NDLI: Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued

Content Provider	Springer Nature : BioMed Central
Author	Li, Song Wan, Xia Deng, Yu-Qin Hua, Hong-Li Li, Sheng-Lan Chen, Xi-Xiang Zeng, Man-Li Zha, Yunfei Tao, Ze-Zhang
Abstract	Background The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. Methods A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. Results The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722–0.760) and 0.737(95%CI:0.720–0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. Conclusions The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.
Related Links	https://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-023-00530-5.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14707330
DOI	10.1186/s40644-023-00530-5
Journal	Cancer Imaging
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-02-09
Access Restriction	Open
Subject Keyword	Oncology Cancer Research Imaging Radiology Nuclear Medicine Peritumoral region Deep learning Nasopharyngeal carcinoma Prognosis prediction
Content Type	Text
Resource Type	Article
Subject	Radiological and Ultrasound Technology Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.3/2023

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