Academic Radiology最新文献

{"title":"Long-Term Outcomes of Transarterial Chemoembolization plus Ablation versus Surgical Resection in Patients with Large BCLC Stage A/B HCC.","authors":"Ying-Wen Hou, Tian-Qi Zhang, Li-Di Ma, Yi-Quan Jiang, Xue Han, Tian Di, Lu Tang, Rong-Ping Guo, Min-Shan Chen, Jin-Xin Zhang, Zhi-Mei Huang, Jin-Hua Huang","doi":"10.1016/j.acra.2025.02.012","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.012","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Large hepatocellular carcinoma (HCC) exhibits heterogeneous morphologies and varied responses to treatment. We evaluated outcomes of patients with different large HCC classifications receiving surgical resection (SR) or transarterial chemoembolization plus ablation (TA).</p><p><strong>Materials and methods: </strong>Patients with HCC ≥ 5 cm receiving SR or TA between May 2016 and December 2020 at one center were analyzed retrospectively and with propensity score matching (PSM). Overall survival (OS) and progression-free survival (PFS) of the 2 treatment groups were compared. Tumors were classified according to imaging morphology and gross pathology: Type I, simple nodular; Type II, simple nodular with extranodular growth or confluent multinodular; Type III, infiltrative.</p><p><strong>Results: </strong>Of 644 patients, 374 met the inclusion criteria (300 received SR and 74 received TA). Before PSM, median follow-up was 51.2 (IQR 29.6-65.3) months, and the SR group had longer OS (HR 2.13, 95% CI 1.44-3.15, p<0.001) and PFS (HR 2.31, 95% CI 1.66-3.20, p<0.001) than the TA group; after PSM these differences were not significant (all p>0.05). Infiltrative HCC (Type III) was an independent negative prognostic factor for OS and PFS. Within both treatment groups, patients with infiltrative HCC had shorter OS and PFS than patients with non-infiltrative HCC (Types I and II) (all p<0.001).</p><p><strong>Conclusion: </strong>For patients with HCC ≥ 5 cm, tumor classification is an important prognostic factor. In patients with non-infiltrative HCC, TA and SR had comparable OS after PSM. For patients with infiltrative HCC, TA and SR had limited efficacy.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Scourge of Workplace Bullying.","authors":"Richard B Gunderman","doi":"10.1016/j.acra.2025.02.023","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.023","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Microvascular Invasion and Early Recurrence in Hepatocellular Carcinoma Using DeepLab V3+ Segmentation of Multiregional MR Habitat Images.","authors":"Zhenhuan Huang, Yifan Pan, Wanrong Huang, Feng Pan, Huifang Wang, Chuan Yan, Rongping Ye, Shuping Weng, Jingyi Cai, Yueming Li","doi":"10.1016/j.acra.2025.02.006","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.006","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Accurate identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment and prognosis. Single-modality and feature fusion models using manual segmentation fail to provide insights into MVI. This study aims to develop a DeepLab V3+ model for automated segmentation of HCC magnetic resonance (MR) images and a decision fusion model to predict MVI and early recurrence (ER).</p><p><strong>Materials and methods: </strong>This retrospective study included 209 HCC patients (146 in the training and 63 in the test cohorts). The performance of DeepLab V3+ for HCC MR image segmentation was evaluated using Dice Loss and F1 score. Intraclass correlation coefficients (ICCs) assessed feature extraction reliability. Spearman's correlation analyzed the relationship between tumor volumes from automated and manual segmentation, with agreement evaluated using Bland-Altman plots. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC), calibration curves, and decision curve analysis. A nomogram predicted ER of HCC after surgery, with Kaplan-Meier analysis for 2-year recurrence-free survival (RFS).</p><p><strong>Results: </strong>The DeepLab V3+ model demonstrated high segmentation accuracy, with strong agreement in feature extraction (ICC: 0.802-0.999). The decision fusion model achieved AUCs of 0.968 and 0.878 for MVI prediction, and the nomogram for predicting ER yielded AUCs of 0.782 and 0.690 in the training and test cohorts, respectively, with significant RFS differences between the risk groups.