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Evaluation of a Virtual Reality CT-Guided Focal Liver Biopsy Module 虚拟现实ct引导局灶性肝活检模块的评估。
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.09.050
Blaire K. Rikard BS, MMSc-MEd , David N. Williams PhD , Kate Donovan PhD, MBA, MS , Ivan Dimov MD, MSc , Minh-Thuy Nguyen MD , Anjali Dasari , Jonathan G. Martin MD , Raul N. Uppot MD

Rationale and Objectives

This study evaluated a novel, virtual reality (VR) computed tomography (CT)-guided focal liver biopsy module for improving residents’ knowledge and confidence.

Materials and Methods

Interventional radiology (IR) residents (n = 18) were divided into a control group (PGY-1s) and an intervention group (PGY-2s and PGY-3s). All participants completed pre-, post-, and one-month surveys of confidence and a test of knowledge. The intervention group completed the CT-guided focal liver biopsy VR module between surveys on two occasions. When the intervention group performed the procedure in the VR environment, procedure length, number of scans, and accuracy of needle placement were recorded. Exam scores, confidence ratings, and VR headset performance metrics were analyzed using Wilcoxon signed-rank tests.

Results

The control group demonstrated no significant changes at any timepoint. The intervention group demonstrated significant knowledge gains pre- to post-survey (p = 0.03) with no significant change at follow-up (p = 0.09). Confidence in ordering steps and performing the procedure increased significantly pre- to post- (p = 0.03 vs p = 0.02) and pre- to final- (p = 0.01 vs p = 0.01). VR needle placement accuracy was stable at one month (p = 0.64) though scan counts (p = 0.16) and completion times (p = 0.03) increased.

Conclusion

The VR module improved residents’ knowledge and confidence with gains remaining stable at one month, despite a decline in VR-specific motor skills. These findings demonstrate the benefits of VR as a teaching tool.
基本原理和目的:本研究评估了一种新型的、虚拟现实(VR)计算机断层扫描(CT)引导的局灶性肝活检模块,以提高居民的知识和信心。材料与方法:将18名介入放射科住院医师分为对照组(pgy -1)和干预组(pgy -2和pgy -3)。所有的参与者都完成了一个月前、一个月后和一个月的信心调查和知识测试。干预组两次在调查间隙完成ct引导的局灶肝活检VR模块。当干预组在虚拟现实环境下进行手术时,记录手术时间、扫描次数和针头放置的准确性。使用Wilcoxon符号秩检验分析考试分数、信心评级和VR耳机性能指标。结果:对照组各时间点无明显变化。干预组在调查前后有显著的知识增益(p=0.03),随访时无显著变化(p=0.09)。排序步骤和执行程序的信心在术前至术后(p=0.03 vs p=0.02)和术前至术后(p=0.01 vs p=0.01)显著增加。尽管扫描次数(p=0.16)和完成时间(p=0.03)增加,但VR针头放置精度在1个月时稳定(p=0.64)。结论:尽管VR特定的运动技能有所下降,但VR模块提高了居民的知识和信心,并且在一个月后收益保持稳定。这些发现证明了虚拟现实作为教学工具的好处。
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引用次数: 0
18F-FDG PET Radiomic Analysis to Predict Occult Liver Metastases of Pancreatic Ductal Adenocarcinoma 18F-FDG PET放射组学分析预测胰腺导管腺癌隐匿性肝转移。
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.10.024
Jingtao Chen , Zhiang Zhang , Ze Jin , Pengcheng Ma , Zhichen Jiang , Chao Lu , Qicong Zhu , Yiping Mou , Weiwei Jin

Rationale and Objectives

To develop and validate a preoperative predictive model for occult liver metastases (OLM) in pancreatic ductal adenocarcinoma (PDAC) using fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) radiomics.

Material and Methods

This retrospective study included 117 patients with PDAC who underwent preoperative 18F-FDG PET/CT and surgical resection. OLM was defined as liver metastases detected during surgery or within 6 months postoperatively. A fully automated pancreas segmentation strategy was employed, and radiomic features were extracted from PET images. Three machine learning models (logistic regression, multilayer perceptron, and adaptive boosting) were developed and compared to a clinical model incorporating jaundice, metabolic tumor diameter, and maximum standardized uptake value. A fusion model integrating PET radiomic features with clinical variables was subsequently constructed. Model performance was evaluated using receiver operating characteristic curves and decision curve analysis.

Results

Among the 117 patients, 15.4% (n = 18) had OLM. The logistic regression radiomics model demonstrated favorable predictive performance (area under the curve [AUC]: 0.936 in the testing cohort) compared to a clinical model based on conventional parameters (AUC: 0.755, P<0.001). Subgroup analyses confirmed robustness across different jaundice statuses, tumor locations, and carbohydrate antigen 19–9 levels. The fusion model that integrates radiomic and clinical features provides a comprehensive tool for preoperative risk stratification, with the potential to guide personalized treatment strategies.

Conclusion

In this exploratory study, the 18F-FDG PET radiomics model demonstrates promising predictive performance for OLM in PDAC, outperforming conventional clinical parameters. It shows potential as a valuable tool for preoperative risk stratification and may help inform personalized treatment planning.
基本原理和目的:利用氟-18氟脱氧葡萄糖正电子发射断层扫描(18F-FDG PET)放射组学技术,建立并验证胰腺导管腺癌(PDAC)隐匿性肝转移(OLM)的术前预测模型。材料和方法:本回顾性研究纳入117例术前行18F-FDG PET/CT和手术切除的PDAC患者。OLM定义为手术中或术后6个月内发现的肝转移。采用全自动胰腺分割策略,从PET图像中提取放射学特征。开发了三种机器学习模型(逻辑回归、多层感知器和自适应增强),并将其与包含黄疸、代谢性肿瘤直径和最大标准化摄取值的临床模型进行了比较。随后构建了PET放射学特征与临床变量的融合模型。采用受试者工作特征曲线和决策曲线分析对模型性能进行评价。结果:117例患者中,OLM发生率为15.4% (n=18)。与基于常规参数的临床模型(AUC: 0.755)相比,logistic回归放射组学模型具有较好的预测效果(测试队列中曲线下面积[AUC]: 0.936)。结论:在本探索性研究中,18F-FDG PET放射组学模型对PDAC中OLM的预测效果较好,优于常规临床参数。它显示了作为术前风险分层的有价值的工具的潜力,并可能有助于告知个性化的治疗计划。
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引用次数: 0
Individualizing Radiation Risk in Lung Cancer Screening: Towards Precision Dosimetry 肺癌筛查中的个体化辐射风险:迈向精确剂量学。
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.10.003
Harleen Kaur, Ritu R. Gill MD, MPH
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-01
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引用次数: 0
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Academic Radiology
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