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Machine Learning-Driven Risk Stratification and Adjuvant Treatment Guidance in Oral Cavity Cancer.

Andrea Costantino1, Nir Tsur2, Daniel Uralov3

  • 1Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.

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Summary
This summary is machine-generated.

Machine learning models effectively stratify oral cavity squamous cell carcinoma (OCSCC) patients into low, intermediate, and high-risk groups for overall survival (OS). This stratification guides adjuvant therapy decisions, intensifying treatment for high-risk patients and potentially de-escalating for low-risk individuals.

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Area of Science:

  • Oncology
  • Machine Learning
  • Biostatistics

Background:

  • Oral cavity squamous cell carcinoma (OCSCC) poses significant challenges in predicting patient outcomes.
  • Accurate postoperative risk stratification is crucial for tailoring adjuvant therapy and improving overall survival (OS).

Purpose of the Study:

  • To develop and validate machine learning (ML) models for postoperative risk stratification in OCSCC.
  • To assess if ML-derived risk groups influence the effectiveness of adjuvant therapy on OS.

Main Methods:

  • Utilized the National Cancer Database to identify OCSCC patients treated with primary surgery.
  • Developed and tested DeepSurv, NMTLR, and RSF ML models on a surgery-alone cohort.
  • Generated risk scores for a larger cohort, categorizing patients into low, intermediate, and high-risk groups to analyze adjuvant therapy effects.

Main Results:

  • DeepSurv model achieved the highest performance (C-index 0.73), with similar results from NMTLR/RSF.
  • ML-derived risk groups showed distinct 5-year OS rates: 77.6% (low), 53.0% (intermediate), and 29.3% (high).
  • Adjuvant therapy (RT/CRT) significantly improved OS in intermediate and high-risk groups, but not in the low-risk group. CRT offered a modest benefit over RT.

Conclusions:

  • ML-based risk stratification accurately identifies OCSCC patients who benefit most from adjuvant therapy.
  • Supports intensifying treatment for intermediate/high-risk patients and considering de-intensification for low-risk patients.
  • External prospective validation is recommended for clinical implementation.