European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
Role of machine learning segmentation method based on CT images in preoperative staging of oral cavity cancer.
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Can Sermin, Succo Giovanni, Coskun Cengiz, Korkmaz Mehmet Hakan, Akdag Mehmet
Аннотация
OBJECTIVE: The article aims to demonstrate, using oral cavity SCC as an example, that machine learning can accurately predict the T and N staging of OSCC, using the conventional radiologist/ surgeon interpretation of the scan as the reference standard.
MATERIALS AND METHODS: Two datasets for tumor mass and nodal metastasis were used in this study. Each of the datasets consists of 179 Contrast-enhanced Computed Tomography images. A customized U-Net deep learning architecture was employed for the segmentation of tumor masses and nodal metastases. Comprehensive maps of the tumor mass and metastatic lymph nodes were generated. Following this mapping process, the dimensions of the identified lesions were measured and classified according to the Tumor and Lymph Node Metastasis classification system. The resulting classifications were then compared with those established by a radiologist to assess accuracy.
RESULTS: The performance metrics for tumor mass and metastasis segmentation were as follows: binary accuracy value of 98.81% and 99.58%, respectively. The accuracy values were 75.00% for tumor grade classification and 97.22% for nodal status classification.
CONCLUSION: We emphasize that machine learning-based segmentation methods effectively predict tumor mapping and staging in oral cavity tumors, demonstrating correlation with surgeons/radiologists' assessments. As such, this model can be a diagnostic tool that supports clinicians in making informed therapeutic decisions. We foresee that, with the continuous evolution of technology, the segmentation model employed in our study will undergo significant advancements, ultimately facilitating three-dimensional tumor mapping in the near future.
MATERIALS AND METHODS: Two datasets for tumor mass and nodal metastasis were used in this study. Each of the datasets consists of 179 Contrast-enhanced Computed Tomography images. A customized U-Net deep learning architecture was employed for the segmentation of tumor masses and nodal metastases. Comprehensive maps of the tumor mass and metastatic lymph nodes were generated. Following this mapping process, the dimensions of the identified lesions were measured and classified according to the Tumor and Lymph Node Metastasis classification system. The resulting classifications were then compared with those established by a radiologist to assess accuracy.
RESULTS: The performance metrics for tumor mass and metastasis segmentation were as follows: binary accuracy value of 98.81% and 99.58%, respectively. The accuracy values were 75.00% for tumor grade classification and 97.22% for nodal status classification.
CONCLUSION: We emphasize that machine learning-based segmentation methods effectively predict tumor mapping and staging in oral cavity tumors, demonstrating correlation with surgeons/radiologists' assessments. As such, this model can be a diagnostic tool that supports clinicians in making informed therapeutic decisions. We foresee that, with the continuous evolution of technology, the segmentation model employed in our study will undergo significant advancements, ultimately facilitating three-dimensional tumor mapping in the near future.