Academic Radiology最新文献_第5页

In Reply: Toward Multilingual, Patient-Centered AI Translations of Radiology Reports 回复：面向多语言、以患者为中心的放射学报告人工智能翻译。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-10-30 DOI: 10.1016/j.acra.2025.10.018

André Euler MD, MHBA, EBCR, Dusan Pisarcik, Rahel A. Kubik-Huch

引用次数: 0

A Multi-view Deep Survival Combined Model for Predicting Stroke Recurrence in Symptomatic Intracranial Atherosclerosis 多视角深度生存联合模型预测症状性颅内动脉粥样硬化卒中复发。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-15 DOI: 10.1016/j.acra.2025.10.052

Ziang Li , Tingting Huang , Lan Zhang , Xiaoyang Zhai , Qiuyi Zhao , Hanqi Lu , Qian Xu , Lin Han , Jie Wang , Gang Zhang , Yu Gao

Background

Symptomatic intracranial atherosclerotic stenosis (sICAS) is associated with a high risk of stroke recurrence. Current risk stratification approaches based on high-resolution vessel wall imaging (HR-VWI) remain dependent on subjective human assessment, which limits their precision.

Methods

From June 2020 to December 2024, HR-VWI images were retrospectively collected from 363 patients with sICAS across 2 medical institutions. Among the 363 patients, there were 79 cases of stroke recurrence (21.76%) and 284 cases without recurrence (78.24%). The cohort was divided into a Training/Validation set (n = 290) and a Test set (n = 73). Using the T1-weighted contrast-enhanced sequence, we developed a Multi-View Deep Survival Combined Model. This model employs a Vision Transformer and radiomics to analyze MR images and uses DeepSurv to extend the modeling capacity of the traditional Cox proportional hazards model. It enables the prediction of stroke recurrence risk related to intracranial culprit plaques. Model performance was evaluated using time-dependent receiver operating characteristic curves and the C-index. Additionally, decision curve analysis and calibration curves were used to comprehensively validate the model’s practical value and clinical application prospects.

Results

The Combined Model exhibited superior predictive performance, achieving a C-index of 0.872 (95% CI: 0.785–0.958) in the internal validation set and 0.803 (95% CI: 0.711–0.895) in the external test set. This performance significantly outperformed that of clinical models, radiomics models, and standalone deep learning models. The model also demonstrated excellent time-dependent predictive accuracy for 1, 2, and 3-year recurrence (area under the curve: 0.841, 0.870, and 0.802, respectively). Calibration and decision curve analysis confirmed the model’s clinical utility.

Conclusion

By integrating automated multi-view deep feature learning and DeepSurv-based survival analysis, the Combined Model provides a robust and objective tool for stratifying recurrence risk in patients with sICAS. It outperforms conventional methods and holds significant potential for guiding personalized secondary prevention strategies.

背景：症状性颅内动脉粥样硬化性狭窄（sICAS）与卒中复发的高风险相关。目前基于高分辨率血管壁成像（HR-VWI）的风险分层方法仍然依赖于主观的人类评估，这限制了它们的准确性。方法：回顾性收集2020年6月至2024年12月2家医疗机构363例sICAS患者的HR-VWI图像。363例患者中卒中复发79例（21.76%），无复发284例（78.24%）。将队列分为训练/验证集（n=290）和测试集（n=73）。使用t1加权对比度增强序列，我们开发了一个多视图深度生存组合模型。该模型采用Vision Transformer和radiomics对MR图像进行分析，并使用DeepSurv扩展了传统Cox比例风险模型的建模能力。它能够预测颅内罪魁祸首斑块相关的卒中复发风险。使用随时间变化的接收者工作特征曲线和c指数来评估模型的性能。采用决策曲线分析和标定曲线综合验证模型的实用价值和临床应用前景。结果：联合模型具有较好的预测性能，内部验证集的c指数为0.872 (95% CI: 0.785-0.958)，外部检验集的c指数为0.803 （95% CI: 0.711-0.895）。该性能明显优于临床模型、放射组学模型和独立深度学习模型。该模型还对1年、2年和3年的复发表现出极好的随时间变化的预测精度（曲线下面积分别为0.841、0.870和0.802）。校正和决策曲线分析证实了该模型的临床实用性。结论：通过集成自动化多视图深度特征学习和基于deepsurv的生存分析，联合模型为sICAS患者的复发风险分层提供了一个强大而客观的工具。它优于传统方法，并具有指导个性化二级预防战略的巨大潜力。

