Differentiation of intrathoracic lymph node histopathology by volumetric dual energy CT radiomic analysis

IF 1.8 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Clinical Imaging Pub Date : 2024-08-10 DOI:10.1016/j.clinimag.2024.110252
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Abstract

Purpose

To determine the performance of volumetric dual energy low kV and iodine radiomic features for the differentiation of intrathoracic lymph node histopathology, and influence of contrast protocol.

Materials and methods

Intrathoracic lymph nodes with histopathologic correlation (neoplastic, granulomatous sarcoid, benign) within 90 days of DECT chest imaging were volumetrically segmented. 1691 volumetric radiomic features were extracted from iodine maps and low-kV images, totaling 3382 features. Univariate analysis was performed using 2-sample t-test and filtered for false discoveries. Multivariable analysis was used to compute AUCs for lymph node classification tasks.

Results

129 lymph nodes from 72 individuals (mean age 61 ± 15 years) were included, 52 neoplastic, 51 benign, and 26 granulomatous-sarcoid. Among all contrast enhanced DECT protocol exams (routine, PE and CTA), univariable analysis demonstrated no significant differences in iodine and low kV features between neoplastic and non-neoplastic lymph nodes; in the subset of neoplastic versus benign lymph nodes with routine DECT protocol, 199 features differed (p = .01- < 0.05).

Multivariable analysis using both iodine and low kV features yielded AUCs >0.8 for differentiating neoplastic from non-neoplastic lymph nodes (AUC 0.86), including subsets of neoplastic from granulomatous (AUC 0.86) and neoplastic from benign (AUC 0.9) lymph nodes, among all contrast protocols.

Conclusions

Volumetric DECT radiomic features demonstrate strong collective performance in differentiation of neoplastic from non-neoplastic intrathoracic lymph nodes, and are influenced by contrast protocol.

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通过容积双能 CT 放射线学分析区分胸腔内淋巴结组织病理学。
目的:确定容积双能低 kV 和碘放射学特征在胸内淋巴结组织病理学鉴别方面的性能以及造影剂方案的影响:对 DECT 胸部成像 90 天内具有组织病理学相关性(肿瘤、肉芽肿性肉瘤、良性)的胸内淋巴结进行容积分割。从碘图和低 kV 图像中提取了 1691 个容积放射学特征,共计 3382 个特征。使用双样本 t 检验进行单变量分析,并过滤错误发现。多变量分析用于计算淋巴结分类任务的 AUC:共纳入 72 人(平均年龄 61 ± 15 岁)的 129 个淋巴结,其中 52 个为肿瘤性淋巴结,51 个为良性淋巴结,26 个为肉芽肿肉芽肿性淋巴结。在所有造影剂增强 DECT 方案检查(常规、PE 和 CTA)中,单变量分析表明肿瘤性和非肿瘤性淋巴结在碘和低 kV 特征方面无显著差异;在常规 DECT 方案的肿瘤性和良性淋巴结子集中,199 个特征存在差异(区分肿瘤性和良性淋巴结的 p = .01- 0.8),包括新 生淋巴结与肉芽肿淋巴结(AUC 0.86)和新 生淋巴结与良性淋巴结(AUC 0.9):在区分肿瘤性和非肿瘤性胸腔内淋巴结方面,容积 DECT 放射学特征表现出很强的集体性能,并受造影剂方案的影响。
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来源期刊
Clinical Imaging
Clinical Imaging 医学-核医学
CiteScore
4.60
自引率
0.00%
发文量
265
审稿时长
35 days
期刊介绍: The mission of Clinical Imaging is to publish, in a timely manner, the very best radiology research from the United States and around the world with special attention to the impact of medical imaging on patient care. The journal''s publications cover all imaging modalities, radiology issues related to patients, policy and practice improvements, and clinically-oriented imaging physics and informatics. The journal is a valuable resource for practicing radiologists, radiologists-in-training and other clinicians with an interest in imaging. Papers are carefully peer-reviewed and selected by our experienced subject editors who are leading experts spanning the range of imaging sub-specialties, which include: -Body Imaging- Breast Imaging- Cardiothoracic Imaging- Imaging Physics and Informatics- Molecular Imaging and Nuclear Medicine- Musculoskeletal and Emergency Imaging- Neuroradiology- Practice, Policy & Education- Pediatric Imaging- Vascular and Interventional Radiology
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