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

7. Input Variables Patient demographics Tumor parameters
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
Predicted survival: YES or NO
Predictive model
179 cases
Cross-validation

10. Predictability of 6-mon Overall Survival
Method
Features
Sensitivity
Specificity
AUC NB
Age
PalliativeRT
Treatment Completed
0.76
0.75
0.64 LR
Interruption in RT
0.72
0.65 NN
Height
0.74
0.80
0.63
SVM

11. Predictability of 1-yr Overall Survival
Method
Features
Sensitivity
Specificity
AUC NN
Location Reirradiation ECOG ChemoType
0.673
0.667
0.68 RF
Gender
ECOG
PackYears PalliativeRT Treatment Completed
0.627
0.686
0.65

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.