.1148/radiol.262083","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e262083"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI Metabolic Biomarkers Predict Ischemic Lesion Survival in Hyperacute Stroke: New Insight on Diffusion Reversibility.","authors":"Mona Asghariahmadabad, Ameera Ismail, Mana Shams, Mersedeh Bahr-Hosseini, Viktor Szeder, Jeffrey L Saver, Mehmet Kurt, Abhejit Rajagopal, Adam Bauer, Wade S Smith, Nerissa Ko, Kazim Narsinh, Luis E Savastano, Daniel L Cooke, Steven W Hetts, Matthew R Amans, S Andrew Josephson, Christopher P Hess, David S Liebeskind, Kambiz Nael","doi":"10.1148/radiol.252121","DOIUrl":"https://doi.org/10.1148/radiol.252121","url":null,"abstract":"<p><p>Background In patients with acute ischemic stroke (AIS), ischemic core volume overestimation at diffusion-weighted MRI due to diffusion reversal has been described following successful reperfusion. Purpose To assess the potential role of the oxygen extraction fraction (OEF), derived from baseline dynamic susceptibility contrast (DSC) MRI, in diffusion reversal prediction in patients with AIS after successful reperfusion. Materials and Methods This retrospective study (January 2015 to December 2020) included patients with anterior-circulation large-vessel occlusion stroke who underwent pretreatment MRI, including diffusion-weighted imaging and DSC perfusion, achieved successful reperfusion (modified Thrombolysis in Cerebral Infarction score ≥2c), and underwent follow-up MRI within 48 hours. Ischemic core masks were generated from baseline MRI using an apparent diffusion coefficient (ADC) threshold of 620 × 10^-6 mm²/sec or less, and infarct masks were generated from the final DWI scans. DSC perfusion was used to calculate the relative OEF (rOEF). Image co-registration and subtraction were performed to determine the final voxel fate as persistent infarct versus surviving tissue. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess imaging biomarker predictive value. Results Seventy patients were included (mean age, 72.2 years ± 13.1 [SD]; 40 female). Approximately 35% of the volume initially designated as the ischemic core showed diffusion reversal. Following logistic regression, the independent imaging variables associated with reducing the odds that ischemic core voxels would evolve to infarct voxels were ADC (odds ratio [OR], 0.99; 95% CI: 0.94, 1.0; <i>P</i> < .001) and rOEF (OR, 0.33; 95% CI: 0.32, 0.35; <i>P</i> < .001). A combined ADC plus rOEF model showed an area under the ROC curve (AUC) of 0.80 (95% CI: 0.80, 0.81), sensitivity of 74%, and specificity of 76% for infarct prediction, outperforming ADC alone (AUC, 0.65 [95% CI: 0.64, 0.66], sensitivity, 60%; specificity, 63%; <i>P</i> < .001). The added value of rOEF in delineating ischemic core was time-dependent, with the strongest negative impact within 120 minutes from stroke onset. Conclusion In patients with AIS for whom reperfusion was successful, the addition of rOEF to ADC improved ischemic core definition by predicting diffusion reversal and reducing core volume overestimation, especially in the hyperacute window. © RSNA, 2026 <i>Supplemental material is available for this article.</i> See also the editorial by Rapalino in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e252121"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fracture Detection Algorithm in Real-World Radiology.","authors":"Mario Maas, Kerensa M Beekman","doi":"10.1148/radiol.261838","DOIUrl":"https://doi.org/10.1148/radiol.261838","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e261838"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostically Calibrated AI Image Quality Framework Applied to Prostate Cancer Detection at MRI.","authors":"Tiago F Coelho, Joeran S Bosma, Vilma Bozgo, Constant R Noordman, Robert Grimm, Maarten de Rooij, Marnix C Maas, Henkjan Huisman","doi":"10.1148/radiol.253360","DOIUrl":"https://doi.org/10.1148/radiol.253360","url":null,"abstract":"<p><p>Background Image quality affects diagnostic performance; however, current quality metrics rely on subjective reader labels rather than diagnostic outcomes. Purpose To develop and evaluate a diagnostically calibrated artificial intelligence (AI) framework using deep learning (DL) for image quality assessment, linking prostate MRI quality scores to diagnostic performance. Materials and Methods This single-center retrospective study analyzed 12 496 consecutive prostate multiparametric MRI examinations performed between January 2014 and December 2023 at Radboud University Medical Center. A two-step framework was implemented: First, a DL model was trained on the most reliable low-quality (artifact-degraded structures) and high-quality (clear delineated zones) axial T2-weighted images to produce a continuous image quality score. Second, this score was applied in an independent internal test set and calibrated with diagnostic performance measured as <i>(a)</i> area under the receiver operating characteristic curve (AUC) for clinically significant prostate cancer (csPCa) detection by an AI model and <i>(b)</i> accuracy of csPCa detection by radiologists in routine clinical practice. The reference standard was histopathologic findings at biopsy. Diagnostic performance was calculated for quality score 10-percentile thresholds from 10% to 80%. Linear trend between performance and threshold was assessed using a permutation test on the regression slope. Results A total of 1229 T2-weighted series were used for training, and 568 multiparametric MRI examinations for diagnostic testing (1638 male patients; median age, 67 years [IQR, 62-71 years]). The DL model achieved a mean AUC of 0.99 for distinguishing low- versus high-quality images in the training set during fivefold cross-validation. For csPCa detection in the test set, the AI model AUC improved from 0.92 (95% CI: 0.89, 0.94) to 0.99 (95% CI: 0.97, 1.00) (<i>P</i> = .005) across thresholds of DL model image quality score; radiologist accuracy improved from 77% (95% CI: 73, 80) to 85% (95% CI: 78, 91) (<i>P</i> = .01). Conclusion The AI framework provided a standardized method to derive an objective image quality score that correlated with improved AI model diagnostic performance and radiologist accuracy. © RSNA, 2026 <i>Supplemental material is available for this article.</i> See also the editorial by Barrett in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e253360"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Prostate MRI Quality Becomes a Diagnostic Biomarker in the AI Era.","authors":"Tristan Barrett","doi":"10.1148/radiol.262030","DOIUrl":"https://doi.org/10.1148/radiol.262030","url":null,"abstract":"","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"320 2","pages":"e262030"},"PeriodicalIF":17.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}