</p><p><strong>Conclusion: </strong>The DeepLab V3+ model accurately segmented HCC. The decision fusion model significantly improved MVI prediction, and the nomogram offered valuable insights into recurrence risk for clinical decision-making.</p><p><strong>Availability of data and materials: </strong>The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teaching the Technology: Evaluating a Video-Based PACS Curriculum for Radiology Residents.","authors":"David Gullotti, Moustafa Abou Areda, Wen-Chi Hsu, Victoria Shi, Tae Kyung Kim, Erin Gomez, Cheng Ting Lin","doi":"10.1016/j.acra.2025.02.010","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.010","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Picture archiving and communication system (PACS) is integral to radiology, yet effective training to master PACS viewer tools remains challenging. This study aimed to evaluate the impact of a video-based curriculum on radiology residents' PACS confidence and technical proficiency.</p><p><strong>Materials and methods: </strong>A prospective cohort study was conducted at a tertiary academic institution during 2022-2023, enrolling first-year (R1) and second-year (R2) radiology residents. The Intervention Group received a 30-minute instructional video on PACS tools 3 months after their orientation. The Reference Group did not receive additional training. Comfort levels and PACS task performance were assessed before and after the intervention.</p><p><strong>Results: </strong>A total of 24 residents participated, with 12 in each group. The Intervention Group showed a significant increase in self-reported confidence in PACS skills following the video intervention (median [interquartile range]=-1.01 [-1.30, -0.43] vs. 2.07 [1.37, 2.38], p<0.01). The Intervention Group maintained a higher confidence level at the R2 level compared to the Reference Group, though the difference was not statistically significant. Additionally, the Intervention Group completed their assigned PACS tasks in significantly less time (total time: 220.5 s [179.5, 259.5] vs. 315.0 s [211.8, 397.0], p=0.04).</p><p><strong>Conclusion: </strong>The video-based PACS curriculum effectively improved radiology residents' technical skills and confidence. These results highlight the importance of supplementing on-the-job learning with formal technical training to reinforce proficiency with the PACS viewer.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.","authors":"Lavinia Brockstedt, Nils F Grauhan, Andrea Kronfeld, Mario Alberto Abello Mercado, Julia Döge, Antoine Sanner, Marc A Brockmann, Ahmed E Othman","doi":"10.1016/j.acra.2025.02.002","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.002","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-based reconstruction (DLR) algorithm called AiCE Inner Ear.</p><p><strong>Material and methods: </strong>In a retrospective, single-center study (February 1-July 30, 2023), UHR-CT scans of 57 temporal bones of 35 patients (5 children, 23 male) with at least one anatomical unremarkable temporal bone were included. There is an adult computed tomography dose index volume (CTDIvol 25.6 mGy) and a pediatric protocol (15.3 mGy). Images were reconstructed using HIR at normal resolution (0.5-mm slice thickness, 512² matrix) and UHR (0.25-mm, 1024² and 2048² matrix) as well as with a vendor-specific DLR advanced intelligent clear-IQ engine inner ear (AiCE Inner Ear) at UHR (0.25-mm, 1024² matrix). Three radiologists evaluated 18 anatomic structures using a 5-point Likert scale. Signal-to-noise (SNR) and contrast-to-noise ratio (CNR) were measured automatically.</p><p><strong>Results: </strong>In the adult protocol subgroup (n=30; median age: 51 [11-89]; 19 men) and the pediatric protocol subgroup (n=5; median age: 2 [1-3]; 4 men), UHR-CT with DLR significantly improved subjective image quality (p<0.024), reduced noise (p<0.001), and increased CNR and SNR (p<0.001). DLR also enhanced visualization of key structures, including the tendon of the stapedius muscle (p<0.001), tympanic membrane (p<0.009), and basal aspect of the osseous spiral lamina (p<0.018).</p><p><strong>Conclusion: </strong>Vendor-specific DLR-enhanced UHR-CT significantly improves temporal bone image quality and diagnostic performance.