Enterprise AI Analysis: Clinical-grade AI model for molecular subtyping of endometrial cancer: a multi-center cohort study in China
Transforming Endometrial Cancer Diagnostics with AI
Our deep learning pipeline directly infers EC molecular subtypes from H&E WSIs, offering automated, interpretable, and cost-efficient alternatives to conventional molecular testing. This multi-center study validates its robust performance across diverse clinical settings in China.
Executive Impact: Revolutionizing Cancer Care Efficiency
Leveraging AI for molecular subtyping significantly reduces costs and turnaround times, empowering precision oncology and fertility-preserving management.
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Classification Accuracy (POLEmut & MMRd)
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Survival Prediction Correlation
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MAE Survival Prediction
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External Validation Accuracy
Deep Analysis & Enterprise Applications
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Overview
Methodology
Classification Performance
Survival Prediction
External Validation
Endometrial cancer (EC) is the second most common malignancy in women, with increasing incidence and a shift towards earlier onset, necessitating accurate and individualized treatment. Traditional molecular subtyping methods (Sanger sequencing, IHC) are costly, time-intensive, and not widely scalable. This study addresses these challenges by developing a deep-learning pipeline for EC molecular subtyping directly from hematoxylin-and-eosin (H&E) whole-slide images (WSIs).
Enterprise Process Flow
WSI Digitization
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Super-resolution Enhancement (SRResGAN)
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Lesion Segmentation (MedSAM)
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Molecular Subtype Classification (ResNet-101)
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Survival Modeling (LSTM)
92%
Accuracy for POLEmut and MMRd Subtypes
Achieving Clinical-Grade Accuracy
The ResNet-101 model achieved high classification accuracies, reaching 92% for POLEmut and MMRd, 91% for p53abn, and 90% for NSMP. ROC analysis confirmed strong discriminative power with a micro-average AUC of 0.9838, demonstrating robust performance across all subtypes. Grad-CAM visualizations confirmed the model’s ability to identify subtype-specific histological features.
R²=0.9692
Strong Correlation in Survival Prediction
Predicting Patient Outcomes
The model demonstrated robust performance in estimating survival outcomes, with a strong linear association between predicted and observed survival (Pearson, Spearman, and Kendall correlation coefficients of 0.9850, 0.9858, and 0.9036, respectively). The coefficient of determination (R²) reached 0.9692, and the mean absolute error (MAE) was 123 days. This capability supports prognostic stratification and personalized treatment planning.
93%
Accuracy in PMCHH External Cohort
Robust Generalizability Across Institutions
The ResNet-101 model showed robust classification performance across two independent external validation cohorts: AUC of 0.97 (accuracy 92%) in OGHFU (N=83) and AUC of 0.94 (accuracy 93%) in PMCHH (N=35). This confirms the model's generalizability and potential applicability in diverse clinical settings, despite inter-institutional variations in slide preparation and staining protocols.
Calculate Your Potential AI Impact
Estimate the time and cost savings your enterprise could achieve by integrating AI for pathology analysis.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A typical enterprise AI integration follows a structured approach to ensure maximum impact and seamless adoption.
Phase 1: Discovery & Strategy (2-4 Weeks)
Initial consultations to define objectives, assess existing infrastructure, and identify key integration points for molecular pathology AI. Deliverable: Detailed AI Strategy Blueprint.
Phase 2: Data Preparation & Model Adaptation (6-12 Weeks)
Curate and prepare your historical pathology data (WSIs, molecular results) for training. Adapt and fine-tune our models to your specific clinical workflows and data characteristics. Deliverable: Custom-trained AI Model.
Phase 3: Integration & Pilot Deployment (4-8 Weeks)
Integrate the AI pipeline into your existing LIS/PACS. Conduct a pilot program with a subset of cases to validate real-world performance and gather user feedback. Deliverable: Integrated AI System & Pilot Report.
Phase 4: Full-Scale Rollout & Optimization (Ongoing)
Expand AI deployment across your pathology department. Provide continuous monitoring, performance optimization, and ongoing support to ensure sustained value. Deliverable: Enhanced Clinical Workflow & Ongoing Support.
Ready to Transform Your Pathology Department?
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