Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning.
Authors
Affiliations (7)
Affiliations (7)
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15-W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan.
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan. Electronic address: [email protected].
- Department of Radiology, Sapporo-Kosei General Hospital, N3-E8-5, Chuo-Ku, Sapporo, Hokkaido 060-0033, Japan.
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan.
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan.
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15-W7, Kita ku, Sapporo 060-8638, Japan.
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15-W7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan; Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan; Department of Nuclear Medicine, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan; Medical AI Human Research and Development Center, Hokkaido University Hospital, N14-W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan.
Abstract
To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance. We retrospectively analyzed 91 cases of laryngeal or hypopharyngeal cancer treated surgically at our hospital during the period April 2010 through May 2023, and we divided the cases into datasets for training (n = 61) and testing (n = 30). We reviewed the CT images and pathological diagnoses in all cases to determine the invasion positive- or negative-status as a ground truth. We trained the new CNN model to classify thyroid cartilage invasion-positive or -negative status from the pre-treatment axial CT images by transfer learning from Residual Network 101 (ResNet101), using the training dataset. We then used the test dataset to evaluate the model's performance. Two radiologists, one with extensive head and neck imaging experience (senior reader) and the other with less experience (junior reader) reviewed the CT images of the test dataset to determine whether thyroid cartilage invasion was present. The following were obtained by the CNN model with the test dataset: area under the curve (AUC), 0.82; 90 % accuracy, 80 % sensitivity, and 95 % specificity. The CNN model showed a significant difference in AUCs compared to the junior reader (p = 0.035) but not the senior reader (p = 0.61). The CNN-based diagnostic model can be a useful supportive tool for the assessment of thyroid cartilage invasion in patients with laryngeal or hypopharyngeal cancer.