Academic Radiology最新文献_第7页

Corrigendum to ‘An MRI-based Radiomics Approach to Improve Breast Cancer Histological Grading’ [Acad Radiol 30 (2023) 1794-1804] “基于mri的放射组学方法改善乳腺癌组织学分级”的勘误表[Acad Radiol 30(2023) 1794-1804]。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.08.027

Meng Jiang MD , Chang-Li Li MD , Xiao-Mao Luo MD , Zhi-Rui Chuan MD , Rui-Xue Chen MD , Chao-Ying Jin MD

引用次数: 0

Practical Strategy to Mitigate Overdiagnosis in Asian Low-dose Computed Tomography Lung Cancer Screening 减轻亚洲低剂量ct肺癌筛查中过度诊断的实用策略。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.09.044

Yun-Ju Wu , Yi-Chi Hung , En-Kuei Tang , Fu-Zong Wu

Rationale and Objectives

Low-dose computed tomography (LDCT) screening reduces lung cancer mortality but may lead to overdiagnosis and overtreatment, especially among the Asian LDCT screening population presenting with subsolid nodules (SSNs). Thus, we explored whether implementing chest X-ray (CXR) triage as a preliminary screening tool can improve patient selection and reduce unnecessary interventions.

Materials and Methods

This retrospective cohort study compared patients who underwent direct LDCT screening with those who underwent LDCT and CXR triage. Medical records of 506 patients with SSNs ≤3 cm from January 2006 to December 2021 were reviewed. Primary outcomes included lung cancer prognosis, relapse, and histopathologic spectrum of lung adenocarcinoma to assess potential overdiagnosis. The CXR triage group consisted of patients who had both LDCT and CXR within 3 months, with CXR-visible nodules confirmed by surgical pathology. A 1:1 propensity score matching (PSM) was performed using age, sex, and nodule size, resulting in 97 matched pairs for balanced outcome comparison.

Results

After PSM, 194 patients were analyzed. In the direct LDCT group, 37.1% of resected lesions were adenocarcinoma in situ (AIS) or atypical adenomatous hyperplasia (AAH), indicating a higher likelihood of overdiagnosis. Conversely, the CXR triage group had no patients with AIS/AAH, a significant difference. No significant differences were observed in disease-free survival or relapse rates between groups during follow-up, suggesting that CXR triage may reduce overtreatment without negatively impacting short-term prognosis. Multivariate Cox regression revealed that age, sex, nodule size, smoking status, and screening method were not significant predictors of mortality. Kaplan–Meier survival analysis over 15 years showed similarly high survival rates in both groups, with no significant difference.

Conclusion

CXR triage is a preliminary step in LDCT screening for lung cancer, particularly in non-smoking Asian high-risk populations, to reduce unnecessary surgical procedures while preserving diagnostic effectiveness. Further prospective multicenter studies are needed to validate its long-term safety, cost-effectiveness, and acceptability in routine clinical practice.

理由和目的：低剂量计算机断层扫描（LDCT）筛查可降低肺癌死亡率，但可能导致过度诊断和过度治疗，特别是在亚洲LDCT筛查人群中出现亚实性结节（ssn）。因此，我们探讨了将胸部x线（CXR）分诊作为初步筛查工具是否可以改善患者选择并减少不必要的干预。材料和方法：本回顾性队列研究比较了接受直接LDCT筛查的患者与接受LDCT和CXR分诊的患者。回顾了2006年1月至2021年12月506例ssn≤3cm患者的病历。主要结局包括肺癌预后、复发和肺腺癌的组织病理学谱，以评估潜在的过度诊断。CXR分诊组包括在3个月内进行LDCT和CXR检查并经手术病理证实有CXR可见结节的患者。使用年龄、性别和结节大小进行1:1的倾向评分匹配（PSM），得到97对匹配的结果进行平衡比较。结果：经PSM治疗194例患者。在直接LDCT组中，37.1%的切除病灶为原位腺癌（AIS）或非典型腺瘤性增生（AAH），这表明过度诊断的可能性更高。相反，CXR分诊组没有AIS/AAH患者，差异有统计学意义。随访期间，两组无病生存率或复发率无显著差异，提示CXR分诊可减少过度治疗，而不会对短期预后产生负面影响。多因素Cox回归显示，年龄、性别、结节大小、吸烟状况和筛查方法不是死亡率的显著预测因素。超过15年的Kaplan-Meier生存分析显示，两组患者的生存率相似，没有显著差异。结论：CXR分类是LDCT筛查肺癌的初步步骤，特别是在非吸烟的亚洲高危人群中，可以减少不必要的外科手术，同时保持诊断的有效性。需要进一步的前瞻性多中心研究来验证其长期安全性、成本效益和在常规临床实践中的可接受性。

{"title":"Practical Strategy to Mitigate Overdiagnosis in Asian Low-dose Computed Tomography Lung Cancer Screening","authors":"Yun-Ju Wu , Yi-Chi Hung , En-Kuei Tang , Fu-Zong Wu","doi":"10.1016/j.acra.2025.09.044","DOIUrl":"10.1016/j.acra.2025.09.044","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Low-dose computed tomography (LDCT) screening reduces lung cancer mortality but may lead to overdiagnosis and overtreatment, especially among the Asian LDCT screening population presenting with subsolid nodules (SSNs). Thus, we explored whether implementing chest X-ray (CXR) triage as a preliminary screening tool can improve patient selection and reduce unnecessary interventions.</div></div><div><h3>Materials and Methods</h3><div>This retrospective cohort study compared patients who underwent direct LDCT screening with those who underwent LDCT and CXR triage. Medical records of 506 patients with SSNs ≤3cm from January 2006 to December 2021 were reviewed. Primary outcomes included lung cancer prognosis, relapse, and histopathologic spectrum of lung adenocarcinoma to assess potential overdiagnosis. The CXR triage group consisted of patients who had both LDCT and CXR within 3 months, with CXR-visible nodules confirmed by surgical pathology. A 1:1 propensity score matching (PSM) was performed using age, sex, and nodule size, resulting in 97 matched pairs for balanced outcome comparison.</div></div><div><h3>Results</h3><div>After PSM, 194 patients were analyzed. In the direct LDCT group, 37.1% of resected lesions were adenocarcinoma in situ (AIS) or atypical adenomatous hyperplasia (AAH), indicating a higher likelihood of overdiagnosis. Conversely, the CXR triage group had no patients with AIS/AAH, a significant difference. No significant differences were observed in disease-free survival or relapse rates between groups during follow-up, suggesting that CXR triage may reduce overtreatment without negatively impacting short-term prognosis. Multivariate Cox regression revealed that age, sex, nodule size, smoking status, and screening method were not significant predictors of mortality. Kaplan–Meier survival analysis over 15 years showed similarly high survival rates in both groups, with no significant difference.</div></div><div><h3>Conclusion</h3><div>CXR triage is a preliminary step in LDCT screening for lung cancer, particularly in non-smoking Asian high-risk populations, to reduce unnecessary surgical procedures while preserving diagnostic effectiveness. Further prospective multicenter studies are needed to validate its long-term safety, cost-effectiveness, and acceptability in routine clinical practice.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 255-265"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309998","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}