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Large Language Models in Oncological Monitoring.","authors":"Dr Alperen Elek","doi":"10.1016/j.acra.2025.02.031","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.031","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in CT for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer Patients: A Meta-analysis.","authors":"Sixun Zeng, Yingxian Liu, Xinyi Duan, Xin Zhao, Xiangjuan Sun, Fenghua Zhang","doi":"10.1016/j.acra.2025.02.007","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.007","url":null,"abstract":"<p><strong>Purpose: </strong>This meta-analysis aims to evaluate the diagnostic performance of CT-based artificial intelligence (AI) in diagnosing cervical lymph node metastasis (LNM) of papillary thyroid cancer (PTC).</p><p><strong>Methods: </strong>A systematic search was conducted in PubMed, Embase, and Web of Science databases through December 2024, following PRISMA-DTA guidelines. Studies evaluating CT-based AI models for diagnosing cervical LNM in patients with pathologically confirmed PTC were included. The methodological quality was assessed using a modified QUADAS-2 tool. A bivariate random-effects model was used to calculate pooled sensitivity, specificity, and area under the curve (AUC). Heterogeneity was evaluated using I² statistics, and meta-regression analyses were performed to explore potential sources of heterogeneity.</p><p><strong>Results: </strong>17 studies comprising 1778 patients in internal validation sets and 4072 patients in external validation sets were included. In internal validation sets, AI demonstrated a sensitivity of 0.80 (95% CI: 0.71-0.86), specificity of 0.79 (95% CI: 0.73-0.84), and AUC of 0.86 (95% CI: 0.83-0.89). Radiologists suggested comparable performance with sensitivity of 0.77 (95% CI: 0.64-0.87), specificity of 0.79 (95% CI: 0.72-0.85), and AUC of 0.85 (95% CI: 0.81-0.88). Subgroup analyses revealed that deep learning methods outperformed machine learning in sensitivity (0.86 vs 0.72, P<0.05). No significant publication bias was found in internal validation sets for AI diagnosis (P=0.78).</p><p><strong>Conclusion: </strong>CT-based AI showed comparable diagnostic performance to radiologists for detecting cervical LNM in PTC patients, with deep learning models showing superior sensitivity. AI could potentially serve as a valuable diagnostic support tool, though further prospective validation is warranted. Limitations include high heterogeneity among studies and insufficient external validation in diverse populations.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial: Differentiating Malignant From Benign Subpleural Lung Lesions Using Perfluorobutane-Enhanced Ultrasound: A Very New Technology for an Old Problem.","authors":"Paul Patrick Cronin","doi":"10.1016/j.acra.2025.02.026","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.026","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation of Radiological and Pathological Tumor Sizes in Breast Cancer Based on Molecular Subtypes and Accompanying DCIS: A Retrospective Multicenter Study. TR-BRC 2023-01.","authors":"Deniz Esin Tekcan Sanli, Gul Esen Icten, Sibel Kul, Pınar Balci, Nermin Tuncbilek, Levent Celik, Yasemin Kayadibi, Aysenur Oktay, Serap Gultekin, Fusun Taskin, Mustafa Erkin Aribal, Emel Ozveri, Fatma Tokat, Aykut Teymur, Isıl Basara Akin, Gulsah Ozdemir, Davut Can Guner, Seda Aladag Kurt, Ozge Aslan, Aydan Avdan Aslan, Ebru Yilmaz","doi":"10.1016/j.acra.2025.01.037","DOIUrl":"https://doi.org/10.1016/j.acra.2025.01.037","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to compare radiological tumor sizes obtained by mammography (MMG), ultrasonography (US), and magnetic resonance imaging (MRI) with pathological sizes to determine if molecular subtypes and the presence of accompanying ductal carcinoma in-situ (DCIS) affect accuracy.</p><p><strong>Methods: </strong>A total of 559 cases diagnosed with breast cancer in 11 different centers between 2010-2023 were included in the study. The patients' MMG, US, and MRI images were re-evaluated, and radiological findings and tumor sizes were recorded. Histological diagnosis (invasive/in-situ/mixed), receptor status, Ki-67 index, and tumor size were recorded from the pathology reports. Pathologic tumor size (pT) was accepted as the gold standard.