TY - JOUR
T1 - Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography
AU - Orooji, Mahdi
AU - Alilou, Mehdi
AU - Rakshit, Sagar
AU - Beig, Niha
AU - Khorrami, Mohammad Hadi
AU - Rajiah, Prabhakar
AU - Thawani, Rajat
AU - Ginsberg, Jennifer
AU - Donatelli, Christopher
AU - Yang, Michael
AU - Jacono, Frank
AU - Gilkeson, Robert
AU - Velcheti, Vamsidhar
AU - Linden, Philip
AU - Madabhushi, Anant
N1 - Publisher Copyright:
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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%.
AB - 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%.
KW - artificial intelligence
KW - computed tomography scan
KW - computer-Assisted diagnosis
KW - lung cancer
KW - phenotype.
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U2 - 10.1117/1.JMI.5.2.024501
DO - 10.1117/1.JMI.5.2.024501
M3 - Article
C2 - 29721515
AN - SCOPUS:85045845665
SN - 2329-4302
VL - 5
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 2
M1 - 024501
ER -