引用次数: 0

CT-Based Radiomics and Deep Learning for Preoperative Thyroid Nodule Classification: A Systematic Review, Meta-analysis, and Radiologist Comparison 基于ct的放射组学和深度学习用于术前甲状腺结节分类：系统回顾，荟萃分析和放射科医生比较。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.09.045

Nima Broomand Lomer , Amir Mahmoud Ahmadzadeh , Mohammad Amin Ashoobi , Sepideh Abdi , Amirhosein Ghasemi , Ali Gholamrezanezhad

Introduction

Computed tomography (CT) can evaluate thyroid cancer invasion into adjacent structures and is useful in identifying incidental thyroid nodules. Computer-aided diagnostic approaches may provide valuable clinical advantages in this domain. Here, we aim to evaluate the diagnostic performance of radiomics and deep-learning methods using CT imaging for preoperative nodule classification.

Methods

A comprehensive search of PubMed, Embase, Scopus, and Web of Science was conducted from inception to June 2, 2025. Study quality was assessed using QUADAS-2 and METRICS. A bivariate meta-analysis estimated pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). Two supplementary analyses compared AI model performance with radiologists and assessed diagnostic utility across CT imaging phases (plain, venous, arterial). Subgroup and sensitivity analyses explored sources of heterogeneity. Publication bias was evaluated using Deek’s funnel plot.

Results

The meta-analysis included 12 radiomics studies (sensitivity: 0.85, specificity: 0.83, PLR: 4.60, NLR: 0.19, DOR: 30.29, AUC: 0.894) and five deep-learning studies (sensitivity: 0.87, specificity: 0.93, PLR: 14.04, NLR: 0.15, DOR: 95.76, AUC: 0.911). Radiomics models showed low heterogeneity, while deep-learning models showed substantial heterogeneity, potentially due to differences in validation, segmentation, METRICS quality, and reference standards. Overall, these models outperformed radiologists, and models using plain CT images outperformed those based on arterial or venous phases.

Conclusion

Radiomics and deep-learning models have demonstrated promising performance in classifying thyroid nodules and may improve radiologists’ accuracy in indeterminate cases, while reducing unnecessary biopsies.

计算机断层扫描（CT）可以评估甲状腺癌对邻近结构的侵犯，并有助于识别偶发甲状腺结节。计算机辅助诊断方法可能在这一领域提供有价值的临床优势。在这里，我们的目的是评估放射组学和深度学习方法在术前结节分类中的诊断性能。方法：综合检索PubMed、Embase、Scopus和Web of Science自成立至2025年6月2日。采用QUADAS-2和METRICS评估研究质量。双变量荟萃分析估计了合并的敏感性、特异性、阳性和阴性似然比（PLR和NLR）、诊断优势比（DOR）和曲线下面积（AUC）。两项补充分析比较了人工智能模型与放射科医生的表现，并评估了CT成像各阶段（普通、静脉、动脉）的诊断效用。亚组分析和敏感性分析探讨了异质性的来源。采用Deek漏斗图评价发表偏倚。结果：meta分析包括12项放射组学研究（敏感性：0.85，特异性：0.83,PLR: 4.60, NLR: 0.19, DOR: 30.29, AUC: 0.894）和5项深度学习研究（敏感性：0.87，特异性：0.93,PLR: 14.04, NLR: 0.15, DOR: 95.76, AUC: 0.911）。放射组学模型显示出较低的异质性，而深度学习模型显示出较大的异质性，这可能是由于验证、分割、METRICS质量和参考标准的差异。总的来说，这些模型优于放射科医生，使用普通CT图像的模型优于基于动脉或静脉相的模型。结论：放射组学和深度学习模型在甲状腺结节分类方面表现良好，可以提高放射科医生在不确定病例中的准确性，同时减少不必要的活检。

