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

Yoshiharu Ohno, Kota Aoyagi, Atsushi Yaguchi, Shinichiro Seki, Yoshiko Ueno, Yuji Kishida, Daisuke Takenaka, Takeshi Yoshikawa

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)432-443
Number of pages12
JournalRadiology
Volume296
Issue number2
DOIs
Publication statusPublished - 08-2020

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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