Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules

Abstract

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.

Keywords: computer-aided image analysis; early detection; lung cancer; neural networks; risk stratification.

Figures

Figure 1.

Schematics showing the (A) Lung Cancer Prediction Convolutional Neural Network (LCP-CNN) architecture, (B) the training procedure, and (C) application of the trained model to novel data. The input to the network is a three-dimensional anisotropically resampled box ∼56 mm in width.

Figure 2.

Receiver operating characteristic curves and area under the curve (AUC) analysis of the (A) internal National Lung Screening Trial (NLST) dataset using eight-way cross-validation, (B) external Vanderbilt dataset, and (C) external Oxford dataset. The Brock model was used as a comparator for the screening population, and the Mayo model was used for the incidental nodule populations for the two independent validation datasets. LCP-CNN = Lung Cancer Prediction Convolutional Neural Network.

Figure 3.

Reclassification diagrams. (A) National Lung Screening Trial (NLST) dataset for 200 cases and 200 benign nodules (randomly selected; numbers were limited for readability of the figure). (B) Vanderbilt University Medical Center dataset. (C) Oxford University Hospitals dataset. Reclassification diagrams are a useful way to visualize the impact of a new biomarker compared with a reference at predefined thresholds. Here we use rule-out and rule-in thresholds at 5% and 65%, respectively, as shown by the black lines. Red triangles indicate cancers, and blue circles indicate controls. If a new biomarker improves classification of cancers compared with the reference, then one would expect, for example, cases (red triangles) that were below 65% on the horizontal axis to move above 65% to the vertical axis, that is, from the central rectangular region to the region immediately above it. For example, on the Vanderbilt and Oxford datasets, 45% and 32% of the cancers, respectively, are reclassified up compared with the Mayo model. Similarly, a new biomarker improves benign classification compared with the reference if it moves controls (blue circles) that were above the 5% threshold on the horizontal axis to below 5% on the vertical axis. For nodules that stay within the three square regions intersected by the green diagonal, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN) does not add value because none of the nodules are correctly reclassified compared with the Brock or Mayo model. On the Vanderbilt and Oxford datasets, 33% and 61% of the benign nodules, respectively, are reclassified down compared with the Mayo model.