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Open Access 01-12-2024 | Oral Cancer | Research

Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer

Authors: Katsuya Sakamoto, Shin-ichiro Hiraoka, Kohei Kawamura, Peiying Ruan, Shuji Uchida, Ryo Akiyama, Chonho Lee, Kazuki Ide, Susumu Tanaka

Published in: BMC Cancer | Issue 1/2024

Abstract

Background

Sarcopenia has been identified as a potential negative prognostic factor in cancer patients. In this study, our objective was to investigate the relationship between the assessment method for sarcopenia using the masseter muscle volume measured on computed tomography (CT) images and the life expectancy of patients with oral cancer. We also developed a learning model using deep learning to automatically extract the masseter muscle volume and investigated its association with the life expectancy of oral cancer patients.

Methods

To develop the learning model for masseter muscle volume, we used manually extracted data from CT images of 277 patients. We established the association between manually extracted masseter muscle volume and the life expectancy of oral cancer patients. Additionally, we compared the correlation between the groups of manual and automatic extraction in the masseter muscle volume learning model.

Results

Our findings revealed a significant association between manually extracted masseter muscle volume on CT images and the life expectancy of patients with oral cancer. Notably, the manual and automatic extraction groups in the masseter muscle volume learning model showed a high correlation. Furthermore, the masseter muscle volume automatically extracted using the developed learning model exhibited a strong association with life expectancy.

Conclusions

The sarcopenia assessment method is useful for predicting the life expectancy of patients with oral cancer. In the future, it is crucial to validate and analyze various factors within the oral surgery field, extending beyond cancer patients.

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Title: Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer
Authors: Katsuya Sakamoto
Shin-ichiro Hiraoka
Kohei Kawamura
Peiying Ruan
Shuji Uchida
Ryo Akiyama
Chonho Lee
Kazuki Ide
Susumu Tanaka
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Oral Cancer
Oral Cavity Carcinoma
Sarcopenia
Computed Tomography
Computed Tomography
Published in: BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-024-11873-y

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Abstract

Background

Methods

Results

Conclusions

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