Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT

Abstract

Background: Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT.Purpose: To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods:From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results:The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodules (mean size 6 standard deviation, 11 mm 6 5; range, 4-29 mm) diagnosed as 132 malignant nodules and 158 benign nodules. There were 132 solid nodules (46%), 106 partsolid nodules (36%), and 52 ground-glass nodules (18%). The test set results showed that the diagnostic performance of the CNN with CADv for total nodule volume change per day was larger than DT of CADv with CNN (AUC, 0.94 [95% confidence interval {CI}: 0.90, 0.96] vs 0.67 [95% CI: 0.60, 0.74]; P , .001) and CADv without CNN (total nodule volume change per day: AUC, 0.69 [95% CI: 0.62, 0.75]; P , .001; DT: AUC, 0.58 [95% CI: 0.51, 0.65]; P , .001). The accuracy of total nodule volume change per day of CADv with CNN was significantly higher than that of CADv without CNN (P , .001) and DT of both methods (P , .001). Conclusion:Convolutional neural network is useful for improving accuracy of computer-aided detection of volume measurement and nodule differentiation capability at CT for patients with pulmonary nodules.

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography

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The Value of CT Radiomics Nomogram Analysis of Parenchyma Surrounding Pulmonary Nodules in the Differentiation of Inflammatory Nodules from Tuberculous Nodules

“…Recently,Most studies of pulmonary nodules focus on the discrimination of benign and malignant nodules, and pay insu cient attention to the classi cation and diagnosis of benign nodules [12,13] . In order to reduce medical waste under the requirements of precision medical model, it is necessary to further classify and diagnose benign nodules.…”

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

“…Ohno et al approached this issue by calculating the volume change and volume doubling time of pulmonary nodules, assisted by the DL technique applied to computeraided detection of volume (CADv) measurements (67). They reported that the AUC and accuracy of total volume change per day calculated by the CADv with the DL method (AUC, 0.94; accuracy, 90%) were significantly higher than those of the volume doubling times with CADv using the DL method (AUC, 0.67; accuracy, 83%), and CADv not using the DL method (total volume change per day: AUC, 0.69 and accuracy, 67%; volume doubling time: AUC, 0.58, and accuracy, 65%) (67).…”