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Background

Machine learning analysis for predicting survival in stage III non-small cell lung cancer patients receiving definitive chemotherapy and proton radiation therapy. D Kunaprayoon , HH Zhang, GL Larson, HK Tsai, GE Laramore , CJ Rossi, P Mohindra , SN Badiyan , WF Hartsell , CB Simone, II

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Background

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  1. Machine learning analysis for predicting survival in stage III non-small cell lung cancer patients receiving definitive chemotherapy and proton radiation therapy D Kunaprayoon, HH Zhang, GL Larson, HK Tsai, GE Laramore, CJ Rossi, P Mohindra, SN Badiyan, WF Hartsell, CB Simone, II Department of Radiation Oncology, University of Maryland School of Medicine ProCure Proton Therapy Center, Oklahoma City ProCure Proton Therapy Center, New Jersey Seattle Cancer Care Alliance Proton Therapy Center-UW Scripps Proton Therapy Center Northwestern Medicine Chicago Proton Therapy Center

  2. Background Standard of care for locally advanced NSCLC (non-small cell lung cancer) is chemoradiation Proton therapy can better spare organs at risk and may be associated with improved outcomes A machine learning analytic approach may help identify what factors are predictive of survival

  3. Objective To evaluate stage III non-small cell lung cancer (NSCLC) patients undergoing definitive treatment with chemotherapy and proton radiation therapy using a machine-learning analytic approach to identify factors predictive for survival.

  4. Statistical Analysis vs Machine Learning fitting probabilistic models low dimensions “significant” understanding • learning • optimization • high dimensions • unsuspected/unknown • prediction vs Both are based on population data, clearly different focus! We need both tool sets + our clinical wisdom !!

  5. It is “Data Science” Machine Learning Machine learning algorithms operate by building model from example inputs in order to make data-driven predictions or decisions, without being explicitly/strictly programmed. Traditional Software Traditional Research Oncology

  6. Methods Study design: Retrospective Patient population: 179 consecutive stage III NSCLC patients receiving chemoradiation at six proton centers enrolled on PCG registry REG001-09 Years enrolled: 2010-2017

  7. Input Variables • Patient demographics • Age • Gender • Race • Smoking history • ECOG performance status • Tumor parameters • Histology • Primary tumor location • Clinical and pathologic stage • Treatment parameters • Target site • Dose and fractionation • Radiation modality • Curative intent • Treatment interruptions • Chemotherapy agents and timing • Prior surgery • Prior treatment history

  8. Predictive Models Naive Bayes (NB) Logistic regression (LR) Neural network (NN) Support vector machine (SVM) Random forest (RF)

  9. Survival Prediction Framework Predictive model 179 cases Cross-validation Predicted survival: YES or NO

  10. Predictability of 6-mon Overall Survival

  11. Predictability of 1-yr Overall Survival

  12. Conclusions This is the largest survival predictive analysis for lung cancer patients treated with proton therapy performed to date. A machine-learning analytic approach using features of patient demographics, tumor parameters, and treatment characteristics shows promise for predicting overall survival in stage III lung cancer patients treated with chemotherapy and proton radiotherapy. Additional prospective data is needed to validate these findings and identify which features are most predictive, which may improve future prediction models.

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