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Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study
BACKGROUND: Prompt diagnosis of malnutrition and appropriate interventions can substantially improve the prognosis of patients with cancer; however, it is difficult to unify the tools for screening malnutrition risk. 3D imaging technology has been emerging as an approach to assisting in the diagnosi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042485/ https://www.ncbi.nlm.nih.gov/pubmed/36992909 http://dx.doi.org/10.3389/fnut.2023.1115079 |
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author | Chen, Moxi Wang, Xue Han, Meifen Li, Yunzhu Yu, Nanze Long, Xiao Chen, Wei |
author_facet | Chen, Moxi Wang, Xue Han, Meifen Li, Yunzhu Yu, Nanze Long, Xiao Chen, Wei |
author_sort | Chen, Moxi |
collection | PubMed |
description | BACKGROUND: Prompt diagnosis of malnutrition and appropriate interventions can substantially improve the prognosis of patients with cancer; however, it is difficult to unify the tools for screening malnutrition risk. 3D imaging technology has been emerging as an approach to assisting in the diagnosis of diseases, and we designed this study to explore its application value in identifying the malnutrition phenotype and evaluating nutrition status. METHODS: Hospitalized patients treating with maintenance chemotherapy for advanced malignant tumor of digestive system were recruited from the Department of Oncology, whose NRS 2002 score > 3. Physical examination and body composition data of patients at risk for malnutrition were analyzed by physicians trained to complete a subjective global assessment. The facial depression index was recognized using the Antera 3D® system, temporal and periorbital depression indexes were acquired using the companion software Antera Pro. This software captures quantitative data of depression volume, affected area, and maximum depth of temporal and periorbital concave areas. RESULTS: A total of 53 inpatients with malnutrition-related indicators were included. The volume of temporal depression was significantly negatively correlated with upper arm circumference (r = −0.293, p = 0.033) and calf circumference (r = −0.285, p = 0.038). The volume and affected area of periorbital depression were significantly negatively correlated with fat mass index (r = −0.273, p = 0.048 and r = −0.304, p = 0.026, respectively) and percent body fat (r = −0.317, p = 0.021 and r = −0.364, p = 0.007, respectively). The volume and affected area of temporal depression in patients with muscle loss phenotype (low arm circumference/low calf circumference/low handgrip strength/low fat-free mass index) were significantly higher than those in patients without muscle loss. Moreover, patients with fat mass loss phenotype (low fat mass index) showed a significant increase in the volume and affected area of periorbital depression. CONCLUSION: The facial temporal region, and periorbital depression indicators extracted by 3D image recognition technology were significantly associated with the phenotype of malnutrition-related muscle and fat loss and showed a trend of grade changes in the population of different subjective global assessment nutritional classifications. |
format | Online Article Text |
id | pubmed-10042485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100424852023-03-28 Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study Chen, Moxi Wang, Xue Han, Meifen Li, Yunzhu Yu, Nanze Long, Xiao Chen, Wei Front Nutr Nutrition BACKGROUND: Prompt diagnosis of malnutrition and appropriate interventions can substantially improve the prognosis of patients with cancer; however, it is difficult to unify the tools for screening malnutrition risk. 3D imaging technology has been emerging as an approach to assisting in the diagnosis of diseases, and we designed this study to explore its application value in identifying the malnutrition phenotype and evaluating nutrition status. METHODS: Hospitalized patients treating with maintenance chemotherapy for advanced malignant tumor of digestive system were recruited from the Department of Oncology, whose NRS 2002 score > 3. Physical examination and body composition data of patients at risk for malnutrition were analyzed by physicians trained to complete a subjective global assessment. The facial depression index was recognized using the Antera 3D® system, temporal and periorbital depression indexes were acquired using the companion software Antera Pro. This software captures quantitative data of depression volume, affected area, and maximum depth of temporal and periorbital concave areas. RESULTS: A total of 53 inpatients with malnutrition-related indicators were included. The volume of temporal depression was significantly negatively correlated with upper arm circumference (r = −0.293, p = 0.033) and calf circumference (r = −0.285, p = 0.038). The volume and affected area of periorbital depression were significantly negatively correlated with fat mass index (r = −0.273, p = 0.048 and r = −0.304, p = 0.026, respectively) and percent body fat (r = −0.317, p = 0.021 and r = −0.364, p = 0.007, respectively). The volume and affected area of temporal depression in patients with muscle loss phenotype (low arm circumference/low calf circumference/low handgrip strength/low fat-free mass index) were significantly higher than those in patients without muscle loss. Moreover, patients with fat mass loss phenotype (low fat mass index) showed a significant increase in the volume and affected area of periorbital depression. CONCLUSION: The facial temporal region, and periorbital depression indicators extracted by 3D image recognition technology were significantly associated with the phenotype of malnutrition-related muscle and fat loss and showed a trend of grade changes in the population of different subjective global assessment nutritional classifications. Frontiers Media S.A. 2023-03-13 /pmc/articles/PMC10042485/ /pubmed/36992909 http://dx.doi.org/10.3389/fnut.2023.1115079 Text en Copyright © 2023 Chen, Wang, Han, Li, Yu, Long and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Nutrition Chen, Moxi Wang, Xue Han, Meifen Li, Yunzhu Yu, Nanze Long, Xiao Chen, Wei Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study |
title | Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study |
title_full | Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study |
title_fullStr | Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study |
title_full_unstemmed | Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study |
title_short | Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study |
title_sort | temporal and periorbital depressions identified by 3d images are correlated with malnutrition phenotypes in cancer patients: a pilot study |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042485/ https://www.ncbi.nlm.nih.gov/pubmed/36992909 http://dx.doi.org/10.3389/fnut.2023.1115079 |
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