Reliable Gene Mutation Prediction in Clear Cell Renal Cell Carcinoma through Multi-classifier Multi-objective Radiogenomics Model

TL;DR

This paper introduces a novel multi-classifier multi-objective radiogenomics model that improves non-invasive prediction of gene mutations in clear cell renal cell carcinoma by integrating multiple classifiers and optimizing for sensitivity and specificity.

Contribution

The study presents a new multi-classifier multi-objective model with a similarity-based optimization algorithm and evidential reasoning fusion, enhancing prediction reliability over existing methods.

Findings

Achieved AUC over 0.86 for key gene mutations

Outperformed individual classifiers and other fusion strategies

Demonstrated association between CT features and gene mutations

Abstract

Genetic studies have identified associations between gene mutations and clear cell renal cell carcinoma (ccRCC). Because the complete gene mutational landscape cannot be characterized through biopsy and sequencing assays for each patient, non-invasive tools are needed to determine the mutation status for tumors. Radiogenomics may be an attractive alternative tool to identify disease genomics by analyzing amounts of features extracted from medical images. Most current radiogenomics predictive models are built based on a single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is difficult. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) radiogenomics predictive model. To obtain more reliable prediction results, similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. To take advantage of different classifiers, the evidential reasoning (ER) approach was used for fusing the output of each classifier. Additionally, a new similarity-based multi-objective optimization algorithm (SMO) was developed for training the MCMO to predict ccRCC related gene mutations (VHL, PBRM1 and BAP1). Preliminary results revealed an association between some quantitative computed tomography (CT) features and underlying mutations. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) over 0.86 for VHL, PBRM1, and BAP1 genes with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers and yielded more reliable results than other optimization algorithms and commonly used fusion strategies.