Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography
| Autor: | Ayako Suzuki, Hayato Kaida, Mitsuru Matsuki, Teruyoshi Oda, Sung-Woon Im, Tomoya Kadoba, Takenori Kozuka, Kazunari Ishii, Yuko Matsukubo, Yukinobu Yagyu, Tomoko Hyodo, Masakatsu Tsurusaki |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Lung Neoplasms Chest ct CAD Computed tomography Sensitivity and Specificity 030218 nuclear medicine & medical imaging Young Adult 03 medical and health sciences Deep Learning 0302 clinical medicine Humans Medicine Radiology Nuclear Medicine and imaging Diagnosis Computer-Assisted Lung Aged Retrospective Studies Aged 80 and over Multiple Pulmonary Nodules medicine.diagnostic_test business.industry Reproducibility of Results Solitary Pulmonary Nodule Gold standard (test) Middle Aged Cad system Predictive value Computer-aided diagnosis 030220 oncology & carcinogenesis Radiographic Image Interpretation Computer-Assisted Female Tomography X-Ray Computed business Nuclear medicine |
| Zdroj: | Japanese Journal of Radiology. 38:1052-1061 |
| ISSN: | 1867-108X 1867-1071 |
| Popis: | To 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. A 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. The 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. CAD 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. |
| Databáze: | OpenAIRE |
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