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Related Experiment Video

Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Deep-learning/transfer-learning based Overall Survival prediction conditional on Progression-Free Interval with TCGA

Bo Lin1, Yinglei Lai2

  • 1School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China.

Computational Biology and Chemistry
|January 1, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning network predicts cancer patient survival using gene expression data and pathway information. This method, integrating Progression-Free Interval, outperforms existing approaches for most tumor types, aiding in understanding cancer progression.

Keywords:
Deep learningOverall SurvivalPathwayProgression-Free IntervalRNA-seq

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Clinical and experimental data acquisition is often costly, leading to data scarcity in cancer research.
  • Predicting patient Overall Survival (OS) is crucial for treatment planning and clinical trial design.
  • Existing computational methods for survival prediction face challenges with limited sample sizes.

Purpose of the Study:

  • To develop a novel deep-learning network for predicting Overall Survival (OS) using The Cancer Genome Atlas (TCGA) RNA-seq data.
  • To integrate Progression-Free Interval (PFI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway-gene relationships into the predictive model.
  • To address data scarcity by pre-training the network on diverse tumor types.

Main Methods:

  • Developed a novel deep-learning network incorporating transfer learning and fine-tuning.
  • Utilized KEGG pathway-gene relationships to enhance prediction accuracy.
  • Pre-trained the network on data from 31 tumor types to handle low sample sizes.
  • Evaluated performance on 10 TCGA tumor types, comparing against 10 existing survival prediction methods.

Main Results:

  • The novel deep-learning network significantly outperformed existing methods in predicting OS for 9 out of 10 tumor types, based on C-index and Integrated Brier Score.
  • Performance was comparable to existing methods for the Lower Grade Glioma (LGG) tumor type.
  • Identified temporal associations between KEGG pathways (e.g., circadian rhythm, DNA replication) and OS.

Conclusions:

  • The developed deep-learning approach offers a robust and superior method for cancer patient survival prediction, particularly in low-data scenarios.
  • The model's ability to leverage pathway-gene interactions provides insights into cancer biology and progression.
  • This method has the potential to improve clinical decision-making and advance cancer research.