ArticleBMC cancer2024
Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Sarcopenia is associated with poor swallowing outcomes after resection and free flap reconstruction for oral cancer.Oral oncology reports · 2026Article
- Deep learning-based automated masseter muscle area on routine CT stratifies survival in oral cancer.Oral radiology · 2026Article
- Auxiliary meta-learning strategy for cancer recognition: leveraging external data and optimized feature mapping.BMC cancer · 2025Article
- AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies.EXCLI journal · 2024Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSarcopenia 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.
methodsTo 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.
resultsOur 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.