DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients
with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
- URL: http://arxiv.org/abs/2109.07711v1
- Date: Thu, 16 Sep 2021 04:12:59 GMT
- Title: DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients
with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
- Authors: Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, and
Jinman Kim
- Abstract summary: Nasopharyngeal Carcinoma (NPC) is a worldwide malignant epithelial cancer.
Deep learning has been introduced to the survival prediction in various cancers including NPC.
In this study, we introduced the concept of multi-task leaning into deep survival models to address the overfitting problem resulted from small data.
- Score: 15.386240118882569
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Nasopharyngeal Carcinoma (NPC) is a worldwide malignant epithelial cancer.
Survival prediction is a major concern for NPC patients, as it provides early
prognostic information that is needed to guide treatments. Recently, deep
learning, which leverages Deep Neural Networks (DNNs) to learn deep
representations of image patterns, has been introduced to the survival
prediction in various cancers including NPC. It has been reported that
image-derived end-to-end deep survival models have the potential to outperform
clinical prognostic indicators and traditional radiomics-based survival models
in prognostic performance. However, deep survival models, especially 3D models,
require large image training data to avoid overfitting. Unfortunately, medical
image data is usually scarce, especially for Positron Emission
Tomography/Computed Tomography (PET/CT) due to the high cost of PET/CT
scanning. Compared to Magnetic Resonance Imaging (MRI) or Computed Tomography
(CT) providing only anatomical information of tumors, PET/CT that provides both
anatomical (from CT) and metabolic (from PET) information is promising to
achieve more accurate survival prediction. However, we have not identified any
3D end-to-end deep survival model that applies to small PET/CT data of NPC
patients. In this study, we introduced the concept of multi-task leaning into
deep survival models to address the overfitting problem resulted from small
data. Tumor segmentation was incorporated as an auxiliary task to enhance the
model's efficiency of learning from scarce PET/CT data. Based on this idea, we
proposed a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint
survival prediction and tumor segmentation. Our DeepMTS can jointly learn
survival prediction and tumor segmentation using PET/CT data of only 170
patients with advanced NPC.
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