Prediction of survival outcome based on clinical features and pretreatment 18FDG-PET/CT for HNSCC patients. (October 2020) - Ghosh, Sayantani; Maulik, Shaurav; Chatterjee, Sanjoy; Mallick, Indranil; Chakravorty, Nishant; Mukherjee, Jayanta
Highlights: The main objective of the study is to develop a classifier to predict the outcome for HNSCC patients and compare the performance of the model with the conventional statistical model (CoxPH). Clinical features of each patient are provided in the dataset, and other features are calculated using LIFEx software. In this method, 3-D contours are drawn around voxels equal to or greater than 40% SUVmax. For testing cohort, GBDT achieves a balanced accuracy of 88%, where conventional statistical model reported a balanced accuracy of 81.5%. Abstract: Background and objective: In this study, we have analysed pretreatment positron-emission tomography/ computed tomography (PET/CT) images of head and neck squamous cell carcinoma (HNSCC) patients. We have used a publicly available dataset for our analysis. The clinical features of the patient, PET quantitative parameters, and textural indices from pretreatment PET-CT images are selected for the study. The main objective of the study is to use classifiers to predict the outcome for HNSCC patients and compare the performance of the model with the conventional statistical model (CoxPH). Methods: We have applied a 40% fixed SUV threshold method for tumour delineation. Clinical features of each patient are provided in the dataset, and other features are calculated using LIFEx software. For predicting the outcome, we have implemented three classifiers - Random Forest classifier, Gradient Boosted Decision tree (GBDT) and Decision tree classifier. We have trained each model using 93 data points and test the model performance using 39 data points. The best model - GBDT is chosen based on the performance metrics. Results: It is observed that typically three features: MTV (Metabolic tumour Volume), primary tumour site and GLCM_correlation are significant for prediction of survival outcome. For testing cohort, GBDT achieves a balanced accuracy of 88%, where conventional statistical model reported a balanced accuracy of 81.5%. Conclusions: The proposed classifier achieves higher accuracy than the state of the art technique. Using this classifier we can estimate the HNSCC patient's outcome, and depending upon the outcome treatment policy can be selected.