Yoshiko Ariji1, Motoki Fukuda2, Yoshitaka Kise3, Michihito Nozawa2, Yudai Yanashita4, Hiroshi Fujita5, Akitoshi Katsumata6, Eiichiro Ariji7. 1. Associate Proffessor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan. Electronic address: Yoshiko@dpc.agu.ac.jp. 2. Instructor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan. 3. Assistant Professor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan. 4. Postgraduate student, Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan. 5. Professor, Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan. 6. Professor, Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan. 7. Associate Proffessor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
Abstract
OBJECTIVE: Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis. STUDY DESIGN: The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma. The performance of a deep learning image classification system for the diagnosis of lymph node metastasis on CT images was compared with the diagnostic interpretations of 2 experienced radiologists by using the Mann-Whitney U test and χ2 analysis. RESULTS: The performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists. CONCLUSIONS: The deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.
OBJECTIVE: Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis. STUDY DESIGN: The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma. The performance of a deep learning image classification system for the diagnosis of lymph node metastasis on CT images was compared with the diagnostic interpretations of 2 experienced radiologists by using the Mann-Whitney U test and χ2 analysis. RESULTS: The performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists. CONCLUSIONS: The deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.
Authors: Myrthel Vranckx; Adriaan Van Gerven; Holger Willems; Arne Vandemeulebroucke; André Ferreira Leite; Constantinus Politis; Reinhilde Jacobs Journal: Int J Environ Res Public Health Date: 2020-05-25 Impact factor: 3.390