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引用次数: 0

Yttrium-90 Radioembolization for Androgen-Independent Prostate Cancer Metastasis to the Liver 氚-90放射栓塞治疗雄激素非依赖型前列腺癌肝转移。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.acra.2025.11.025

Mohamad M. Alzein MD , Andrew C. Gordon MD, PhD , Maha H. Hussain MD , Riad Salem MD , Robert J. Lewandowski MD, FSIR

Rationale and Objectives

This study reports outcomes of patients undergoing transarterial radioembolization (TARE) utilizing yttrium-90 (Y90) for androgen-independent prostate cancer liver metastasis.

Materials and Methods

A retrospective review was conducted for seven patients treated with TARE between 2007 and January 2024. Index tumor response was described through the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Survival analysis was calculated by the Kaplan–Meier method for index tumor, liver, extra-hepatic, and time-to-progression, as well as overall survival from day of TARE. Adverse events within one month of TARE were evaluated by the Common Terminology Criteria for Adverse Events (CTCAE) version 5.

Results

The median age of our cohort was 60.9 years (range, 52.3–79.2 years). The index tumor was treated with a median dose of 95.0 Gy (range, 28.7–300.7 Gy) and activity of 1.2 GBq (range, 0.7–2.5 GBq). Imaging follow up was completed for 86% (n = 6), with one early death. By RECIST criteria, 50% (n = 3) of patients achieved partial response and 50% (n = 3) of patients had stable disease as their final imaging responses. No index tumor progressed based on the RECIST criteria. Median progression was 2.3 (range, 1.3–6.9), 2.8 (range, 1.3–6.9), and 7.0 (range, 1.3–22.1) months for time-to-, hepatic, and extrahepatic progression, respectively. Median overall survival was 16.2 months (range, 1.0–93.2 months). One patient died within 30 days of the procedure. One patient reported CTCAE grade 3 effect: fatigue (n = 1).

Conclusion

TARE demonstrates antitumor activity and manageable toxicity in androgen-independent prostate cancer liver metastasis. However, optimal treatment timing remains uncertain.

理由和目的：本研究报告了使用钇-90 （Y90）进行经动脉放射栓塞治疗雄激素不依赖型前列腺癌肝转移的患者的结果。材料与方法：对2007年至2024年1月7例TARE患者进行回顾性分析。指标肿瘤反应通过实体肿瘤反应评价标准（RECIST） 1.1版进行描述。生存分析采用Kaplan-Meier法计算肿瘤指数、肝脏、肝外、进展时间以及TARE当天的总生存期。根据不良事件通用术语标准（CTCAE）第5版评估TARE一个月内的不良事件。结果：我们队列的中位年龄为60.9岁（范围52.3-79.2岁）。指数肿瘤的治疗中位剂量为95.0 Gy（范围28.7-300.7 Gy），活性为1.2 GBq（范围0.7-2.5 GBq）。86% （n = 6）的患者完成了影像学随访，其中1例早期死亡。根据RECIST标准，50% （n = 3）的患者达到部分缓解，50% （n = 3）的患者最终影像学反应为病情稳定。根据RECIST标准，没有指标肿瘤进展。中位进展分别为2.3个月（范围，1.3-6.9）、2.8个月（范围，1.3-6.9）和7.0个月（范围，1.3-22.1）。中位总生存期为16.2个月（范围1.0-93.2个月）。一名患者在手术后30天内死亡。1例患者报告CTCAE 3级效应：疲劳（n = 1）。结论：TARE在雄激素非依赖型前列腺癌肝转移中具有抗肿瘤活性和可控制的毒性。然而，最佳治疗时机仍不确定。

