Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas

医学 成像生物标志物 放射科 生物标志物 接收机工作特性 正电子发射断层摄影术 断层摄影术 卷积神经网络 核医学 磁共振成像 人工智能 计算机科学 内科学 生物化学 化学
作者
Mohammadhadi Khorrami,Mahdi Orooji,Niha Beig,Prateek Prasanna,Prabhakar Rajiah,Christopher Donatelli,Vamsidhar Velcheti,Sagar Rakshit,Michael Yang,Frank J. Jacono,Robert Gilkeson,Philip A. Linden,Anant Madabhushi
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:8 (1) 被引量:29
标识
DOI:10.1038/s41598-018-33473-0
摘要

Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.
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