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

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 patterns^2,3. 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^....

Protocol

1. Extraction of radiomic features using 3D Slicer PyRadiomics extension

NOTE: The following steps are designed to compute radiomic features of a lung CT DICOM file using 3D Slicer PyRadiomics extension and to save in a file of comma separated value (csv) format.

1. Install and open 3D Slicer (use the latest stable release from https://download.slicer.org/.
2. Install the PyRadiomics Extension and RT Slicer.
   1. In the menu bar, go to View > Extensions Manager. Then, search for Radiomics or SlicerRadiomics and RT Slicer.
   ....

Results

Dataset summary
The NSCLC Radiomics dataset comprises 422 CT volumes of lung cancer stage I, II, and III patients. While the number of CT datasets with early-stage cancer (I, II) is 134, the data samples with advanced-stage cancer (IIIa, IIIb) are 288. The dataset exhibited a significant class imbalance, with a higher number of advanced-stage (stage III) cases compared to early-stage (stage I and stage II) cases. To address this imbalance, oversampling was applied to the extracted radiomic features t.......

Discussion

The robustness and reliability of the proposed framework are evident from the high values of evaluation metrics, including accuracy, recall, precision, and F-1 score²⁴. All scores obtained over 90% performance on the test data with a 5-fold CV employed during MLP training.

The performance and validity of the proposed GL-RFE framework were further supported through visualization techniques. Correlation heatmaps²⁵ in

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
3D Slicer Software	Official Website	5.x	Medical image visualization, segmentation, and ROI extraction for radiomics analysis
Imbalanced-learn Package	PyPI	0.11+	Handling class imbalance (e.g., SMOTE)
Matplotlib  Package	PyPI	3.x	Plotting training curves and feature importance
NumPy Package	PyPI	1.26.x	Numerical operations and feature matrix handling
Pandas Package	PyPI	2.x	Data preprocessing and structured dataset management
PyRadiomics Package	PyPI	3.x	Extraction of radiomic features from CT images
PyTorch  Package	PyPI	2.x	Deep learning framework for MLP and gradient computation
Scikit-learn Package	PyPI	1.3.x	Model evaluation (accuracy, precision, recall, F1-score)
SciPy  Package	PyPI	1.11+	Statistical analysis and validation
Seaborn  Package	PyPI	0.13.x	Heatmaps for feature correlation analysis
Torch.nn Module	PyPI	2.x	Neural network architecture (layers, activations)
Torch.optim Module	PyPI	2.x	Optimization algorithms (e.g., Adam)