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引用次数: 0

MRI-based Intratumoral and Peritumoral Habitat Radiomics for Early Prediction of Pathologic Complete Response in Breast Cancer: A Multicenter Study 基于mri的肿瘤内和肿瘤周围栖息地放射组学用于乳腺癌病理完全缓解的早期预测：一项多中心研究。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.acra.2025.12.012

Bin Zhang , Shumao Zhang , Guang Tian , Haohan Wang , Deyuan Zhu , Yaodan Ban , Benqiang Yang

Rationale and Objectives

Tumor heterogeneity is a main driver of varied treatment responses for neoadjuvant therapy (NAT) in breast cancer, yet conventional radiomics often overlooks intratumoral heterogeneity. This study aims to develop and validate a habitat radiomics model derived from pretreatment MRI for noninvasive prediction of pathologic complete response (pCR) in breast cancer patients undergoing NAT.

Materials and Methods

This retrospective multicenter study included 301 patients with breast cancer who underwent NAT followed by surgery. Patients were assigned to a training set (n=143), internal validation set (n=62), and two external testing sets (n=96). Habitat subregions were generated via K-means clustering, and concentric peritumoral regions (3 mm, 5 mm, and 7 mm) were delineated. Several models were developed: a clinical model, an intratumoral radiomics model, a habitat radiomics model, three peritumoral models, and a combined model. Models were evaluated using the area under the receiver operating characteristic curve (AUC), while calibration and decision curves were used to assess model reliability and clinical applicability. Model interpretability was assessed using Shapley Additive exPlanations (SHAP).

Results

The habitat radiomics model achieved AUCs of 0.931 (training), 0.850 (internal validation), 0.811 (external test 1), and 0.802 (external test 2), outperforming both global and peritumoral radiomics models. The combined model integrating 3 mm peritumoral features and clinical factors achieved higher AUCs of 0.957, 0.871, 0.842, and 0.853, respectively. SHAP analysis revealed that four of the top five contributing features originated from one dominant intratumoral habitat, highlighting habitat subregions as an important predictive marker of pCR.

Conclusion

MRI-based habitat radiomics enables noninvasive prediction of pCR by capturing spatial heterogeneity within and around tumors. This approach may improve individualized treatment planning in breast cancer.

基本原理和目的：肿瘤异质性是乳腺癌新辅助治疗（NAT）不同治疗反应的主要驱动因素，然而传统放射组学常常忽略肿瘤内异质性。本研究旨在建立和验证一种基于预处理MRI的栖息地放射组学模型，用于无创预测乳腺癌NAT患者的病理完全缓解（pCR）。材料和方法：本回顾性多中心研究包括301例接受NAT手术的乳腺癌患者。患者被分配到一个训练集（n=143）、一个内部验证集（n=62）和两个外部测试集（n=96）。通过K-means聚类生成生境亚区，并圈定了肿瘤周围3 mm、5 mm和7 mm的同心区域。开发了几种模型：临床模型，肿瘤内放射组学模型，栖息地放射组学模型，三个肿瘤周围模型和一个组合模型。采用受试者工作特征曲线下面积（AUC）评价模型，采用校准曲线和决策曲线评价模型的可靠性和临床适用性。采用Shapley加性解释（SHAP）评价模型可解释性。结果：生境放射组学模型的auc分别为0.931（训练）、0.850（内部验证）、0.811（外部测试1）和0.802（外部测试2），优于全局和肿瘤周围放射组学模型。结合3 mm肿瘤周围特征与临床因素的联合模型auc较高，分别为0.957、0.871、0.842、0.853。SHAP分析显示，前5个贡献特征中有4个来自一个主要的肿瘤内栖息地，突出了栖息地亚区域是pCR的重要预测标记。结论：基于mri的栖息地放射组学可以通过捕获肿瘤内部和周围的空间异质性来实现非侵入性pCR预测。这种方法可以改善乳腺癌的个体化治疗计划。

