[Paper Review] LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment

Daeyoung Kim|arXiv (Cornell University)|Jan 26, 2026

Lung Cancer Diagnosis and Treatment0 citations

TL;DR

LungCRCT introduces a latent causal representation learning framework for lung CT analysis that enables causal intervention analysis in lung cancer treatment and achieves a high classification AUC of 93.91% with lightweight downstream models.

ABSTRACT

Due to silence in early stages, lung cancer has been one of the most leading causes of mortality in cancer patients world-wide. Moreover, major symptoms of lung cancer are hard to differentiate with other respiratory disease symptoms such as COPD, further leading patients to overlook cancer progression in early stages. Thus, to enhance survival rates in lung cancer, early detection from consistent proactive respiratory system monitoring becomes crucial. One of the most prevalent and effective methods for lung cancer monitoring would be low-dose computed tomography(LDCT) chest scans, which led to remarkable enhancements in lung cancer detection or tumor classification tasks under rapid advancements and applications of computer vision based AI models such as EfficientNet or ResNet in image processing. However, though advanced CNN models under transfer learning or ViT based models led to high performing lung cancer detections, due to its intrinsic limitations in terms of correlation dependence and low interpretability due to complexity, expansions of deep learning models to lung cancer treatment analysis or causal intervention analysis simulations are still limited. Therefore, this research introduced LungCRCT: a latent causal representation learning based lung cancer analysis framework that retrieves causal representations of factors within the physical causal mechanism of lung cancer progression. With the use of advanced graph autoencoder based causal discovery algorithms with distance Correlation disentanglement and entropy-based image reconstruction refinement, LungCRCT not only enables causal intervention analysis for lung cancer treatments, but also leads to robust, yet extremely light downstream models in malignant tumor classification tasks with an AUC score of 93.91%.

Motivation & Objective

• Motivate early lung cancer detection and proactive respiratory monitoring to improve survival.
• Address limitations of deep learning models (correlation dependence and low interpretability) in treatment analysis.
• Propose a latent causal representation framework for lung cancer progression factors.
• Enable causal intervention analysis for treatment scenarios while maintaining lightweight classification models.

Proposed method

• Use graph autoencoder–based causal discovery to learn latent factors aligned with physical causal mechanisms of lung cancer progression.
• Apply distance correlation disentanglement to improve factor separation.
• Incorporate entropy-based image reconstruction refinement to enhance representation quality.
• Leverage causal representations to support intervention analysis in treatment scenarios.
• Demonstrate robust, lightweight downstream models for malignant tumor classification (AUC 93.91%).

Experimental results

Research questions

• RQ1 How can causal representations be learned from lung CT data to reflect the physical mechanisms of cancer progression?
• RQ2 Can causal intervention analysis be performed on lung cancer treatment scenarios using latent factors?
• RQ3 Do causal representations enable lighter and robust tumor classification models compared to standard deep learning approaches?
• RQ4 What is the impact of distance correlation disentanglement and entropy-based reconstruction on the quality of causal representations?

Key findings

• LungCRCT enables causal intervention analysis for lung cancer treatments.
• The framework yields robust, lightweight downstream classification models.
• Achieves an AUC score of 93.91% on malignant tumor classification tasks.