Multifactorial cancer treatment outcome prediction through multifaceted radiomics

TL;DR

This paper introduces M-radiomics, a unified framework for multifactorial cancer outcome prediction that integrates multi-modality, multi-classifier, and multi-criteria approaches to improve accuracy and reliability.

Contribution

The paper proposes a novel M-radiomics framework combining multiple classifiers and criteria, along with validation and optimization strategies, to enhance cancer treatment outcome prediction.

Findings

M-radiomics outperforms traditional single-modality models.

The framework improves prediction accuracy for head & neck cancer outcomes.

Validation shows increased reliability and robustness of the proposed method.

Abstract

Accurately predicting the treatment outcome plays a greatly important role in tailoring and adapting a treatment planning in cancer therapy. Although the development of different modalities and personalized medicine can greatly improve the accuracy of outcome prediction, they also bring the three mainly simultaneous challenges including multi-modality, multi-classifier and multi-criteria, which are summarized as multifactorial outcome prediction (MFOP) in this paper. Compared with traditional outcome prediction, MFOP is a more generalized problem. To handle this novel problem, based on the recent proposed radiomics, we propose a new unified framework termed as multifaceted radiomics (M-radiomics). M-radiomics trains multiple modality-specific classifiers first and then optimally combines the output from the outputs of different classifiers which are trained according to multiple different criteria such as sensitivity and specificity. It considers multi-modality, multi-classifier and multi-criteria into a unified framework, which makes the prediction more accurate. Furthermore, to obtain the more reliable predictive performance which is to maximize the similarity between predicted output and labelled vector, a new validation set based reliable fusion (VRF) strategy and reliable optimization models as well as a new recursive two stage hybrid optimization algorithm (RTSH) were also developed. Two clinical problems for predicting distant metastasis and locoregional recurrence in head & neck cancer were investigated to validate the performance and reliability of the proposed M-radiomics. By using the proposed RF strategy and RTSH optimization algorithm, the experimental results demonstrated that M-radiomics performed better than current radiomic models that rely on a single objective, modality or classifier.