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引用次数: 0

A RRA Perspective on Advanced Imaging in Radiology: Emerging Technologies for Precision and Personalized Care 从RRA的角度看放射学的先进成像：精确和个性化护理的新兴技术。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2026-03-02 DOI: 10.1016/j.acra.2026.01.046

Ceylan Z. Cankurtaran MD , Joseph Fotos MD , Melis Ozkan BS , Vidya Sankar Viswanathan MBBS , Daphne Zhu BS , Jessica M. Sin MD, PhD , Monica Cheng MD , Nicole Brofman MD , Anna Rozenshtein MD , Michele Retrouvey MD

Recent advancements in imaging technologies are poised to redefine the diagnostic and therapeutic role of radiology. In part 2 of this review series, we outline emerging innovations with transformative clinical potential, including photon-counting detector CT for ultra-high-resolution, spectral imaging; ultra-high-field MRI systems (≥7 T); and the expanding field of theranostics. We also examine nanotechnology-enhanced imaging agents, the integration of AI-driven opportunistic screening, and radiogenomics to enable precision diagnostics and early disease detection. Together, these developments represent a paradigm shift toward more personalized, data-rich, and preventative imaging approaches. Challenges in implementation, such as safety, cost, and workflow integration, are discussed with an emphasis on collaboration and infrastructure to support sustained clinical translation.

影像技术的最新进展将重新定义放射学的诊断和治疗作用。在本综述系列的第2部分，我们概述了具有变革临床潜力的新兴创新，包括用于超高分辨率光谱成像的光子计数检测器CT；超高场MRI系统（≥7T）；以及不断扩大的治疗学领域。我们还研究了纳米技术增强的显像剂、人工智能驱动的机会性筛查和放射基因组学的整合，以实现精确诊断和早期疾病检测。总之，这些发展代表了向更加个性化、数据丰富和预防性成像方法的范式转变。讨论了实现中的挑战，如安全性、成本和工作流集成，重点是协作和基础设施，以支持持续的临床翻译。

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引用次数: 0

Bridging the Transparency Gap: Enhancing Disclosure of Generative AI Tools in Radiology Research Manuscripts 弥合透明度差距：加强放射学研究手稿中生成人工智能工具的披露。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-14 DOI: 10.1016/j.acra.2025.10.053

Herlina Uinarni , Laith Saheb , Nigora Djuraeva , Aria Diba

引用次数: 0

Integrating Deep Feature Extraction and MRI Radiomics for Survival Prediction in Breast Cancer After Neoadjuvant Chemotherapy 结合深度特征提取和MRI放射组学预测乳腺癌新辅助化疗后的生存。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-17 DOI: 10.1016/j.acra.2025.10.050

Quan Yuan , Zhipeng Hong , Rongjie Ye , Peidong Yang , Juli Lin , Xinyu Jiang , Huilei Qiu , Aoyu Liu , Hao Yu , Fei Gao , Pengfei He , Kaizhou Chen , Jian Cai , Xinru Xie , Wenkang You , Haiyu Yuan , Kejie Zhang , Shuqi Yang , Boqian Yu , Xinfa Huang , Ming Niu

Rationale and Objectives

Breast cancer (BC) remains a leading contributor to the global cancer burden among women, with neoadjuvant chemotherapy (NAC) established as the standard of care for early-stage disease. However, substantial interpatient variability in treatment outcomes persists, primarily driven by inherent tumor biological heterogeneity. This underscores an urgent need for more precise prognostic tools to optimize clinical decision-making.