{"title":"CT-Based Radiomics and Deep Learning for Preoperative Thyroid Nodule Classification: A Systematic Review, Meta-analysis, and Radiologist Comparison","authors":"Nima Broomand Lomer , Amir Mahmoud Ahmadzadeh , Mohammad Amin Ashoobi , Sepideh Abdi , Amirhosein Ghasemi , Ali Gholamrezanezhad","doi":"10.1016/j.acra.2025.09.045","DOIUrl":"10.1016/j.acra.2025.09.045","url":null,"abstract":"<div><h3>Introduction</h3><div>Computed tomography (CT) can evaluate thyroid cancer invasion into adjacent structures and is useful in identifying incidental thyroid nodules. Computer-aided diagnostic approaches may provide valuable clinical advantages in this domain. Here, we aim to evaluate the diagnostic performance of radiomics and deep-learning methods using CT imaging for preoperative nodule classification.</div></div><div><h3>Methods</h3><div>A comprehensive search of PubMed, Embase, Scopus, and Web of Science was conducted from inception to June 2, 2025. Study quality was assessed using QUADAS-2 and METRICS. A bivariate meta-analysis estimated pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). Two supplementary analyses compared AI model performance with radiologists and assessed diagnostic utility across CT imaging phases (plain, venous, arterial). Subgroup and sensitivity analyses explored sources of heterogeneity. Publication bias was evaluated using Deek’s funnel plot.</div></div><div><h3>Results</h3><div>The meta-analysis included 12 radiomics studies (sensitivity: 0.85, specificity: 0.83, PLR: 4.60, NLR: 0.19, DOR: 30.29, AUC: 0.894) and five deep-learning studies (sensitivity: 0.87, specificity: 0.93, PLR: 14.04, NLR: 0.15, DOR: 95.76, AUC: 0.911). Radiomics models showed low heterogeneity, while deep-learning models showed substantial heterogeneity, potentially due to differences in validation, segmentation, METRICS quality, and reference standards. Overall, these models outperformed radiologists, and models using plain CT images outperformed those based on arterial or venous phases.</div></div><div><h3>Conclusion</h3><div>Radiomics and deep-learning models have demonstrated promising performance in classifying thyroid nodules and may improve radiologists’ accuracy in indeterminate cases, while reducing unnecessary biopsies.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 98-116"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314130","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}

引用次数: 0

Balancing Advocacy and Education: A Complementary Perspective on Resident Unionization in Radiology 平衡宣传与教育：放射科住院医师工会化的互补视角。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.09.047

Aura María Ramírez Cabrera MD, Radiology Resident , María José Veloza Vega MD, Radiology

引用次数: 0

Automated Detection and Segmentation of Aortoiliac Calcified Plaques Using nnU-Net for Whole-Torso Atherosclerotic Burden Assessment on Non-Contrast and Contrast-Enhanced CT Scans 在非对比和增强CT扫描中使用nnU-Net自动检测和分割主动脉髂钙化斑块。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.10.010

Jianfei Liu PhD , Vivek Batheja MD , Pritam Mukherjee PhD , Tejas Sudharshan Mathai PhD , Peter C. Grayson MD , Perry J. Pickhardt MD , Ronald M. Summers MD,PhD

Rationale and Objectives

Cardiovascular disease (CVD) is closely associated with aortoiliac plaque burden, yet current research on its automated detection and segmentation has largely focused on plaque burden analysis using CT angiography. In this study, we present an automated method for aortoiliac plaque detection and segmentation that enables accurate quantification of calcified plaque burden on both non-contrast and contrast-enhanced CT scans.

Materials and Methods

The training data included 119 non-contrast whole-body PET-CT scans and 23 contrast-enhanced abdominopelvic CT urography scans, all obtained from our institution. The testing data comprised 99 contrast-enhanced thoracoabdominopelvic CT scans from the sarcopenia dataset; 93 from the prostate cancer dataset; 1214 paired non-contrast and contrast-enhanced abdominal CT scans from a renal donor cohort; 9199 non-contrast abdominal CT colonography scans from a second institution; and 1446 non-contrast chest CT scans from a third institution. The nnU-Net was used to train a model for aortoiliac plaque detection and segmentation. Detection accuracy was evaluated on non-contrast chest CT scans. Segmentation accuracy was assessed on CT scans with manually labeled plaque regions from the sarcopenia, prostate, renal donor, and CT colonography datasets. The correlation between Agatston scores on paired non-contrast and contrast-enhanced scans was evaluated in the renal donor cohort. Correlations between whole-torso calcified plaque burden (Agatston scores), demographics, and diseases were analyzed using multivariable analysis on the CT colonography dataset.

Results

Aortoiliac plaques were detected with 88.1% precision, 99.5% recall, and a 93.4% F1 score. Segmentation achieved Dice scores of 64.3–83.7% across two internal contrast-enhanced and two external non-contrast CT datasets, outperforming baseline methods by over 10% (p < 0.001). Agatston scores from paired CT scans showed strong correlation (R² = 0.99). Multivariate analysis showed calcified plaque burden assessment correlated with sex, age, BMI, and smoking (all p < 0.001), as well as alcohol abuse (p = 0.01). The calcified burden assessment was also correlated with CVD, heart failure, myocardial infarction (all p < 0.001), and type 2 diabetes (p = 0.03), but showed no correlation with cancer (p = 0.14) or femoral neck fracture (p = 0.61).

Conclusion

Automated aortoiliac plaque detection enables accurate whole-torso atherosclerotic calcified burden assessment, offering a potential pathway for improved CVD diagnosis and treatment.

理由和目的：心血管疾病（CVD）与主动脉-髂斑块负荷密切相关，但目前对其自动检测和分割的研究主要集中在利用CT血管造影分析斑块负荷。在这项研究中，我们提出了一种主动脉髂斑块检测和分割的自动化方法，可以在非对比和增强CT扫描中准确量化钙化斑块负担。材料和方法：训练数据包括119张全身PET-CT非对比扫描和23张增强腹部骨盆CT尿路扫描，均来自我院。测试数据包括来自肌肉减少症数据集的99个增强胸腹骨盆CT扫描；来自前列腺癌数据集的93个；来自肾脏供者队列的1214对非对比和增强腹部CT扫描；9199例来自第二机构的非对比腹部CT结肠镜扫描；以及来自第三家机构的1446张非对比胸部CT扫描。利用nnU-Net对主动脉-髂斑块检测和分割模型进行训练。评估胸部CT非对比扫描的检测准确性。在CT扫描中，通过手工标记来自肌肉减少症、前列腺、肾脏供者和CT结肠镜数据集的斑块区域来评估分割的准确性。在肾供者队列中评估配对非对比扫描和增强扫描的Agatston评分之间的相关性。利用CT结肠镜数据集的多变量分析，分析了全身钙化斑块负担（Agatston评分）、人口统计学和疾病之间的相关性。结果：主动脉髂斑块的检测准确率为88.1%，召回率为99.5%，F1评分为93.4%。在两个内部对比度增强和两个外部非对比度CT数据集上，分割的Dice得分为64.3-83.7%，优于基线方法10%以上（p 2 = 0.99）。多因素分析显示，钙化斑块负荷评估与性别、年龄、BMI和吸烟相关（均为p）。结论：自动检测主动脉-髂斑块能够准确评估整个躯干动脉粥样硬化钙化负荷，为改善CVD的诊断和治疗提供了潜在途径。

