Work

Data tells the truth: A Knowledge distillation method for genomic survival analysis by handling censoring

Year: 2024

Type: article

Abstract: • Supervision Bias Correction : The proposed method, KD (Knowledge Distillation), effectively addresses the supervision bias inherent in censored data for survival prediction. By distilling knowledge from uncensored data, KD rectifies inaccurate hazards and enhances the accuracy of survival predictions. • Enhanced Prediction Accuracy : KD leverages both rectified censored data and uncensored data, leading to improved survival prediction accuracy. This approach not only harnesses the power of censored data but also aligns better with clinical reality, making it a valuable tool for survival analysis. • Superior Performance : The application of the KD method to target cancer sites using The Cancer Genome Atlas (TCGA) dataset consistently outperforms traditional machine learning and deep learning-based methods. This superiority is observed across both target cancer sites and independent cancer cohorts. • Clinical Relevance and Hidden Information : The KD method reveals hidden information from censored data, resulting in conclusions that align more closely with clinical knowledge and the true clinical scenario. This underscores the method’s ability to effectively utilize censored data and emphasizes its substantial value in both cancer research and clinical decision-making. • Open Access Resources : All data and codes related to the study are freely accessible, providing transparency and facilitating further research. They can be accessed at: https://datatellstruth.github.io/ . Survival analysis is a critical tool for cancer research, yet handling censored data remains challenging due to supervision bias and inaccurate hazard estimates. To address these issues, we propose a simple but effective method termed KD, which employs knowledge distillation using uncensored data to rectify the supervision bias in censored data. This approach leverages the combined power of both rectified censored data and uncensored data to improve survival prediction accuracy. Remarkably, our KD method not only effectively harnesses censored data but also better reflects clinical reality, demonstrating its immense value in survival analysis. We applied our KD method to 19 target cancer sites using The Cancer Genome Atlas (TCGA) dataset. Our results consistently outperform traditional machine learning and deep learning-based methods across both target cancer sites and independent cancer cohorts. More importantly, our data-driven approach enables the model to extract hidden information from censored data, leading to conclusions that align more closely with clinical knowledge and scenarios. This validation of our KD method’s effectiveness highlights the substantial value of rational censored data usage, providing valuable insights for cancer research and clinical decisions. All data and codes are freely available at: https://datatellstruth.github.io/ .

Source: Fundamental Research

DOI: 10.1016/j.fmre.2024.06.016