โก Quick Summary
This study introduces a novel approach using deep learning and an Image Generator for Health Tabular Data (IGHT) to predict 5-year overall survival in colorectal cancer patients. The VGG16 model outperformed traditional methods, achieving an accuracy of 78.44% for colon cancer and 74.83% for rectal cancer.
๐ Key Details
- ๐ Dataset: Analyzed anonymized EMR data from 3,321 patients
- ๐งฉ Features used: Demographics, tumor characteristics, lab values, treatment modalities
- โ๏ธ Technology: Deep learning models including ANN, CNN, and VGG16
- ๐ Performance: VGG16: Accuracy 78.44% (colon), 74.83% (rectal)
๐ Key Takeaways
- ๐ IGHT technology transforms tabular EMR data into 2D image matrices.
- ๐ก VGG16 model demonstrated superior predictive performance compared to ANN and CNN.
- ๐ฉโ๐ฌ Grad-CAM visualization identified key prognostic features such as age, gender, and CEA levels.
- ๐ฅ High specificity of VGG16 indicates reliability in identifying long-term survivors.
- ๐ Study conducted at Gil Medical Center, enhancing the understanding of colorectal cancer prognosis.
- ๐ฎ Future research should focus on multicenter validation to ensure generalizability.
๐ Background
The field of oncology has seen significant advancements due to artificial intelligence (AI), particularly in predictive modeling. Traditional methods often struggle with complex clinical data interactions, leading to a need for innovative approaches. The introduction of IGHT technology allows for a more nuanced analysis of electronic medical records (EMRs), paving the way for improved survival predictions in colorectal cancer.
๐๏ธ Study
This retrospective study analyzed the EMR data of 3,321 patients diagnosed with colorectal cancer at the Gil Medical Center. The researchers aimed to develop a deep learning model that could accurately predict 5-year overall survival rates by converting clinical variables into structured 2D image matrices using IGHT. The patients were categorized into colon and rectal cancer groups to account for biological differences.
๐ Results
Among the models tested, the VGG16 architecture achieved the highest accuracy, with 78.44% for colon cancer and 74.83% for rectal cancer. It also demonstrated high specificity, with 89.55% for colon cancer and 87.9% for rectal cancer, indicating its effectiveness in identifying patients likely to survive beyond five years. In contrast, the CNN model showed lower accuracy and specificity, limiting its clinical applicability.
๐ Impact and Implications
The findings from this study highlight the potential of IGHT-based deep learning models as a clinical decision support system (CDSS) tool. By effectively stratifying patients into risk categories with balanced sensitivity and specificity, this approach could significantly enhance prognostic accuracy in colorectal cancer, ultimately improving patient management and outcomes.
๐ฎ Conclusion
This research underscores the promising capabilities of deep learning in predicting long-term survival in colorectal cancer patients. The use of IGHT technology, particularly with the VGG16 model, offers a new avenue for enhancing prognostic accuracy and interpretability. Continued exploration and validation of these models are essential for their integration into clinical practice, paving the way for more personalized patient care.
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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study.
Abstract
BACKGROUND: Recent advances in artificial intelligence (AI) have contributed to improved predictive modeling in health care, particularly in oncology. Traditional methods often rely on structured tabular data, but these approaches can struggle to capture complex interactions among clinical variables. Image generator for health tabular data (IGHT) transform tabular electronic medical record (EMR) data into structured 2D image matrices, enabling the use of powerful computer vision-based deep learning models. This approach offers a novel baseline for survival prediction in colorectal cancer by leveraging spatial encoding of clinical features, potentially enhancing prognostic accuracy and interpretability.
OBJECTIVE: This study aimed to develop and evaluate a deep learning model using EMR data to predict 5-year overall survival in patients with colorectal cancer and to examine the clinical interpretability of model predictions using explainable artificial intelligence (XAI) techniques.
METHODS: Anonymized EMR data of 3321 patients at the Gil Medical Center were analyzed. The dataset included demographic details, tumor characteristics, laboratory values, treatment modalities, and follow-up outcomes. Clinical variables were converted into 2D image matrices using the IGHT. Patients were stratified into colon and rectal cancer groups to account for biological and prognostic differences. Three models were developed and compared: a conventional artificial neural network (ANN), a basic convolutional neural network (CNN), and a transfer learning-based Visual Geometry Group (VGG)16 model. Model performance was assessed using accuracy, sensitivity, specificity, precision, and F1-scores. To interpret model decisions, gradient-weighted class activation mapping (Grad-CAM) was applied to visualize regions of the input images that contributed most to predictions, enabling identification of key prognostic features.
RESULTS: Among the tested models, VGG16 exhibited superior predictive performance, achieving an accuracy of 78.44% for colon cancer and 74.83% for rectal cancer. It showed notably high specificity (89.55% for colon cancer and 87.9% for rectal cancer), indicating strong reliability in correctly identifying patients likely to survive beyond 5 years. Compared to ANN and CNN models, VGG16 achieved a better balance between sensitivity and specificity, demonstrating robustness in the presence of moderate class imbalance within the dataset. Grad-CAM visualization highlighted clinically relevant features (eg, age, gender, smoking history, American Society of Anesthesiologists physical status classification (ASA) grade, liver disease, pulmonary disease, and initial carcinoembryonic antigen [CEA] levels). Conversely, the CNN model yielded lower overall accuracy and low specificity, which limits its immediate applicability in clinical settings.
CONCLUSIONS: The proposed IGHT-based deep learning model, particularly leveraging the VGG16 architecture, demonstrates promising accuracy and interpretability in predicting 5-year overall survival in patients with colorectal cancer. Its capability to effectively stratify patients into risk categories with balanced sensitivity and specificity underscores its potential utility as a clinical decision support system (CDSS) tool. Future studies incorporating external validation with multicenter cohorts and prospective designs are necessary to establish generalizability and clinical integration feasibility.
Author: [‘Oh SH’, ‘Lee Y’, ‘Baek JH’, ‘Sunwoo W’]
Journal: JMIR Med Inform
Citation: Oh SH, et al. Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study. Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study. 2025; 13:e75022. doi: 10.2196/75022