[1]QIU Chengyu,LI Bing,LAM Saikit,et al.Radioactive multi-omics collaborative learning for adaptive radiation therapy eligibility prediction in nasopharyngeal carcinoma[J].CAAI Transactions on Intelligent Systems,2024,19(1):58-66.[doi:10.11992/tis.202304029]
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Radioactive multi-omics collaborative learning for adaptive radiation therapy eligibility prediction in nasopharyngeal carcinoma

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 1 Page number: 58-66 Column: 学术论文—机器学习 Public date: 2024-01-05
Title:
Radioactive multi-omics collaborative learning for adaptive radiation therapy eligibility prediction in nasopharyngeal carcinoma
Author(s):
QIU Chengyu1; 2;  LI Bing3; 4;  LAM Saikit5;  SHENG Jiabao1;  TENG Xinzhi1;  ZHANG Jiang1;  CHENG Yuting2;  ZHANG Xingyun2;  ZHOU Ta1; 4;  GE Hong3;  ZHANG Yuanpeng1; 2; 4;  CAI Jing1; 4
1. Department of Health Science, Technology and Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China;
2. Department of Medical Informatics, Nantong University, Nantong 226019, China;
3. The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China;
4. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China;
5. Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong 999077, China
Keywords:
data fusion; machine learning; feature extraction; feature selection; forecasting; image analysis; adaptive algorithms; nasopharyngeal carcinoma; multi-omic
CLC:
TP391.71
DOI:
10.11992/tis.202304029
Abstract:
Traditional radiation omics models, including radiomics, dosiomics, and contouromics, typically adopt feature splicing, which tends to ignore the specific statistical attributes of different omics and therefore leads to overfitting. A multi-omics collaborative learning (MOCL) algorithm focused on consistency constraints and adaptive weights was proposed in the study to address this problem. The MOCL algorithm employs consistency constraints to explore complementary patterns among heterogeneous omics features and adaptively learns their weights using Shannon entropy while avoiding overfitting through compactness mapping. An experiment was conducted on the clinical imaging data of 311 patients with nasopharyngeal carcinoma using MOCL. The experimental result is compared with three traditional machine learning algorithms and two multiperspective algorithms. The results demonstrate that MOCL has certain advantages in collaborative learning of multi-omics and can provide a valuable prediction basis for adaptive radiotherapy qualification in the case of nasopharyngeal carcinoma.
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