{"title":"Automated Detection and Segmentation of Aortoiliac Calcified Plaques Using nnU-Net for Whole-Torso Atherosclerotic Burden Assessment on Non-Contrast and Contrast-Enhanced CT Scans","authors":"Jianfei Liu PhD , Vivek Batheja MD , Pritam Mukherjee PhD , Tejas Sudharshan Mathai PhD , Peter C. Grayson MD , Perry J. Pickhardt MD , Ronald M. Summers MD,PhD","doi":"10.1016/j.acra.2025.10.010","DOIUrl":"10.1016/j.acra.2025.10.010","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Cardiovascular disease (CVD) is closely associated with aortoiliac plaque burden, yet current research on its automated detection and segmentation has largely focused on plaque burden analysis using CT angiography. In this study, we present an automated method for aortoiliac plaque detection and segmentation that enables accurate quantification of calcified plaque burden on both non-contrast and contrast-enhanced CT scans.</div></div><div><h3>Materials and Methods</h3><div>The training data included 119 non-contrast whole-body PET-CT scans and 23 contrast-enhanced abdominopelvic CT urography scans, all obtained from our institution. The testing data comprised 99 contrast-enhanced thoracoabdominopelvic CT scans from the sarcopenia dataset; 93 from the prostate cancer dataset; 1214 paired non-contrast and contrast-enhanced abdominal CT scans from a renal donor cohort; 9199 non-contrast abdominal CT colonography scans from a second institution; and 1446 non-contrast chest CT scans from a third institution. The nnU-Net was used to train a model for aortoiliac plaque detection and segmentation. Detection accuracy was evaluated on non-contrast chest CT scans. Segmentation accuracy was assessed on CT scans with manually labeled plaque regions from the sarcopenia, prostate, renal donor, and CT colonography datasets. The correlation between Agatston scores on paired non-contrast and contrast-enhanced scans was evaluated in the renal donor cohort. Correlations between whole-torso calcified plaque burden (Agatston scores), demographics, and diseases were analyzed using multivariable analysis on the CT colonography dataset.</div></div><div><h3>Results</h3><div>Aortoiliac plaques were detected with 88.1% precision, 99.5% recall, and a 93.4% F1 score. Segmentation achieved Dice scores of 64.3–83.7% across two internal contrast-enhanced and two external non-contrast CT datasets, outperforming baseline methods by over 10% (<em>p</em> < 0.001). Agatston scores from paired CT scans showed strong correlation (<em>R</em><sup>2</sup> = 0.99). Multivariate analysis showed calcified plaque burden assessment correlated with sex, age, BMI, and smoking (all <em>p</em> < 0.001), as well as alcohol abuse (<em>p</em> = 0.01). The calcified burden assessment was also correlated with CVD, heart failure, myocardial infarction (all <em>p</em> < 0.001), and type 2 diabetes (<em>p</em> = 0.03), but showed no correlation with cancer (<em>p</em> = 0.14) or femoral neck fracture (<em>p</em> = 0.61).</div></div><div><h3>Conclusion</h3><div>Automated aortoiliac plaque detection enables accurate whole-torso atherosclerotic calcified burden assessment, offering a potential pathway for improved CVD diagnosis and treatment.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 86-97"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427155","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}

引用次数: 0

New Advances in Imaging-Based Preoperative Prediction of STAS in Lung Adenocarcinoma: From CT and PET/CT to Radiomics and Deep Learning 基于影像的肺腺癌STAS术前预测的新进展：从CT和PET/CT到放射组学和深度学习。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.10.009

Yuhao Fan , Rong Niu , Jianxiong Gao , Yan Sun , Jinbao Feng , Yaoting Zhu , Mengyue Hu , Yunmei Shi , Yuetao Wang , Xiaonan Shao , Qianyun Wang

Lung cancer remains highly prevalent worldwide, with persistently high mortality rates, and postoperative recurrence poses a serious threat to long-term survival. Spread through air spaces (STAS) of tumor cells has been identified as a critical factor influencing recurrence and prognosis in lung cancer. Since its formal definition and classification in 2015, numerous studies have confirmed the significant prognostic impact of STAS. Lung adenocarcinoma, the most common subtype of lung cancer, is particularly challenging due to its high histological heterogeneity and generally poor prognosis, making accurate preoperative assessment of STAS especially crucial. However, the gold standard for diagnosing STAS still relies on postoperative pathology, limiting its clinical utility due to diagnostic delay. Methods combining Computed Tomography or Positron Emission Tomography/Computed Tomography with machine learning have demonstrated outstanding potential in the preoperative prediction of STAS. This systematic review focuses on the current applications and recent advances of imaging plus Artificial Intelligence (AI) in predicting STAS in lung adenocarcinoma, and discusses their role in evaluating personalized treatment models and clinical value. In the future, as AI, multimodal imaging, and big data technologies continue to evolve, noninvasive imaging-based prediction of STAS is expected to become more accurate and widely applicable, promoting personalized and standardized management of lung adenocarcinoma and improving patient prognosis and quality of life.

