Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F20%3A00116130" target="_blank" >RIV/00216224:14110/20:00116130 - isvavai.cz</a>
Result on the web
<a href="https://www.atsjournals.org/doi/10.1164/rccm.201903-0505OC#aff5" target="_blank" >https://www.atsjournals.org/doi/10.1164/rccm.201903-0505OC#aff5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1164/rccm.201903-0505OC" target="_blank" >10.1164/rccm.201903-0505OC</a>
Alternative languages
Result language
angličtina
Original language name
Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30203 - Respiratory systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
American Journal of Respiratory And Critical Care Medicine
ISSN
1073-449X
e-ISSN
1535-4970
Volume of the periodical
202
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
241-249
UT code for WoS article
000551375700018
EID of the result in the Scopus database
2-s2.0-85088177199