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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

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Published on: September 27, 2024

Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With

Seo Hyun Oh¹, Youngho Lee², Jeong-Heum Baek³

¹Department of IT Convergence, Gachon University, 1342, Seongnam-daero, Sung-nam si, Republic of Korea.

JMIR Medical Informatics

|August 19, 2025

Summary

A novel deep learning model using Image Generator for Health Tabular Data (IGHT) transforms electronic medical record (EMR) data into images for improved colorectal cancer survival prediction. The VGG16 model demonstrated superior accuracy and interpretability, offering a promising clinical decision support tool.

Keywords:

EHR South Korea VGG16 cancer clinical informatics colon convolutional neural networks deep learning electronic health record health care model models neural network predict prediction predictions predictive rectum

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Area of Science:

Oncology
Artificial Intelligence
Medical Informatics

Background:

Artificial intelligence (AI) enhances predictive modeling in healthcare, especially oncology.
Traditional methods struggle with complex clinical variable interactions.
Image Generator for Health Tabular Data (IGHT) converts electronic medical record (EMR) data into 2D images for deep learning analysis.

Purpose of the Study:

Develop and evaluate a deep learning model for predicting 5-year overall survival in colorectal cancer patients using EMR data.
Assess the clinical interpretability of model predictions via explainable AI (XAI) techniques.

Main Methods:

Anonymized EMR data from 3321 patients were analyzed.
Clinical variables were transformed into 2D image matrices using IGHT.
Compared Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and VGG16 models for survival prediction.
Utilized Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability.

Main Results:

The VGG16 model achieved superior performance (78.44% accuracy for colon cancer, 74.83% for rectal cancer) with high specificity.
VGG16 demonstrated a better balance of sensitivity and specificity compared to ANN and CNN.
Grad-CAM identified key prognostic features, including age, gender, smoking history, ASA grade, liver/pulmonary disease, and CEA levels.

Conclusions:

The IGHT-based VGG16 model shows potential for accurate and interpretable 5-year survival prediction in colorectal cancer.
The model can stratify patients into risk categories, serving as a potential clinical decision support system (CDSS).
Further validation with multicenter cohorts and prospective studies is needed for generalizability and clinical integration.