肺癌在世界范围内仍然非常普遍，死亡率一直很高，术后复发对长期生存构成严重威胁。肿瘤细胞通过空气间隙扩散（STAS）已被确定为影响肺癌复发和预后的关键因素。自2015年正式定义和分类以来，大量研究证实了STAS的显著预后影响。肺腺癌是最常见的肺癌亚型，由于其高度的组织学异质性和普遍较差的预后，尤其具有挑战性，因此术前准确评估STAS尤为重要。然而，STAS诊断的金标准仍然依赖于术后病理，由于诊断延迟，限制了其临床应用。计算机断层扫描或正电子发射断层扫描/计算机断层扫描与机器学习相结合的方法在STAS的术前预测中显示出突出的潜力。本文系统综述了影像学和人工智能（AI）在肺腺癌STAS预测中的应用现状和最新进展，并讨论了它们在评估个性化治疗模式和临床价值中的作用。未来，随着人工智能、多模态成像和大数据技术的不断发展，基于无创成像的STAS预测有望变得更加准确和广泛应用，促进肺腺癌的个性化、规范化管理，改善患者预后和生活质量。

{"title":"New Advances in Imaging-Based Preoperative Prediction of STAS in Lung Adenocarcinoma: From CT and PET/CT to Radiomics and Deep Learning","authors":"Yuhao Fan , Rong Niu , Jianxiong Gao , Yan Sun , Jinbao Feng , Yaoting Zhu , Mengyue Hu , Yunmei Shi , Yuetao Wang , Xiaonan Shao , Qianyun Wang","doi":"10.1016/j.acra.2025.10.009","DOIUrl":"10.1016/j.acra.2025.10.009","url":null,"abstract":"<div><div>Lung cancer remains highly prevalent worldwide, with persistently high mortality rates, and postoperative recurrence poses a serious threat to long-term survival. Spread through air spaces (STAS) of tumor cells has been identified as a critical factor influencing recurrence and prognosis in lung cancer. Since its formal definition and classification in 2015, numerous studies have confirmed the significant prognostic impact of STAS. Lung adenocarcinoma, the most common subtype of lung cancer, is particularly challenging due to its high histological heterogeneity and generally poor prognosis, making accurate preoperative assessment of STAS especially crucial. However, the gold standard for diagnosing STAS still relies on postoperative pathology, limiting its clinical utility due to diagnostic delay. Methods combining Computed Tomography or Positron Emission Tomography/Computed Tomography with machine learning have demonstrated outstanding potential in the preoperative prediction of STAS. This systematic review focuses on the current applications and recent advances of imaging plus Artificial Intelligence (AI) in predicting STAS in lung adenocarcinoma, and discusses their role in evaluating personalized treatment models and clinical value. In the future, as AI, multimodal imaging, and big data technologies continue to evolve, noninvasive imaging-based prediction of STAS is expected to become more accurate and widely applicable, promoting personalized and standardized management of lung adenocarcinoma and improving patient prognosis and quality of life.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 281-296"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402258","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}

引用次数: 0

Multicenter Study of YOLOv9 for Automated Detection and Classification of Supraspinatus Tendon Tears on MRI YOLOv9在冈上肌腱撕裂MRI自动检测与分类中的多中心研究。

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.10.022

Xiaonan Yang MMSc , Zitong Liu BS , Hongyuan Jiang MD , Chengjian Wang MD , Xiaona Xia MD , Tingting Han BS , Rui Zheng BS , Xirui Li MMSc , Dapeng Hao MD , Jiufa Cui MD , Sheng Miao PhD

Rationale and Objectives

This study develops a deep learning model using the You Only Look Once (YOLO) framework for the automated diagnosis of supraspinatus tendon tears (ST) based on multicenter MRI data.

Materials and Methods

In this retrospective study, 1698 patients from five hospitals were included and allocated to training (n = 1047), validation (n = 299), test (n = 154), and external test (n = 198) sets. A YOLOv9-based automated model was developed using coronal fat-suppressed T2-weighted images for lesion detection, localization, and classification. Model performance was assessed using Intersection over Union and confusion matrices. Comparisons between model outputs and radiologist interpretations were performed with McNemar’s test, and interobserver agreement among radiologists was evaluated using Cohen’s kappa.

Results

The YOLOv9 model successfully identified the supraspinatus tendon layer in all images across the validation, test, and external test sets, achieving 100% accuracy. For ST tear detection, the model achieved accuracies of 69.0% (755/1094) in the validation set, 73.9% (414/560) in the test set, and 75.64% (559/739) in the external test set. For classification of partial- and full-thickness tears on the test set, the model demonstrated a macro F1 score of 77.7% (95% CI: 67.4–90.5), outperforming all radiologists (all P<0.05).

Conclusion

The MRI-based YOLOv9 model excelled in diagnosing supraspinatus tendon tears, surpassing radiologists with varying levels of experience.

基本原理和目的：本研究基于多中心MRI数据，利用You Only Look Once （YOLO）框架开发了一个深度学习模型，用于冈上肌腱撕裂（ST）的自动诊断。材料和方法：本回顾性研究纳入来自5家医院的1698例患者，分为训练组（n=1047）、验证组（n=299）、检验组（n=154）和外部检验组（n=198）。使用冠状动脉脂肪抑制的t2加权图像开发了基于yolov9的自动模型，用于病变检测，定位和分类。模型性能评估使用交集超过联合和混淆矩阵。模型输出和放射科医生解释之间的比较使用McNemar的测试，放射科医生之间的观察者之间的协议使用Cohen的kappa进行评估。结果：YOLOv9模型在验证集、测试集和外部测试集的所有图像中都能成功识别冈上肌腱层，准确率达到100%。对于ST撕裂检测，该模型在验证集中的准确率为69.0%(755/1094)，在测试集中的准确率为73.9%(414/560)，在外部测试集中的准确率为75.64%（559/739）。对于测试集上部分和全层撕裂的分类，该模型的宏观F1得分为77.7% (95% CI: 67.4-90.5)，优于所有放射科医生。结论：基于mri的YOLOv9模型在诊断峡上肌腱撕裂方面表现出色，超过了具有不同经验水平的放射科医生。

