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Predicting five-year mortality in soft-tissue sarcoma patients.

Teja Yeramosu1, Waleed Ahmad1, Azhar Bashir2

  • 1School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA.

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|May 31, 2023
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Summary

Tumor size, M stage, and histological subtype are key predictors of five-year cancer-related mortality in soft-tissue sarcoma (STS) patients. Machine learning models can accurately predict survival, aiding treatment decisions for limb and trunk STS.

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Area of Science:

  • Oncology
  • Machine Learning in Medicine
  • Biostatistics

Background:

  • Soft-tissue sarcoma (STS) is a rare malignancy with variable prognoses.
  • Identifying factors predicting cancer-related mortality is crucial for patient management.
  • Existing predictive models may not fully capture the complexity of STS outcomes.

Purpose of the Study:

  • To identify key factors associated with five-year cancer-related mortality in limb and trunk STS.
  • To develop and validate machine learning (ML) algorithms for predicting this mortality.
  • To provide tools for improved risk stratification and treatment planning.

Main Methods:

  • Analysis of demographic, clinicopathological, and treatment data from the SEER database (2004-2017).
  • Multivariable logistic regression to identify significant mortality predictors.
  • Development and comparison of various ML models (e.g., random forest) using AUC, calibration, and decision curve analysis.
  • External validation of the best-performing model on an institutional dataset.

Main Results:

  • 13,646 STS patients were analyzed; 35.9% experienced five-year cancer-related mortality.
  • The random forest model demonstrated superior performance.
  • Tumor size was the most significant predictor, followed by M stage, histological subtype, age, and surgical excision.
  • External validation achieved an AUC of 0.752.

Conclusions:

  • Clinically significant variables for predicting STS mortality were identified.
  • A validated ML model offers good accuracy and predictability for cancer-related mortality.
  • These findings can assist orthopaedic oncologists in risk-stratifying patients and optimizing treatment strategies.