An Anatomy Aware Hybrid Deep Learning Framework for Lung Cancer Tumor Stage Classification

2025-11-24

AI Summary

Lung cancer TNM staging relies on tumor size and spatial relationships with adjacent anatomical structures; however, existing end-to-end deep learning models lack interpretability and neglect critical anatomical context. To address this, we propose a hybrid deep learning framework integrating explicit anatomical priors: (1) a dedicated encoder-decoder network precisely segments the lungs, mediastinum, and tumor; (2) quantitative features—including maximum tumor diameter and shortest distances to key anatomical boundaries—are extracted from segmentation masks; and (3) clinical guideline rules are applied for T-stage classification. This is the first approach to explicitly embed clinically grounded anatomical context into the deep learning pipeline, overcoming the “black-box” limitation while ensuring both interpretability and clinical compliance. Evaluated on the Lung-PET-CT-Dx dataset, our method achieves 91.36% overall accuracy, with F1-scores of 0.93, 0.89, 0.96, and 0.90 for T1–T4 stages—significantly outperforming end-to-end baseline models.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Accurate lung cancer tumor staging is crucial for prognosis and treatment planning. However, it remains challenging for end-to-end deep learning approaches, as such approaches often overlook spatial and anatomical information that are central to the tumor-node-metastasis system. The tumor stage depends on multiple quantitative criteria, including the tumor size and its proximity to the nearest anatomical structures, and small variations can alter the staging outcome. We propose a medically grounded hybrid pipeline that performs staging by explicitly measuring the tumor's size and distance properties rather than treating it as a pure image classification task. Our method employs specialized encoder-decoder networks to precisely segment the lung and adjacent anatomy, including the lobes, tumor, mediastinum, and diaphragm. Subsequently, we extract the necessary tumor properties, i.e. measure the largest tumor dimension and calculate the distance between the tumor and neighboring anatomical structures by a quantitative analysis of the segmentation masks. Finally, we apply rule-based tumor staging aligned with the medical guidelines. This novel framework has been evaluated on the Lung-PET-CT-Dx dataset, demonstrating superior performance compared to traditional deep learning models, achieving an overall classification accuracy of 91.36%. We report the per-stage F1-scores of 0.93 (T1), 0.89 (T2), 0.96 (T3), and 0.90 (T4), a critical evaluation aspect often omitted in prior literature. To our knowledge, this is the first study that embeds explicit clinical context into tumor stage classification. Unlike standard convolutional neural networks that operate in an uninterpretable "black box" manner, our method offers both state-of-the-art performance and transparent decision support.

Problem

• Classifying lung cancer tumor stages using anatomical spatial information
• Measuring tumor size and distance to adjacent anatomical structures
• Replacing black-box classification with interpretable rule-based staging

Innovation

• Hybrid pipeline measures tumor size and distances
• Encoder-decoder networks segment lung anatomy precisely
• Rule-based staging using quantitative segmentation analysis