Materials and Methods

This multicenter study included 216 BC patients who completed NAC, with no overlap in datasets with previous research. We extracted four-dimensional data: clinical characteristics, pathomics features, deep learning-derived pathological features (via ResNet50), and multiparametric MRI (mpMRI) radiomics. A multimodal Cox model integrating deep feature representations and radiomic variables was constructed to combine these data. Notably, this approach differs from prior studies, which have predominantly focused on single-modality inputs (eg, radiomics or pathomics alone) or short-term endpoints such as pathological complete response (pCR).

Results

The proposed model, leveraging deep feature representations derived from CNNs and radiomic fusion, achieved superior prognostic accuracy in predicting 5-year and 7-year overall survival (OS) compared to both single-modality models and findings from previous research. For 5-year OS, it achieved an area under the receiver operating characteristic curve (AUC) of 0.890 in the training set and 0.820 in the validation set; for 7-year OS, the AUC values were 0.910 (training) and 0.870 (validation), with statistically significant superiority over unidimensional models. Calibration curves and decision curve analyses further confirmed its robust clinical utility.

Conclusion

The multimodal integration of imaging, pathology, and clinical data, particularly the inclusion of CNN-derived deep features, provides complementary information that improves survival prediction in NAC-treated BC patients. This represents a meaningful advancement over existing models that rely on single-modality data or focus on short-term outcomes.

Research registration unique identifying number

The study is registered at https://www.chictr.org.cn and has acquired only Identifier: ChiCTR2500098023.

理由和目的：乳腺癌（BC）仍然是全球女性癌症负担的主要贡献者，新辅助化疗（NAC）已被确立为早期疾病的标准治疗。然而，由于固有的肿瘤生物学异质性，治疗结果仍然存在很大的患者间差异。这强调了迫切需要更精确的预后工具来优化临床决策。材料和方法：本多中心研究包括216例完成NAC的BC患者，数据集与以往研究无重叠。我们提取了四维数据：临床特征、病理特征、深度学习衍生的病理特征（通过ResNet50）和多参数MRI （mpMRI）放射组学。构建了融合深度特征表示和放射学变量的多模态Cox模型。值得注意的是，这种方法不同于先前的研究，后者主要侧重于单模态输入（例如，放射组学或单独的病理学）或短期终点，如病理完全缓解（pCR）。结果：与单模态模型和先前的研究结果相比，该模型利用来自cnn和放射学融合的深度特征表示，在预测5年和7年总生存（OS）方面取得了更高的预后准确性。对于5年OS，在训练集中实现了接收者工作特征曲线下面积（AUC）为0.890，在验证集中实现了0.820；对于7年OS， AUC值分别为0.910（训练）和0.870（验证），与一维模型相比具有统计学上的显著优势。校正曲线和决策曲线分析进一步证实了该方法的临床应用价值。结论：影像学、病理学和临床数据的多模式整合，特别是cnn衍生的深部特征，提供了补充信息，提高了nac治疗的BC患者的生存预测。与依赖单模态数据或关注短期结果的现有模型相比，这是一个有意义的进步。研究注册唯一识别码：本研究注册于https://www.chictr.org.cn，并已获得唯一识别码：ChiCTR2500098023。

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引用次数: 0

The Emergence of Foundation Models in U.S. Radiology: A Narrative Review of Clinical Utility, Safety, and Evaluation 美国放射学基础模型的出现：临床效用、安全性和评估的叙述性回顾。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1016/j.acra.2025.10.063

Pranay Chetan Uppuluri MD (Chief Radiologist) , Carina W. Yang MD (Associate Professor)