{"title":"Multicenter Study of YOLOv9 for Automated Detection and Classification of Supraspinatus Tendon Tears on MRI","authors":"Xiaonan Yang MMSc , Zitong Liu BS , Hongyuan Jiang MD , Chengjian Wang MD , Xiaona Xia MD , Tingting Han BS , Rui Zheng BS , Xirui Li MMSc , Dapeng Hao MD , Jiufa Cui MD , Sheng Miao PhD","doi":"10.1016/j.acra.2025.10.022","DOIUrl":"10.1016/j.acra.2025.10.022","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study develops a deep learning model using the You Only Look Once (YOLO) framework for the automated diagnosis of supraspinatus tendon tears (ST) based on multicenter MRI data.</div></div><div><h3>Materials and Methods</h3><div>In this retrospective study, 1698 patients from five hospitals were included and allocated to training (<em>n  </em>=299), test (<em>n  </em>=198) sets. A YOLOv9-based automated model was developed using coronal fat-suppressed T2-weighted images for lesion detection, localization, and classification. Model performance was assessed using Intersection over Union and confusion matrices. Comparisons between model outputs and radiologist interpretations were performed with McNemar’s test, and interobserver agreement among radiologists was evaluated using Cohen’s kappa.</div></div><div><h3>Results</h3><div>The YOLOv9 model successfully identified the supraspinatus tendon layer in all images across the validation, test, and external test sets, achieving 100% accuracy. For ST tear detection, the model achieved accuracies of 69.0% (755/1094) in the validation set, 73.9% (414/560) in the test set, and 75.64% (559/739) in the external test set. For classification of partial- and full-thickness tears on the test set, the model demonstrated a macro F1 score of 77.7% (95% CI: 67.4–90.5), outperforming all radiologists (all P<0.05).</div></div><div><h3>Conclusion</h3><div>The MRI-based YOLOv9 model excelled in diagnosing supraspinatus tendon tears, surpassing radiologists with varying levels of experience.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 157-167"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145423364","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}

引用次数: 0

Should Patients ≥90 Years with Acute Ischemic Stroke Still Undergo Endovascular Thrombectomy? ≥90岁急性缺血性卒中患者是否仍应行血管内取栓术？

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

Academic Radiology

Pub Date : 2026-01-01 DOI: 10.1016/j.acra.2025.10.011

Xiaobo Guan MD , Jianhua Ma MD, PhD , Chenguang Hao MD, PhD , Guosen Bu MD, PhD

Background

To compare outcomes of endovascular thrombectomy (EVT) and best medical management (BMM) in patients aged ≥90 years with acute large vessel occlusion (LVO) stroke, a population underrepresented in prior trials.

Methods

Consecutive patients treated with either EVT or BMM were retrospectively included from a single-center. The primary outcome was the distribution of the modified Rankin Scale (mRS) score at 90 days after stroke onset. Baseline differences were adjusted using inverse probability weighting (IPW) based on propensity scores, followed by ordinal logistic regression on the weighted cohort to evaluate the association between treatment strategy and functional outcome.

Results

A total of 149 patients were included, of whom 52 (34.9%) received EVT and 97 (65.1%) received BMM. After IPW, the weighted sample sizes were 102 for EVT and 138 for BMM, with baseline characteristics well balanced between the two groups. Ordinal logistic regression analysis showed that EVT was significantly associated with better functional outcomes at 90 days compared to BMM (adjusted odds ratio [aOR] 1.74, 95% confidence interval [CI]: 1.09–4.52). EVT was also associated with a higher rate of functional independence (mRS 0–2) (13.7% vs 4.3%; aOR 3.50, 95% CI: 1.30–9.45), lower mortality (40.2% vs 57.2%; aOR 0.50, 95% CI: 0.30–0.84), but increased risk of symptomatic intracranial hemorrhage (7.8% vs 2.2%; aOR 3.23, 95% CI: 1.26–8.24).

Conclusion

EVT was associated with improved functional outcomes and reduced mortality compared to BMM in patients aged ≥90 years with acute LVO stroke; however, the overall clinical outcome in this highly aged population remained poor.

背景：为了比较≥90岁急性大血管闭塞（LVO）卒中患者血管内血栓切除术（EVT）和最佳医疗管理（BMM）的结果，该人群在先前的试验中代表性不足。方法从单中心回顾性纳入连续接受EVT或BMM治疗的患者。主要终点是卒中发作后90天的改良兰金量表（mRS）评分分布。使用基于倾向得分的逆概率加权（IPW）调整基线差异，然后对加权队列进行有序逻辑回归，以评估治疗策略与功能结局之间的关系。结果共纳入149例患者，其中EVT 52例（34.9%），BMM 97例（65.1%）。IPW后，EVT的加权样本量为102，BMM的加权样本量为138，两组的基线特征很好地平衡。有序逻辑回归分析显示，与BMM相比，EVT与90天时更好的功能结局显著相关（调整优势比[aOR] 1.74, 95%可信区间[CI]: 1.09-4.52）。EVT还与较高的功能独立性（mRS 0-2）相关（13.7% vs 4.3%; aOR 3.50, 95% CI: 1.30-9.45），较低的死亡率（40.2% vs 57.2%; aOR 0.50, 95% CI: 0.30-0.84），但增加了症状性颅内出血的风险（7.8% vs 2.2%; aOR 3.23, 95% CI: 1.26-8.24）。结论：与BMM相比，年龄≥90岁的急性左心室卒中患者evt与功能预后改善和死亡率降低相关；然而，这一高度老龄化人群的总体临床结果仍然很差。

