Deep learning in head & neck cancer outcome prediction.

Scientific Reports
André DiamantJan Seuntjens

Abstract

Traditional radiomics involves the extraction of quantitative texture features from medical images in an attempt to determine correlations with clinical endpoints. We hypothesize that convolutional neural networks (CNNs) could enhance the performance of traditional radiomics, by detecting image patterns that may not be covered by a traditional radiomic framework. We test this hypothesis by training a CNN to predict treatment outcomes of patients with head and neck squamous cell carcinoma, based solely on their pre-treatment computed tomography image. The training (194 patients) and validation sets (106 patients), which are mutually independent and include 4 institutions, come from The Cancer Imaging Archive. When compared to a traditional radiomic framework applied to the same patient cohort, our method results in a AUC of 0.88 in predicting distant metastasis. When combining our model with the previous model, the AUC improves to 0.92. Our framework yields models that are shown to explicitly recognize traditional radiomic features, be directly visualized and perform accurate outcome prediction.

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Citations

Jan 24, 2020·Physics in Medicine and Biology·Chenyang ShenXun Jia
Aug 1, 2020·Canadian Association of Radiologists Journal = Journal L'Association Canadienne Des Radiologistes·Tricia ChinneryPencilla Lang
May 8, 2020·Scientific Reports·Higor Y D SigakiHaroldo V Ribeiro
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Nov 23, 2019·Scientific Reports·Stephen BaekXiaodong Wu
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Software Mentioned

Keras
Tensorflow
Grad
CAM
Inception
CADx
vis

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