Foundation Models (FMs) mark a significant evolution in medical AI, enabling multimodal and multitask performance across text and imaging. Radiology, with its structured data formats and early adoption of AI, is uniquely positioned to benefit from FM capabilities. However, despite promising technical advances, questions remain about their clinical readiness, safety, and regulatory oversight. This narrative review explores the development, utility, and implementation challenges of FMs in U.S. radiology. Literature from PubMed, Scopus, arXiv, and IEEE Xplore (January 2022 to May 2025) was synthesized to highlight architectural trends, clinical applications, evaluation methods, and regulatory developments. U.S.-based models like CheXzero, BioMedCLIP, and Med-PaLM demonstrate strong diagnostic and reporting performance but face key limitations—including lack of FDA clearance, limited external validation, and integration barriers with PACS/RIS systems. Safety issues such as hallucination, automation bias, and underperformance in edge cases persist. While human-in-the-loop frameworks, federated learning, and emerging reporting standards show promise, institutional readiness and regulatory clarity remain fragmented. We propose a roadmap that includes continuous monitoring, equity-focused design, and a national FM registry to guide responsible deployment. Radiology’s digital maturity makes it a critical testbed for foundational AI integration—offering lessons for broader clinical adoption.

基础模型（FMs）标志着医疗人工智能的重大发展，实现了跨文本和图像的多模式和多任务性能。放射学以其结构化的数据格式和早期采用的人工智能，具有独特的定位，可以从FM功能中受益。然而，尽管技术进步有希望，但它们的临床准备、安全性和监管监督方面仍然存在问题。这篇叙述性综述探讨了FMs在美国放射学中的发展、应用和实施挑战。综合了PubMed、Scopus、arXiv和IEEE explore（2022年1月至2025年5月）的文献，以突出建筑趋势、临床应用、评估方法和监管发展。基于美国的模型，如CheXzero、BioMedCLIP和Med-PaLM表现出强大的诊断和报告性能，但面临关键限制，包括缺乏FDA许可、有限的外部验证以及与PACS/RIS系统的集成障碍。安全问题，如幻觉、自动化偏差和在边缘情况下表现不佳，仍然存在。虽然人在循环框架、联合学习和新兴的报告标准显示出希望，但制度准备和监管清晰度仍然是分散的。我们提出了一个路线图，其中包括持续监测、以权益为中心的设计和一个国家FM注册表，以指导负责任的部署。放射学的数字化成熟度使其成为基础人工智能集成的关键测试平台，为更广泛的临床应用提供经验教训。

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引用次数: 0

Identifying Patients with EGFR-Mutated Oligometastatic NSCLC Suitable for Third-Generation EGFR-TKI Combined with Thoracic Radiotherapy Using Nomograms Based on CT Radiomic and Clinicopathological Factors 基于CT放射学和临床病理因素的nomogram鉴别egfr突变的少转移性NSCLC患者适合第三代EGFR-TKI联合胸部放疗。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1016/j.acra.2025.11.009

Xinhang Gu , Junfeng Zhao , Jiaxiao Geng , Ying Li , Chengxin Liu

Rationale and Objectives

It remains uncertain whether all patients with oligometastatic non-small cell lung cancer (NSCLC) benefit from the combination of third-generation EGFR-TKIs and TRT. This study aimed to identify which patients are most likely to benefit from combined third-generation EGFR-TKI and TRT, and which patients may safely omit TRT, thereby guiding clinical decision-making and optimizing prognosis.

Materials and Methods

A total of 338 patients with EGFR-mutated oligometastatic NSCLC who received first-line third-generation EGFR-TKI treatment were included. These patients were divided into training and validation cohorts. Univariate and multivariate analyses incorporating clinicopathological variables and radiomic features were performed to identify independent prognostic factors for progression-free survival (PFS) and overall survival (OS). A predictive nomogram was developed based on these factors and validated using receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