{"title":"Should Patients ≥90 Years with Acute Ischemic Stroke Still Undergo Endovascular Thrombectomy?","authors":"Xiaobo Guan MD , Jianhua Ma MD, PhD , Chenguang Hao MD, PhD , Guosen Bu MD, PhD","doi":"10.1016/j.acra.2025.10.011","DOIUrl":"10.1016/j.acra.2025.10.011","url":null,"abstract":"<div><h3>Background</h3><div>To compare outcomes of endovascular thrombectomy (EVT) and best medical management (BMM) in patients aged ≥90 years with acute large vessel occlusion (LVO) stroke, a population underrepresented in prior trials.</div></div><div><h3>Methods</h3><div>Consecutive patients treated with either EVT or BMM were retrospectively included from a single-center. The primary outcome was the distribution of the modified Rankin Scale (mRS) score at 90 days after stroke onset. Baseline differences were adjusted using inverse probability weighting (IPW) based on propensity scores, followed by ordinal logistic regression on the weighted cohort to evaluate the association between treatment strategy and functional outcome.</div></div><div><h3>Results</h3><div>A total of 149 patients were included, of whom 52 (34.9%) received EVT and 97 (65.1%) received BMM. After IPW, the weighted sample sizes were 102 for EVT and 138 for BMM, with baseline characteristics well balanced between the two groups. Ordinal logistic regression analysis showed that EVT was significantly associated with better functional outcomes at 90 days compared to BMM (adjusted odds ratio [aOR] 1.74, 95% confidence interval [CI]: 1.09–4.52). EVT was also associated with a higher rate of functional independence (mRS 0–2) (13.7% vs 4.3%; aOR 3.50, 95% CI: 1.30–9.45), lower mortality (40.2% vs 57.2%; aOR 0.50, 95% CI: 0.30–0.84), but increased risk of symptomatic intracranial hemorrhage (7.8% vs 2.2%; aOR 3.23, 95% CI: 1.26–8.24).</div></div><div><h3>Conclusion</h3><div>EVT was associated with improved functional outcomes and reduced mortality compared to BMM in patients aged ≥90 years with acute LVO stroke; however, the overall clinical outcome in this highly aged population remained poor.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 180-188"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886541","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}

引用次数: 0

Artificial Intelligence-Accelerated vs. Conventional Diffusion-Weighted Imaging for Prostate MRI: Comparing Quality and Quantitative Metrics. 人工智能加速与传统前列腺MRI弥散加权成像：比较质量和定量指标。

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

Academic Radiology

Pub Date : 2025-12-31 DOI: 10.1016/j.acra.2025.12.003

Vlad Sacalean, Oliver Gebler, Wei Liu, Ralph Strecker, Elisabeth Weiland, Fabian Bamberg, Jakob Weiß, Maximilian F Russe, Hannes Engel

Rationale and objectives: Diffusion-weighted imaging (DWI) is central to prostate magnetic resonance imaging (MRI) but lengthens examinations. We evaluated whether an artificial intelligence (AI)-accelerated, reduced-field-of-view diffusion sequence (AI-DWI) could shorten scan time without sacrificing perceived diagnostic image quality, and how it affects quantitative diffusion metrics.

Materials and methods: This prospective, single-center study of diagnostic accuracy enrolled consecutive men with elevated prostate-specific-antigen levels between March and May 2025. The index AI-DWI sequence was compared against the standard conventional DWI sequence (c-DWI) for each patient. Three radiologists scored subjective image quality. Quantitative analysis involved comparing mean apparent diffusion coefficient (ADC) and seven additional texture features. Wilcoxon signed-rank tests assessed ordinal scores, and paired t-tests were used for quantitative metrics.

Results: 62 men (mean age, 68.7 years ± 9) were evaluated. The AI-DWI sequence demonstrated a significantly shorter acquisition time compared to c-DWI (3 min 59 s vs. 4 min 21 s; p<0.01). There was no significant difference in subjective scores for overall image quality, lesion conspicuity, artifacts, or anatomic differentiability (p>0.05 for all). AI-DWI yielded significantly lower mean ADC values (975.92±174.57 vs. 1013.21±189.34; adj. p<0.01) and maximum ADC values (adj. p<0.01). No significant differences were found for standard deviation, coefficient of variation, entropy, kurtosis, minimum, or skewness (adj. p>0.05).

Conclusion: The AI-DWI sequence allows for reduced acquisition time while preserving subjective image quality compared to the c-DWI. Quantitatively, it yields lower mean and maximum ADC values, while showing no significant differences in the rest of the quantitative metrics relative to the conventional sequence.

原理和目的：扩散加权成像（DWI）是前列腺磁共振成像（MRI）的核心，但延长了检查时间。我们评估了人工智能（AI）加速、缩小视场扩散序列（AI- dwi）是否可以在不牺牲感知诊断图像质量的情况下缩短扫描时间，以及它如何影响定量扩散指标。材料和方法：这项诊断准确性的前瞻性单中心研究纳入了2025年3月至5月期间前列腺特异性抗原水平升高的连续男性。将每个患者的指数AI-DWI序列与标准常规DWI序列（c-DWI）进行比较。三名放射科医生对主观图像质量进行评分。定量分析包括比较平均表观扩散系数（ADC）和7个附加纹理特征。Wilcoxon符号秩检验评估序数得分，配对t检验用于定量指标。结果：共纳入62例男性，平均年龄68.7岁±9岁。AI-DWI序列的采集时间明显短于c-DWI （3 min 59 s vs. 4 min 21 s；均p0.05）。AI-DWI的平均ADC值明显低于前者（975.92±174.57 vs. 1013.21±189.34,p < 0.05）。结论：与c-DWI相比，AI-DWI序列可以减少采集时间，同时保持主观图像质量。在定量上，它产生较低的平均值和最大ADC值，而相对于传统序列，在其余定量指标中没有显着差异。

{"title":"Artificial Intelligence-Accelerated vs. Conventional Diffusion-Weighted Imaging for Prostate MRI: Comparing Quality and Quantitative Metrics.","authors":"Vlad Sacalean, Oliver Gebler, Wei Liu, Ralph Strecker, Elisabeth Weiland, Fabian Bamberg, Jakob Weiß, Maximilian F Russe, Hannes Engel","doi":"10.1016/j.acra.2025.12.003","DOIUrl":"https://doi.org/10.1016/j.acra.2025.12.003","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Diffusion-weighted imaging (DWI) is central to prostate magnetic resonance imaging (MRI) but lengthens examinations. We evaluated whether an artificial intelligence (AI)-accelerated, reduced-field-of-view diffusion sequence (AI-DWI) could shorten scan time without sacrificing perceived diagnostic image quality, and how it affects quantitative diffusion metrics.</p><p><strong>Materials and methods: </strong>This prospective, single-center study of diagnostic accuracy enrolled consecutive men with elevated prostate-specific-antigen levels between March and May 2025. The index AI-DWI sequence was compared against the standard conventional DWI sequence (c-DWI) for each patient. Three radiologists scored subjective image quality. Quantitative analysis involved comparing mean apparent diffusion coefficient (ADC) and seven additional texture features. Wilcoxon signed-rank tests assessed ordinal scores, and paired t-tests were used for quantitative metrics.</p><p><strong>Results: </strong>62 men (mean age, 68.7 years ± 9) were evaluated. The AI-DWI sequence demonstrated a significantly shorter acquisition time compared to c-DWI (3 min 59 s vs. 4 min 21 s; p<0.01). There was no significant difference in subjective scores for overall image quality, lesion conspicuity, artifacts, or anatomic differentiability (p>0.05 for all). AI-DWI yielded significantly lower mean ADC values (975.92±174.57 vs. 1013.21±189.34; adj. p<0.01) and maximum ADC values (adj. p<0.01). No significant differences were found for standard deviation, coefficient of variation, entropy, kurtosis, minimum, or skewness (adj. p>0.05).</p><p><strong>Conclusion: </strong>The AI-DWI sequence allows for reduced acquisition time while preserving subjective image quality compared to the c-DWI. Quantitatively, it yields lower mean and maximum ADC values, while showing no significant differences in the rest of the quantitative metrics relative to the conventional sequence.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145890442","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}

