Integrating radiomics and gene expression by mapping on the image with improved DeepInsight for clear cell renal cell...

Radiomics analysis extracts high-dimensional features from medical images, which are used to predict outcomes in machine learning (ML). Recently, deep-learning methods have become applicable to image data converted from nonimage samples.

This study conducted a comparative analysis of outcome-prediction performance using radiomics with a conventional ML approach and deep-learning (DL) approach utilising DeepInsight. Furthermore, we enhance the DeepInsight model by integrating radiomics features with gene expression data. This integration aims to improve predictive power and provide a more comprehensive understanding of ccRCC, ultimately contributing to more personalized and effective treatment strategies.

A total of 142 patients with clear cell renal cell carcinoma who underwent surgery were divided into training and test datasets. Radiomics features were extracted in the entire tumour region from CT images. The two-year disease-free survival was predicted using ML and DL. ML was used for selective features after LASSO regression. ML algorithms were employed for classification, including the support vector machine, k-nearest neighbour, and neural network classifiers. For DL, radiomics features and gene-expression data were converted into image data with DeepInsight, and classification tasks were performed with DL techniques such as AlexNet, SqueezeNet, and InceptionNet.

For ML, 17 prognosis-related radiomic features were selected from the LASSO regression. The ML accuracy was 76.5 %, 71.4 %, and 78.1 % for the support vector machine, k-nearest neighbour, and neural network models, respectively. For DL, the accuracies were 76.7 %, 83.1 %, and 85.4 % for AlexNet, SqueezeNet, and InceptionNet, respectively. Furthermore, the integrated DeepInsight models exhibited the highest accuracy of 90.9 %.

The proposed DL approach utilising DeepInsight demonstrated a significant improvement in outcome-prediction performance compared with the conventional ML approach. Furthermore, the integration of DL with radiomics features and gene-expression data effectively captures the relationship between biological information and image data, rendering it a promising tool for enhancing outcome-prediction capabilities.