| Autor: |
Massion PP; Cancer Early Detection and Prevention Initiative, Vanderbilt Ingram Cancer Center, Division of Allergy, Pulmonary and Critical Care Medicine.; Pulmonary and Critical Care Section, Medical Service, Veterans Affairs, and., Antic S; Cancer Early Detection and Prevention Initiative, Vanderbilt Ingram Cancer Center, Division of Allergy, Pulmonary and Critical Care Medicine., Ather S; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom., Arteta C; Optellum Ltd., Oxford, United Kingdom., Brabec J; Faculty of Medicine, Masaryk University, Brno, Czech Republic., Chen H; Department of Biostatistics, and., Declerck J; Optellum Ltd., Oxford, United Kingdom., Dufek D; Faculty of Medicine, Masaryk University, Brno, Czech Republic., Hickes W; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom., Kadir T; Optellum Ltd., Oxford, United Kingdom., Kunst J; Faculty of Medicine, Masaryk University, Brno, Czech Republic., Landman BA; Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee; and., Munden RF; Department of Radiology, Wake Forest Baptist Health, Winston Salem, North Carolina., Novotny P; Optellum Ltd., Oxford, United Kingdom., Peschl H; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom., Pickup LC; Optellum Ltd., Oxford, United Kingdom., Santos C; Optellum Ltd., Oxford, United Kingdom., Smith GT; Department of Radiology, Vanderbilt University School of Medicine, Nashville, Tennessee.; Department of Radiology, Tennessee Valley Healthcare System, Nashville, Tennessee., Talwar A; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom., Gleeson F; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom. |
| Abstrakt: |
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. |
