Machine learning-based treatment outcome prediction in head and neck cancer using integrated noninvasive diagnostics - Takeaways - MDSpire

Machine learning-based treatment outcome prediction in head and neck cancer using integrated noninvasive diagnostics

  • By

  • Melda Yeghaian

  • Stefano Trebeschi

  • Marina Herrero-Huertas

  • Francisco Javier Mendoza Ferradás

  • Paula Bos

  • Maarten J. A. van Alphen

  • Marcel A. J. van Gerven

  • Regina G. H. Beets-Tan

  • Zuhir Bodalal

  • Lilly-Ann van der Velden

  • December 8, 2025

  • 0 min

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  • 1

    Head and neck squamous cell carcinoma (HNSCC) is a prevalent cancer, accounting for 4.5% of diagnoses and 4.6% of cancer-related deaths globally.

  • 2

    Traditional prognostic models for HNSCC, like TNM staging and HPV status, often overlook the disease's heterogeneity and emerging biomarkers.

  • 3

    Machine learning models integrating clinical, blood, and MRI data can enhance predictions of one-year survival and feeding tube dependence in HNSCC patients.

  • 4

    The study utilized random forest classifiers to analyze data from HNSCC patients, assessing survival and feeding tube needs post-surgery.

  • 5

    Results indicated that machine learning approaches could improve patient stratification and supportive care in HNSCC treatment.

Original Source(s)

Machine learning-based treatment outcome prediction in head and neck cancer using integrated noninvasive diagnostics

  • by Melda Yeghaian, Stefano Trebeschi, Marina Herrero-Huertas, Francisco Javier Mendoza Ferradás, Paula Bos, Maarten J. A. van Alphen, Marcel A. J. van Gerven, Regina G. H. Beets-Tan, Zuhir Bodalal, Lilly-Ann van der Velden

  • December 8, 2025

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