An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
This work addresses prognosis accuracy for head and neck cancer patients, offering a potentially more accurate solution by integrating multimodal data, though it appears incremental in combining existing methods.
The paper tackled the problem of predicting patient prognosis for head and neck tumors by proposing a multimodal ensemble network that combines clinical and imaging data, achieving a C-index of 0.72 and first place in the HECKTOR challenge.
Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Traditional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diagnosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient medical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and has the potential to create more accurate solutions. The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources. We propose a multimodal network that ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard (CoxPH) and CNN models to predict prognostic outcomes for patients with head and neck tumors using patients' clinical and imaging (CT and PET) data. Features from CT and PET scans are fused and then combined with patients' electronic health records for the prediction. The proposed model is trained and tested on 224 and 101 patient records respectively. Experimental results show that our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR test set that saved us the first place in prognosis task of the HECKTOR challenge. The full implementation based on PyTorch is available on \url{https://github.com/numanai/BioMedIA-Hecktor2021}.