Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study.
Radiology. 2018 Jan;286(1):286-295. doi: 10.1148/radiol.2017162725. Epub 2017 Sep 5. Huang P1, Park S1, Yan R1, Lee J1, Chu LC1, Lin CT1, Hussien A1, Rathmell J1, Thomas B1, Chen C1, Hales R1, Ettinger DS1, Brock M1, Hu P1, Fishman EK1, Gabrielson E1, Lam S1.
Purpose: To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce the false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists.
Materials and Methods: A matched case-control sample of low-dose computed tomography (CT) studies in 186 participants with 4-20-mm noncalcified lung nodules who underwent biopsy in the National Lung Screening Trial (NLST) was selected. Variables used for matching were age, sex, smoking status, chronic obstructive pulmonary disease status, body mass index, study year of the positive screening test, and screening results. Studies before lung biopsy were randomly split into a training set (70 cancers plus 70 benign controls) and a validation set (20 cancers plus 26 benign controls). Image features from within and outside dominant nodules were extracted. A CAD algorithm developed from the training set and a random forest classifier were applied to the validation set to predict biopsy outcomes. Receiver operating characteristic analysis was used to compare the prediction accuracy of CAD with the NLST investigator's diagnosis and readings from three experienced and board-certified thoracic radiologists who used contemporary clinical practice guidelines.
Results: In the validation cohort, the area under the receiver operating characteristic curve for CAD was 0.9154. By default, the sensitivity, specificity, and PPV of the NLST investigators were 1.00, 0.00, and 0.43, respectively. The sensitivity, specificity, PPV, and negative predictive value of CAD and the three radiologists' combined reading were 0.95, 0.88, 0.86, and 0.96 and 0.70, 0.69, 0.64, and 0.75, respectively.
Conclusion: CAD could increase PPV and reduce the false-positive rate in the early diagnosis of lung cancer. © RSNA, 2017 Online supplemental material is available for this article.