TY - JOUR
T1 - Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules
T2 - A matched case-control study
AU - Huang, Peng
AU - Park, Seyoun
AU - Yan, Rongkai
AU - Lee, Junghoon
AU - Chu, Linda C.
AU - Lin, Cheng T.
AU - Hussien, Amira
AU - Rathmell, Joshua
AU - Thomas, Brett
AU - Chen, Chen
AU - HalS, Russell
AU - Ettinger, David S.
AU - Brock, Malcolm
AU - Hu, Ping
AU - Fishman, Elliot K.
AU - Gabrielson, Edward
AU - Lam, Stephen
N1 - Funding Information:
P. Huang, R.Y., J.L., and C.T.L. supported by Johns Hopkins-Allegheny Health Network Cancer Research. P. Huang and D.S.E. supported by National Cancer Institute (P30CA006973). P. Huang, S.P., J.L., M.B., and E.K.F. supported by Johns Hopkins University (2015 Discovery Award).
PY - 2018/1
Y1 - 2018/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85038910307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038910307&partnerID=8YFLogxK
U2 - 10.1148/radiol.2017162725
DO - 10.1148/radiol.2017162725
M3 - Article
C2 - 28872442
AN - SCOPUS:85038910307
SN - 0033-8419
VL - 286
SP - 286
EP - 295
JO - RADIOLOGY
JF - RADIOLOGY
IS - 1
ER -