引用次数: 0

Thrombectomy Versus Medical Management in Mild Large Vessel Occlusion Stroke: A Multicenter Cohort with One-Year Outcomes. 轻度大血管闭塞性卒中的血栓切除与药物治疗：一项一年预后的多中心队列研究

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

Academic Radiology

Pub Date : 2025-12-31 DOI: 10.1016/j.acra.2025.12.024

Fuchu Jiang, Yajie Zhang, Zhiwen Geng, Jing Tian, Yadong Yu, Qijin Zhai

Background: Evidence regarding the long-term outcomes of patients with mild large vessel occlusion (LVO) stroke remains limited. This study aimed to compare 12-month outcomes between acute ischemic stroke (AIS) patients treated with endovascular therapy (EVT) versus best medical management (BMM).

Methods: A multicenter, retrospective study across three centers was conducted, including AIS patients with LVO and National Institutes of Health Stroke Scale score (NIHSS) <6 between January 2019 and December 2023. Patients were categorized into EVT or BMM groups according to initial treatment strategy. The primary outcome was functional independence (modified Rankin Scale score of 0-2) at 12 months. Inverse probability of treatment weighting (IPTW) based on propensity scores was used to adjust for potential confounders.

Results: A total of 976 patients with LVO were screened, and 285 with NIHSS <6 were enrolled. After IPTW adjustment (195 EVT vs. 201 BMM), functional independence was achieved in 78.5% of EVT and 74.1% of BMM patients (adjusted odds ratio [aOR] 1.25, 95% confidence interval [CI] 0.70-2.20) at 12 months. Hemorrhagic transformation was more frequent in the EVT group (13.3% vs. 5.5%; aOR 2.63, 95% CI 1.20-5.85), whereas symptomatic intracranial hemorrhage rates were similar between groups. Notably, stroke recurrence within 12 months was significantly lower in the EVT group compared to the BMM group (4.6% vs. 12.9%; aOR 0.33, 95% CI 0.15-0.70).

Conclusion: In patients with mild LVO, no statistically significant difference in long-term functional outcomes was observed between EVT and BMM, although EVT was associated with a lower risk of stroke recurrence.

背景：关于轻度大血管闭塞（LVO）卒中患者长期预后的证据仍然有限。本研究旨在比较急性缺血性卒中（AIS）患者接受血管内治疗（EVT）和最佳医疗管理（BMM）的12个月预后。方法：在3个中心进行多中心回顾性研究，包括AIS合并LVO患者和美国国立卫生研究院卒中量表评分（NIHSS）。结果：共筛查976例LVO患者，285例NIHSS患者。结论：轻度LVO患者，EVT与BMM之间的长期功能结局无统计学差异，尽管EVT与卒中复发风险较低相关。

{"title":"Thrombectomy Versus Medical Management in Mild Large Vessel Occlusion Stroke: A Multicenter Cohort with One-Year Outcomes.","authors":"Fuchu Jiang, Yajie Zhang, Zhiwen Geng, Jing Tian, Yadong Yu, Qijin Zhai","doi":"10.1016/j.acra.2025.12.024","DOIUrl":"https://doi.org/10.1016/j.acra.2025.12.024","url":null,"abstract":"<p><strong>Background: </strong>Evidence regarding the long-term outcomes of patients with mild large vessel occlusion (LVO) stroke remains limited. This study aimed to compare 12-month outcomes between acute ischemic stroke (AIS) patients treated with endovascular therapy (EVT) versus best medical management (BMM).</p><p><strong>Methods: </strong>A multicenter, retrospective study across three centers was conducted, including AIS patients with LVO and National Institutes of Health Stroke Scale score (NIHSS) <6 between January 2019 and December 2023. Patients were categorized into EVT or BMM groups according to initial treatment strategy. The primary outcome was functional independence (modified Rankin Scale score of 0-2) at 12 months. Inverse probability of treatment weighting (IPTW) based on propensity scores was used to adjust for potential confounders.</p><p><strong>Results: </strong>A total of 976 patients with LVO were screened, and 285 with NIHSS <6 were enrolled. After IPTW adjustment (195 EVT vs. 201 BMM), functional independence was achieved in 78.5% of EVT and 74.1% of BMM patients (adjusted odds ratio [aOR] 1.25, 95% confidence interval [CI] 0.70-2.20) at 12 months. Hemorrhagic transformation was more frequent in the EVT group (13.3% vs. 5.5%; aOR 2.63, 95% CI 1.20-5.85), whereas symptomatic intracranial hemorrhage rates were similar between groups. Notably, stroke recurrence within 12 months was significantly lower in the EVT group compared to the BMM group (4.6% vs. 12.9%; aOR 0.33, 95% CI 0.15-0.70).</p><p><strong>Conclusion: </strong>In patients with mild LVO, no statistically significant difference in long-term functional outcomes was observed between EVT and BMM, although EVT was associated with a lower risk of stroke recurrence.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145890499","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}

引用次数: 0