Oral Dysfunction in Patients with Oral Cancer Could Occur Before Treatment and Require Early Nutritional Improvement: A Cross-Sectional Study

Matsuda, Yuichi; Okui, Tatsuo; Tatsumi, Hiroto; Okuma, Satoe; Kato, Akira; Morioka, Reon; Takeda, Mayu; Kanno, Takahiro

doi:10.1007/s00455-022-10531-4

Cited by 4 publications

(1 citation statement)

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“…These observations suggest that the Masseter Muscle Volume (MMV) might serve as a valuable prognostic tool in oral cancer cases. On the other hand, it has been reported that patients with oral cancer often experience a decline in oral function and nutritional status due to the effects of the cancer before treatment [ 36 ]. Clinically, it is anticipated that the more advanced the cancer stage, the more pronounced these effects become.…”

Section: Discussionmentioning

confidence: 99%

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

Sakamoto,

Hiraoka,

Kawamura

et al. 2024

BMC Cancer

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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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Section: Discussionmentioning

confidence: 99%