Journal Article

Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography

Im SungWoon, Matsukubo Yuko, Matsuki Mitsuru, Ishii Kazunari, Kadoba Tomoya, Suzuki, Ayako, Yagyu Yukinobu, Oda Teruyoshi, Hyodo Tomoko, Tsurusaki Masakatsu, Kozuka Takenori, Kaida Hayato

2020

Overview

PurposeTo evaluate the performance of a deep learning-based computer-aided diagnosis (CAD) system at detecting pulmonary nodules on CT by comparing radiologists’ readings with and without CAD.Materials and methodsA total of 120 chest CT images were randomly selected from patients with suspected lung cancer. The gold standard of nodules ≥ 3 mm was established by a panel of three expert radiologists. Two less experienced radiologists read the images without and afterward with CAD system. Their reading times were recorded.ResultsThe radiologists’ sensitivity increased from 20.9% to 38.0% with the introduction of CAD. The positive predictive value (PPV) decreased from 70.5% to 61.8%, and the F1-score increased from 32.2% to 47.0%. The sensitivity significantly increased from 13.7% to 32.4% for small nodules (3–6 mm) and from 33.3% to 47.6% for medium nodules (6–10 mm). CAD alone showed a sensitivity of 70.3%, a PPV of 57.9%, and an F1-score of 63.5%. Reading time decreased by 11.3% with the use of CAD.ConclusionCAD improved the less experienced radiologists’ sensitivity in detecting pulmonary nodules of all sizes, especially including a significant improvement in the detection of clinically important-sized medium nodules (6–10 mm) as well as small nodules (3–6 mm) and reduced their reading time.

• Publisher: Springer Nature B.V
• Subject: CAI / Computed tomography / Computer assisted instruction / Deep learning / Diagnosis / Lung cancer / Lung nodules / Medical diagnosis / Medical imaging / Nodules / Sensitivity