Abstract
Purpose
To explore interpretable machine learning (ML) methods, with the hope of adding more prognosis value, for predicting survival for patients with Oropharyngeal-Cancer (OPC).
Methods
A cohort of 427 OPC patients (Training 341, Test 86) from TCIA database was analyzed. Radiomic features of gross-tumor-volume (GTV) extracted from planning CT using Pyradiomics, and HPV p16 status, etc. patient characteristics were considered as potential predictors. A multi-level dimension reduction algorithm consisting of Least-Absolute-Selection-Operator (Lasso) and Sequential-Floating-Backward-Selection (SFBS) was proposed to effectively remove redundant/irrelevant features. The interpretable model was constructed by quantifying the contribution of each feature to the Extreme-Gradient-Boosting (XGBoost) decision by Shapley-Additive-exPlanations (SHAP) algorithm.
Results
The Lasso-SFBS algorithm proposed in this study finally selected 14 features, and our prediction model achieved an area-under-ROC-curve (AUC) of 0.85 on the test dataset based on this feature set. The ranking of the contribution values calculated by SHAP shows that the top predictors that were most correlated with survival were ECOG performance status, wavelet-LLH_firstorder_Mean, chemotherapy, wavelet-LHL_glcm_InverseVariance, tumor size. Those patients who had chemotherapy, with positive HPV p16 status, and lower ECOG performance status, tended to have higher SHAP scores and longer survival; who had an older age at diagnosis, heavy drinking and smoking pack year history, tended to lower SHAP scores and shorter survival.
Conclusion
We demonstrated predictive values of combined patient characteristics and imaging features for the overall survival of OPC patients. The multi-level dimension reduction algorithm can reliably identify the most plausible predictors that are mostly associated with overall survival. The interpretable patient-specific survival prediction model, capturing correlations of each predictor and clinical outcome, was developed to facilitate clinical decision-making for personalized treatment.
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Data availability
Source data used during the current study are available from The Cancer Imaging Archive website (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=33948764). All datasets generated during the current study are available from the corresponding author on reasonable request.
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Funding
This research was funded by the National Natural Science Foundation of China (INo.62001380) and the General Special Scientific Research Program of Shaanxi Provincial Education Department (20JK0910).
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All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by TF, CL and XSQ. The first draft of the manuscript was written by XP and all authors commented on previous versions of the manuscript. RRS and RKC gave valuable comments when the paper was revised, which greatly improved the quality of the article. All authors read and approved the final manuscript.
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Pan, X., Feng, T., Liu, C. et al. A survival prediction model via interpretable machine learning for patients with oropharyngeal cancer following radiotherapy. J Cancer Res Clin Oncol 149, 6813–6825 (2023). https://doi.org/10.1007/s00432-023-04644-y
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DOI: https://doi.org/10.1007/s00432-023-04644-y