Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human...

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Published in:Journal of medical imaging (Bellingham, Wash.) Wash.), 2018-04, Vol.5 (2), p.024501-024501
Main Authors: Orooji, Mahdi, Alilou, Mehdi, Rakshit, Sagar, Beig, Niha, Khorrami, Mohammad Hadi, Rajiah, Prabhakar, Thawani, Rajat, Ginsberg, Jennifer, Donatelli, Christopher, Yang, Michael, Jacono, Frank, Gilkeson, Robert, Velcheti, Vamsidhar, Linden, Philip, Madabhushi, Anant
Format: Article
Language:English
Subjects:
Computer-Aided Diagnosis
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Summary:Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training (N  =  139) and the other (N  =  56) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.
ISSN:2329-4302
2329-4310
DOI:10.1117/1.JMI.5.2.024501