Radiomics-Dosiomics-Contouromics Collaborative Learning for Adaptive Radiotherapy Eligibility Prediction in Nasopharyngeal Carcinoma
Radiomics, dosiomics and contouromics have been combined to predict adaptive radiotherapy eligibility in nasopharyngeal carcinoma. However, the commonly-used feature concatenation ignores the complementary or consistency relationship across different omics feature spaces. Also, the number of features increases with the concatenation of omics, leading to the curse of dimensionality and potential overfitting. To address the issues, in this study, multi-omics collaborative learning MOCL is developed. In MOCL, a priori knowledge-driven consistency regularization associated with Shannon entropy is designed to automatically explore the weighting consistency across different omics feature spaces. In addition, a label soften strategy is adopted to enlarge the margins between different classes, rendering more freedom for the models to fit the soft label matrix. To avoid overfitting, we design a regularized term deduced from manifold learning to keep samples in the label space as close as possible if they are in the same manifold in the feature space. Experimental results on 311 nasopharyngeal carcinoma patients collected from the Hong Kong Queen Elizabeth Hospital demonstrate the promising performance of MOCL.
