Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data

Authors

Mohamed A Naser
Kareem A Wahid
Abdallah S R Mohamed
Moamen Abobakr Abdelaal
Renjie He
Cem Dede
Lisanne V van Dijk
Clifton D Fuller

Publication Date

1-1-2022

Journal

Head and Neck Tumor Segmentation and Outcome Prediction

Abstract

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N = 224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N = 101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694, placing 2nd in the competition. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed.

DOI

10.1007/978-3-030-98253-9_27

PMID

35399868

PMCID

PMC8991450

PubMedCentral® Posted Date

4-8-2022

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes