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Background <p>This article examines the impact of skeletal muscle mass and sarcopenia on the treatment of head and neck tumors using an AI-based review process and critically evaluates the Synthory.ai platform for systematic literature analysis. Articles from PubMed and PubMed Central published between January 2018 and March 2025 were included.</p> Methods <p>An automated review was generated via Synthory.ai using the query “role of sarcopenia in the treatment of head and neck cancer.” The platform applies LLM-based (Large Language Model)&#xa0;relevance scoring for PubMed pre-selection. Only randomized controlled trials, cohort studies, and qualitative investigations with freely available full text were included. Of 146 screened articles, 26&#xa0;were included and assessed using the Newcastle–Ottawa Scale and PRISMA criteria. One additional article (Jung et&#xa0;al.) was added through manual PubMed search.</p> Results <p>Reduced skeletal muscle mass is associated with decreased overall survival (hazard ratio 1.36–4.51 in multivariate analyses) and increases the risk of dose-limiting toxicities during platinum-based chemoradiotherapy as well as postoperative complications. Studies applying comprehensive sarcopenia assessments (muscle mass, strength, and function) demonstrate stronger effect sizes. Studies differ substantially in terms of measurement methods and cut-off values.</p> Conclusion <p>Reduced skeletal muscle mass is a&#xa0;negative prognostic marker in head and neck cancer patients; comprehensive sarcopenia assessment including muscle strength and function is substantially more predictive. Standardization of measurement methods and cut-off values is urgently needed. While AI-assisted review tools are valuable for literature analysis, they require expert validation and transparent documentation of selection processes.</p>

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Einsatz von KI in der Erstellung von wissenschaftlichen Reviews am Beispiel der Sarkopenie bei Kopf-Hals-Krebs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月27日

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  • Markus Blaurock,
  • Daniel Strüder,
  • Sabina Ulbricht,
  • Sabine Felser

摘要

Background

This article examines the impact of skeletal muscle mass and sarcopenia on the treatment of head and neck tumors using an AI-based review process and critically evaluates the Synthory.ai platform for systematic literature analysis. Articles from PubMed and PubMed Central published between January 2018 and March 2025 were included.

Methods

An automated review was generated via Synthory.ai using the query “role of sarcopenia in the treatment of head and neck cancer.” The platform applies LLM-based (Large Language Model) relevance scoring for PubMed pre-selection. Only randomized controlled trials, cohort studies, and qualitative investigations with freely available full text were included. Of 146 screened articles, 26 were included and assessed using the Newcastle–Ottawa Scale and PRISMA criteria. One additional article (Jung et al.) was added through manual PubMed search.

Results

Reduced skeletal muscle mass is associated with decreased overall survival (hazard ratio 1.36–4.51 in multivariate analyses) and increases the risk of dose-limiting toxicities during platinum-based chemoradiotherapy as well as postoperative complications. Studies applying comprehensive sarcopenia assessments (muscle mass, strength, and function) demonstrate stronger effect sizes. Studies differ substantially in terms of measurement methods and cut-off values.

Conclusion

Reduced skeletal muscle mass is a negative prognostic marker in head and neck cancer patients; comprehensive sarcopenia assessment including muscle strength and function is substantially more predictive. Standardization of measurement methods and cut-off values is urgently needed. While AI-assisted review tools are valuable for literature analysis, they require expert validation and transparent documentation of selection processes.