EGFR exon 21 L858R mutation, brain metastasis, neutrophil-to-lymphocyte ratio ≥ 4.39, and the radiomic score (Rad-score) were identified as independent risk factors for PFS. Age > 60 years, EGFR exon 21 L858R mutation, brain metastasis, monocyte-to-lymphocyte ratio > 0.26, and Rad-score were OS independent predictors. In the training cohort, the nomogram achieved excellent predictive performance with AUCs of 0.858, 0.834, and 0.785 for 1-, 2-, and 3-year PFS, and 0.882, 0.868 and 0.877 for 2-, 3-, and 4-year OS, respectively. In the validation cohort, respective AUCs were 0.800, 0.740, and 0.734,and 0.835, 0.729, 0.766, confirming good discrimination. The model successfully stratified patients into low- and high-risk groups. High-risk patients derived significant PFS (p < 0.001) and OS (p < 0.001) benefits from TRT, whereas low-risk patients did not show significant improvements in PFS (p = 0.056) or OS (p = 0.093) with TRT.

Conclusion

We established and confirmed a robust predictive nomogram that integrates clinicopathological and radiomic factors to stratify patients with first-line therapy for EGFR-mutant oligometastatic NSCLC involving third-generation EGFR-TKIs. This approach helps determine which patients may gain the greatest benefit from combined TRT and help avoid unnecessary TRT in low-risk patients, supporting precision treatment strategies.

理由和目的：尚不确定是否所有低转移性非小细胞肺癌（NSCLC）患者都能从第三代EGFR-TKIs和TRT联合治疗中获益。本研究旨在确定哪些患者最有可能从第三代EGFR-TKI和TRT联合治疗中获益，哪些患者可以安全地省略TRT，从而指导临床决策和优化预后。材料和方法：共纳入338例接受一线第三代EGFR-TKI治疗的egfr突变寡转移性NSCLC患者。这些患者被分为训练组和验证组。结合临床病理变量和放射学特征进行单因素和多因素分析，以确定无进展生存期（PFS）和总生存期（OS）的独立预后因素。基于这些因素建立了预测模态图，并通过受试者工作特征曲线、校准曲线和决策曲线分析进行了验证。结果：EGFR外显子21 L858R突变、脑转移、中性粒细胞与淋巴细胞比值≥4.39、放射组学评分（Rad-score）为PFS的独立危险因素。年龄bbb60岁，EGFR外显子21 L858R突变，脑转移，单核细胞与淋巴细胞比值>0.26和rad评分是OS的独立预测因素。在训练队列中，nomogram获得了很好的预测效果，1年、2年和3年PFS的auc分别为0.858、0.834和0.785,2年、3年和4年OS的auc分别为0.882、0.868和0.877。在验证队列中，auc分别为0.800、0.740、0.734,0.835、0.729、0.766，判别性良好。该模型成功地将患者分为低危组和高危组。高风险患者从TRT中获得了显著的PFS （p < 0.001）和OS （p < 0.001）益处，而低风险患者的PFS （p = 0.056）或OS （p = 0.093）没有显着改善。结论：我们建立并证实了一个整合临床病理和放射学因素的强大预测nomogram，用于对一线治疗egfr突变寡转移性NSCLC患者进行分层，涉及第三代EGFR-TKIs。这种方法有助于确定哪些患者可能从联合TRT中获益最大，并有助于避免对低风险患者进行不必要的TRT，支持精确的治疗策略。

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引用次数: 0

Impact of an Endovascular Simulator and Video Games on Medical Student Procedural Outcomes and Interventional Radiology Interest 血管内模拟器和视频游戏对医学生手术结果和介入放射学兴趣的影响。

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology

Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1016/j.acra.2025.10.045

Mina Dawod MD , Mensur Koso MD , Bidhu Sharma BS , Haris Mujovic BS , Amel Lilic BS , Matthew Yoder MD , Mina S. Makary MD

