Radiation Pneumonitis Prediction Using Multi-Omics Fusion Based on a Novel Machine Learning Pipeline

Radiomics and dosiomics as two kinds of imaging features are widely used for machine learning-based prognosis prediction in adaptive radiotherapy. Feature selection and modeling are two main components in the radiotherapy prognosis prediction pipeline. So far, few studies have considered both the stability and discrimination ability of the features at the stage of feature selection. Also, in the modeling phase, to fuse radiomics and dosiomics features, most works have only directly concatenated radiomics or dosiomics features as inputs into a model, which may omit the complementary information across different omics. Additionally, overfitting is a common issue when the training data is not enough or contains noises. To solve these problems, in this study, we have developed a novel machine learning-based pipeline and applied it to predict radiation pneumonitis for stage III non-small cell lung cancer patients under medical Internet of Things. The contributions contain the following: in the feature selection phase, a decision criterion which considers both the feature stability and feature discrimination is developed to determine appropriate feature selection methods; in the modeling phase, we have developed a non-sparse multi-kernel learning method with manifold regularization for multi-omics fusion, which can fully explore patterns from both radiomics and dosiomics features and reduce overfitting coincidently. Experimental results show that the decision criterion works effectively for feature selection method selection. Compared to direct feature concatenation, the proposed multi-kernel fusion strategy performs better. Moreover, manifold regularization can alleviate the overfitting problem.

Automated summary

In this study, a novel machine learning-based pipeline was developed for predicting radiation pneumonitis in non-small cell lung cancer patients. The study considered the stability and discrimination ability of features in the feature selection phase and developed a decision criterion for selecting appropriate feature selection methods. In the modeling phase, a non-sparse multi-kernel learning method with manifold regularization was used to fuse multi-omics data and reduce overfitting.