A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas
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Published:2023-10-27
Issue:1
Volume:23
Page:
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ISSN:1470-7330
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Container-title:Cancer Imaging
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language:en
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Short-container-title:Cancer Imaging
Author:
Li Yin,Zheng Kaiyi,Li Shuang,Yi Yongju,Li Min,Ren Yufan,Guo Congyue,Zhong Liming,Yang Wei,Li Xinming,Yao Lin
Abstract
Abstract
Background
The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a laborious and time-intensive process. Previous deep learning-based studies have mainly been focused on automatic tumor segmentation or predicting genetic/histological features. However, few studies have specifically addressed the identification of infiltrated brain areas. To bridge this gap, we aim to develop a model that can simultaneously identify infiltrated brain areas and perform accurate segmentation of gliomas.
Methods
We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: identifying infiltrated brain areas segmentation of gliomas. The multi-task model leverages shaped location and boundary information to enhance the performance of both tasks. Our retrospective study involved 354 glioma patients (grades II-IV) with single or multiple brain area infiltrations, which were divided into training (N = 270), validation (N = 30), and independent test (N = 54) sets. We evaluated the predictive performance using the area under the receiver operating characteristic curve (AUC) and Dice scores.
Results
Our multi-task model achieved impressive results in the independent test set, with an AUC of 94.95% (95% CI, 91.78–97.58), a sensitivity of 87.67%, a specificity of 87.31%, and accuracy of 87.41%. Specifically, for grade II-IV glioma, the model achieved AUCs of 95.25% (95% CI, 91.09–98.23, 84.38% sensitivity, 89.04% specificity, 87.62% accuracy), 98.26% (95% CI, 95.22–100, 93.75% sensitivity, 98.15% specificity, 97.14% accuracy), and 93.83% (95%CI, 86.57–99.12, 92.00% sensitivity, 85.71% specificity, 87.37% accuracy) respectively for the identification of infiltrated brain areas. Moreover, our model achieved a mean Dice score of 87.60% for the whole tumor segmentation.
Conclusions
Experimental results show that our multi-task model achieved superior performance and outperformed the state-of-the-art methods. The impressive performance demonstrates the potential of our work as an innovative solution for identifying tumor-infiltrated brain areas and suggests that it can be a practical tool for supporting clinical decision making.
Funder
National Natural Science Foundation of China Basic and Applied Basic Research Foundation of Guangdong Province
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Oncology,General Medicine,Radiological and Ultrasound Technology
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