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@article{1673159,
author = {Massion, P. P. and Antic, S. and Ather, S. and Arteta, C. and Brabec, Jan and Chen, H. D. and Declerck, J. and Dufek, David and Hickes, W. and Kadir, T. and Kunst, Jonáš and Landman, B. A. and Munden, R. F. and Novotny, P. and Peschl, H. and Pickup, L. C. and Santos, C. and Smith, G. T. and Talwar, A. and Gleeson, F.},
article_location = {New York},
article_number = {2},
doi = {http://dx.doi.org/10.1164/rccm.201903-0505OC},
keywords = {early detection; risk stratification; neural networks; lung cancer; computer-aided image analysis},
language = {eng},
issn = {1073-449X},
journal = {American Journal of Respiratory And Critical Care Medicine},
title = {Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules},
url = {https://www.atsjournals.org/doi/10.1164/rccm.201903-0505OC#aff5},
volume = {202},
year = {2020}
}
TY - JOUR
ID - 1673159
AU - Massion, P. P. - Antic, S. - Ather, S. - Arteta, C. - Brabec, Jan - Chen, H. D. - Declerck, J. - Dufek, David - Hickes, W. - Kadir, T. - Kunst, Jonáš - Landman, B. A. - Munden, R. F. - Novotny, P. - Peschl, H. - Pickup, L. C. - Santos, C. - Smith, G. T. - Talwar, A. - Gleeson, F.
PY - 2020
TI - Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules
JF - American Journal of Respiratory And Critical Care Medicine
VL - 202
IS - 2
SP - 241-249
EP - 241-249
PB - American Thoracic Society
SN - 1073449X
KW - early detection
KW - risk stratification
KW - neural networks
KW - lung cancer
KW - computer-aided image analysis
UR - https://www.atsjournals.org/doi/10.1164/rccm.201903-0505OC#aff5
L2 - https://www.atsjournals.org/doi/10.1164/rccm.201903-0505OC#aff5
N2 - Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed. Objectives: To develop and validate a deep learning method to improve the management of IPNs. Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions. Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts. Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.
ER -
MASSION, P. P., S. ANTIC, S. ATHER, C. ARTETA, Jan BRABEC, H. D. CHEN, J. DECLERCK, David DUFEK, W. HICKES, T. KADIR, Jonáš KUNST, B. A. LANDMAN, R. F. MUNDEN, P. NOVOTNY, H. PESCHL, L. C. PICKUP, C. SANTOS, G. T. SMITH, A. TALWAR a F. GLEESON. Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. \textit{American Journal of Respiratory And Critical Care Medicine}. New York: American Thoracic Society, 2020, roč.~202, č.~2, s.~241-249. ISSN~1073-449X. Dostupné z: https://dx.doi.org/10.1164/rccm.201903-0505OC.
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