Radioactive multi-omics collaborative learning for adaptive radiation therapy eligibility prediction in nasopharyngeal carcinoma

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.