</p><p><strong>Results: </strong>The mean pT was 21.1±14.9 (2.7-100) mm in Luminal A tumors, 20.6±12.6 (2-70) mm in Luminal B tumors, 26.3±14.7 (6-80) mm in HER-2(+) tumors, 26.3±14.7 (8-125) mm in triple (-) (TN) tumors. The highest agreement in invasive tumors was obtained with MRI (MRI r:0.831, US r:0.769, MMG r:0.650). In DCIS cases, the agreement was strong with MRI (r:0.770) and intermediate with MMG and US (r:0.517 and r:0.593, respectively). In mixed tumors, agreement was strong with MRI (r:0.817), intermediate with US (r:0.656), and low with MMG (r:0.499). Based on molecular subtypes, MRI had a strong correlation (r>0.7) in both invasive and mixed tumors of all subtypes. US had a strong correlation in all invasive tumors (r>0.7). The correlation was intermediate in Luminal mixed tumors. Mammography had a strong correlation only in invasive Luminal A tumors (r>0.7), and an intermediate correlation in the other invasive tumor subtypes. Regarding mixed tumors, its correlation level was intermediate in Luminal B and TN tumors, and low in Luminal A and HER-2(+) tumors.</p><p><strong>Conclusion: </strong>This multicenter study shows that MRI is the most reliable method for determining preoperative tumor size of invasive and in-situ tumors and all molecular subtypes. The correlation levels of all modalities decreased in pure and mixed DCIS cases, however the difference was minimal with MRI.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRS and Optical Imaging Studies of Therapeutic Response to Combination Therapy Targeting BRAF/MEK in Murine Melanomas.","authors":"Pradeep Kumar Gupta, Lin Z Li, Dinesh Kumar Singh, Skyler Nova, Fernando Arias-Mendoza, Stepan Orlovskiy, Sanjeev Chawla, David S Nelson, Michael D Farwell, Kavindra Nath","doi":"10.1016/j.acra.2025.01.035","DOIUrl":"https://doi.org/10.1016/j.acra.2025.01.035","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Melanoma, an aggressive skin cancer, often harbors BRAFV600E mutations driving tumor progression via the mitogen-activated protein kinase (MAPK) pathway. While targeted therapies like BRAF (dabrafenib) and MEK (trametinib) inhibitors have improved outcomes, resistance linked to metabolic reprogramming remains a challenge. This study investigates metabolic changes induced by dual BRAF/MEK inhibition in a BRAFV600E-mutant murine melanoma model using magnetic resonance spectroscopy (MRS), optical redox imaging (ORI), and biochemical assays. We aim to identify metabolic biomarkers for predicting therapeutic response or resistance.</p><p><strong>Materials and methods: </strong>YUMM1.7 murine melanoma cells and tumored mice were treated with dabrafenib and trametinib. ORI assessed mitochondrial redox status by measuring reduced nicotinamide adenine dinucleotide (NADH), oxidized flavoproteins (Fp), and the redox ratio (Fp/(NADH+Fp)) in vitro. Glucose consumption and lactate production were analyzed using a YSI Biochemical Analyzer. In vivo metabolic changes were monitored via ¹H and ³¹P MRS, evaluating lactate, alanine, pH, βNTP/Pi, and total NAD(P)(H), which represents combined oxidized nicotinamide adenine dinucleotide (NAD⁺), NADH, and reduced nicotinamide adenine dinucleotide phosphate (NADPH).</p><p><strong>Results: </strong>Under the combined therapeutic regimen of dabrafenib and trametinib, YUMM1.7 murine melanoma cells exhibited significant inhibition of lactate generation, non-significant reduction of glucose utilization, decreased intracellular levels of NADH and total NAD(P)(H), and more oxidized redox status in vitro, which can be interpreted as inhibition of the Warburg effect and improved OXPHOS efficiency by targeting BRAF/MEK signaling activities. Furthermore, YUMM1.7 mouse tumors demonstrated less tissue acidification and improved bioenergetics (βNTP/Pi), in agreement with the in vitro data.</p><p><strong>Conclusion: </strong>MRS, ORI, and biochemical assays identified critical metabolic changes, highlighting potential biomarkers and supporting the integration of metabolic inhibitors with MAPK-targeted therapies to improve clinical outcomes.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}