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Updated: Aug 2, 2026
A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A
Peng Huang1, Seyoun Park1, Rongkai Yan1
1From the Departments of Oncology (P. Huang, D.S.E.), Radiology (E.K.F., L.C.C., C.T.L., A.H.), Radiation Oncology and Molecular Radiation Sciences (S.P., J.L., R.H.), Surgery (M.B.), Pathology (E.G.), and Biostatistics (J.S.), Johns Hopkins University School of Medicine, 550 N Broadway, Suite 1103, Baltimore, MD 21205; Department of Medicine, the University of British Columbia, Vancouver, BC, Canada (S.L.); Information Management Services, Rockville, Md (J.R., B.T.); Biometry Research Group, National Cancer Institute, Bethesda, Md (P. Hu); Department of Radiology, Nongken General Hospital of Hainan Medical University, Haikou, Hainan, China (R.Y.); and Department of Thoracic Surgery, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China (C.C.).
Abstract:
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.
