Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

Hina Shakir; Mohammad Mohatram; Javeed Hussain; Syed Rizwan Ali; Muhammad Irfan Memon

doi:10.3791/70181

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Method Article

Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

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DOI:

10.3791/70181

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April 30th, 2026

Hina Shakir¹ , Mohammad Mohatram² , Javeed Hussain² , Syed Rizwan Ali³ , Muhammad Irfan Memon²

¹Department of Software Engineering, Bahria University, ²Global College of Engineering and Technology, ³Software Engineering & Business Incubation Center, Bahria University Karachi

Summary

Presented here is a deep learning-based feature selection method that leverages gradients of a neural network loss function with respect to input features to identify and prioritize those that most strongly influence lung cancer stage detection.

Abstract

Radiomics enables the extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited sample sizes, making feature selection a critical step for building reliable predictive models. This study proposes a gradient-loss recursive feature elimination (GL-RFE) framework that integrates gradient sensitivity analysis from a deep neural network to identify the most influential radiomic features for lung cancer stage detection. A total of 106 radiomic features were extracted from chest computed tomography (CT) scans using the PyRadiomics extension of the 3D Slicer platform. The proposed method evaluates feature importance by computing gradients of the network loss with respect to input features and recursively eliminates features with minimal contribution. The resulting top 15 radiomic features are used to train a deep neural network classifier for distinguishing early-stage and advanced-stage lung cancer. The proposed framework achieves strong classification performance, with an accuracy of 90.22%, precision of 90.10%, recall of 90.24%, and F1-score of 90.16% on the test dataset. Visualization analyses, including correlation heat maps and distribution plots, further confirm reduced feature redundancy and improved class separability. Compared to conventional feature selection techniques, GL-RFE effectively captures nonlinear feature interactions and enhances model generalization. The presented protocol provides a reproducible and interpretable methodology for radiomics-based cancer stage detection. It is particularly suitable for high-dimensional, small-sample biomedical datasets and has potential applications in other domains, such as genomics and multimodal clinical analysis.

Introduction

Lung cancer remains one of the major types of cancer, leading to serious health concerns, often leading to death¹. Radiomics enables quantitative characterization of medical images by extracting large sets of features that describe tumor shape, texture, and intensity patterns²^,³. These features, also termed handcrafted features, serve as potential biomarkers for diagnosis, prognosis, and treatment response of lung cancer. However, radiomics datasets are typically high-dimensional and sample-limited, leading to redundant and noisy features that degrade model performance^....