Deep learning in head & neck cancer outcome prediction

被引:136
|
作者
Diamant, Andre [1 ]
Chatterjee, Avishek
Vallieres, Martin
Shenouda, George
Seuntjens, Jan
机构
[1] McGill Univ, Med Phys Unit, 1001 Decarie Blvd, Montreal, PQ H4A 3J1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
NEURAL-NETWORKS; METASTASES; SURVIVAL; FEATURES;
D O I
10.1038/s41598-019-39206-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:10
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