Document Type

Article

Publication Title

arXiv

Abstract

Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Tradi-tional 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 diag-nosis 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 med-ical 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 pre-dict 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 https://github.com/numanai/BioMedIA-Hecktor2021. Team name: MBZUAI-BioMedIA. © 2022, CC BY-NC-SA.

DOI

doi.org/10.48550/arXiv.2202.12537

Publication Date

2-25-2022

Keywords

Computerized tomography, Deep learning, Diagnosis, Gasoline, Hazards, Logistic regression, Medical imaging, Tumors, Cancer prognosis, Convolutional neural network, Cox proportional hazard, CT-scan, Deep learning, Head-and-neck tumor, Logistics regressions, Multi-modal data, Mutli-task logistic regression, PET Scan, Proportional hazards, Convolutional neural networks, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Machine Learning (cs.LG)

Comments

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC BY-NC-SA 4.0

Uploaded 25 March 2022

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