<div><h3>Rationale and Objectives</h3><div>To determine if student exposure to an endovascular simulator can increase confidence performing procedures and interest in interventional radiology (IR) and to assess if past and present video game experience confers improved procedural skills.</div></div><div><h3>Materials and Methods</h3><div>This IRB-approved prospective randomized control study evaluated medical student specialty interest and procedural performance on an endovascular stimulator before and after video game simulation. Participating students were required to complete a pre-procedure survey and an initial simulated procedure utilizing an endovascular simulator before being randomized to either a Video Game (VG) or control arm. Students then proceeded to complete a second simulated procedure and a post-procedure survey. Before starting the procedure, a standardized explanation was read to each participant. Students randomized to the VG arm would play a video game within a ten-minute slot between the two procedures, while control arm students would rest. Survey data included demographic information, history of video gaming, confidence performing endovascular procedures, and interest in procedural specialties. Primary outcomes included self-reported procedural confidence and specialty interest on a five-point scale (five being the highest). Secondary outcomes included procedural skill as measured by time to completion during the simulated procedures.</div></div><div><h3>Results</h3><div>A total of 52 medical students (mean age, 25.9 years; male 58%) participated in this study, with 26 students randomized to the VG arm and the remainder to the control arm. Of the total cohort, 31% (n=16) identified as first year medical students, 31% (n=16) as third year, 23% (n=12) as fourth year, 10% (n=5) as second year, and 6% (n=3) as leave of absence students. The cohort’s average confidence performing procedures prior to participation was 2.4 (out of five), interest in procedural specialties was 4.1, and interest in IR was 2.8. After participation in the study, confidence performing procedures rose by 57% to 3.8 (<em>p</em><0.0001), interest in procedural specialties rose by 8% to 4.4 (<em>p</em>=0.0016), and interest in IR increased by 28% to 3.6 (<em>p</em><0.0001). The collective cohort improved by an average of 23% between procedure one completion time and procedure two completion time (3 min 56 s to 3 min one second). The differences in the rate of improvement between the VG group and control group was not significant. Gender was found to be the only background variable to significantly correlate with procedural times (<em>p</em>=0.01).</div></div><div><h3>Conclusion</h3><div>Student confidence performing procedures significantly increased after participating in the study, as did student interest in procedural specialties in general and in IR specifically. A history of video games and prospective VG group participation did not confer proc

基本原理和目标：确定学生接触血管内模拟器是否可以增加对介入放射学（IR）的信心和兴趣，并评估过去和现在的电子游戏体验是否可以提高操作技能。材料和方法：这项经irb批准的前瞻性随机对照研究评估了医学生在视频游戏模拟前后在血管内刺激器上的专业兴趣和程序表现。参与研究的学生在被随机分配到视频游戏组（VG）或对照组之前，需要完成手术前调查和利用血管内模拟器进行的初始模拟手术。然后，学生们继续完成第二次模拟手术和手术后调查。在程序开始之前，向每个参与者宣读一份标准化的解释。随机分配到VG组的学生将在两个程序之间的10分钟时间内玩视频游戏，而对照组的学生则休息。调查数据包括人口统计信息、视频游戏史、进行血管内手术的信心以及对手术专业的兴趣。主要结果包括自我报告的程序信心和专业兴趣（五分制）（最高为五分）。次要结果包括程序技能，通过模拟过程中完成的时间来衡量。结果：共有52名医学生（平均年龄25.9岁，男性占58%）参加了本研究，其中26名学生随机分配到VG组，其余学生分配到对照组。在整个队列中，31% （n=16）为一年级医学生，31% （n=16）为三年级医学生，23% （n=12）为四年级医学生，10% （n=5）为二年级医学生，6% （n=3）为休学学生。队列在参与之前执行程序的平均信心为2.4（满分5分），对程序专业的兴趣为4.1，对IR的兴趣为2.8。参与研究后，执行程序的信心上升了57%，达到3.8。（结论：参与研究后，学生执行程序的信心显著增加，学生对一般程序专业的兴趣也显著增加，特别是对IR。）视频游戏的历史和预期的VG组参与并没有通过完成时间来衡量程序技能。

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