Quantitative Radiomic Analysis for Prognostic Medical Applications

Abstract

Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine as it lacks interpretability. The core of this thesis is radiomic biology analysis that connects radiomic imaging information with molecular biology information to achieve a medical “gold standard” for cancer management. Developing methods to succeed in the feature selection process of data of varying dimensions is the main goal of this paper. Our major contributions in this thesis can be summarised as below: 1. We firstly proposed an unsupervised learning framework to guide supervised learning in the reduction of feature dimensions from large cohorts Non-Small Cell Lung Cancer data (NSCLC) on both clinical data and radiomic data for survival prediction. 2. An interpretable machine learning approach measures the contribution of features for each case and the connection of radiomics to its underlying biological features to make clinical decisions in leukemia and breast cancer cases. The weight of the feature can be estimated by measuring the distance of the approximate perturbation centre. 3. Based on the framework of feature selection that we proposed, to ensure the fairness and stability of the data split when processing classification results, cross-validation is embedded in the training process. We further propose a traversal selection method, optimising the computational complexity of the selection process to obtain the most robust feature set.