Enterprise AI Analysis
Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma
In pancreatic ductal adenocarcinoma (PDAC), the tumor immune microenvironment (TIME) is central to prognosis and therapeutic response, yet predictive biomarkers are still lacking. Here we used PDAC patients from TCGA, CPTAC and multiple centers to investigate and validate the relationship between the prognosis and tumor-infiltrating neutrophil-to-T-cell ratio (NTR) in the TIME, including the correlation of NTR with chemotherapy response in PDAC. We developed seven artificial intelligence models to evaluate NTR in the TIME, then evaluated their performance. The multi-modal ROI-model, designated PORCELAIN (Pancreatic Cancer Tumor-Infiltrating Neutrophil-to-T-Cell Ratio Evaluation with Artificial Intelligence), achieved superior external validation performance, demonstrating significant stratification of overall survival and recurrence-free survival. The NTR in the TIME is a potential biomarker for PDAC prognosis and treatment stratification. Importantly, PORCELAIN provides a noninvasive approach to assess NTR in the TIME, offering potential value for clinical management.
Executive Impact Summary
Our study identifies the tumor-infiltrating neutrophil-to-T-cell ratio (NTR) as a novel prognostic biomarker in pancreatic ductal adenocarcinoma (PDAC), consistently associated with reduced overall survival, accelerated disease progression, and increased chemotherapy resistance across multiple cohorts.
We developed PORCELAIN, a deep-learning-based multi-modal AI model, which integrates pretreatment CECT features and clinical parameters to noninvasively classify NTR status, demonstrating superior predictive performance in multicenter validation cohorts.
PORCELAIN offers a significant advancement for precision oncology in PDAC by providing clinicians with a decision-support framework to optimize therapeutic strategies, including personalized neoadjuvant regimens for borderline resectable PDAC, addressing the limitations of current invasive TIME assessment methods.
This AI-driven approach has the potential to overcome challenges in PDAC management, such as low surgical resection rates and difficulty in obtaining tissue samples for pathological evaluation, by enabling noninvasive evaluation of NTR status and prediction of clinical outcomes.
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PDAC accounts for ~90% of pancreatic cancers
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Lowest 5-year survival rate among all cancer types
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80%+ patients present with unresectable disease
Deep Analysis & Enterprise Applications
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NTR as a Prognostic Biomarker
0Hazard Ratio for OS (TCGA, Multivariate)
Our study robustly establishes tumor-infiltrating Neutrophil-to-T-cell Ratio (NTR) as a significant prognostic biomarker in PDAC. Elevated NTR is consistently linked to reduced overall survival, accelerated disease progression, and greater chemotherapy resistance across various cohorts, including TCGA and CPTAC, and multicenter validation datasets. This highlights NTR's critical role beyond traditional immune indices like NLR.
Enterprise Process Flow
Pretreatment CECT Imaging
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Clinical Data Integration
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Deep Learning Feature Extraction (EfficientNet-B0)
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Multi-modal Feature Fusion
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NTR Status Prediction (PORCELAIN)
We developed PORCELAIN, a deep-learning-based multi-modal AI model. It integrates pretreatment CECT-derived AI-based radiological features with clinical parameters to noninvasively evaluate tumor-infiltrating NTR. This model achieved superior external validation performance, demonstrating robust prognostic capabilities for PDAC patients. Its innovative architecture is designed to overcome limitations of traditional histopathological TIME assessment.
| Model Variant | AUC (External Validation) | Key Advantages |
|---|---|---|
| Cuboid-model | 0.81 |
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| ROIex-3mm-model | 0.83 |
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| ROI-model (Intratumoral) | 0.85 |
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| Multimodal Cuboid-model | 0.86 |
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| Multimodal ROI-model (PORCELAIN) | 0.90 |
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Our comparative analysis of seven AI models demonstrated the superior performance of the multi-modal ROI-model, designated PORCELAIN. This model leverages precise intratumoral segmentation combined with clinical data, achieving an AUC of 0.90 in external validation. This highlights the importance of focusing on the intrinsic tumor texture for accurate NTR prediction, outperforming models that include broader peritumoral regions due to noise.
Clinical Value & Interpretability
Scenario: A 68-year-old male presents with pancreatic head adenocarcinoma. Pre-treatment CECT and clinical markers (CA19-9, N-stage) are fed into PORCELAIN.
Analysis: PORCELAIN predicts a high NTR status, correlating with a significantly shorter OS and RFS, and lower likelihood of response to standard chemotherapy. SHAP analysis highlights N-stage and CA19-9 as key clinical predictors, while Grad-CAM shows the model focusing on the tumor core and margin in CECT images.
Outcome: Based on PORCELAIN's prediction, the patient's therapeutic strategy is personalized to include a more aggressive neoadjuvant regimen and closer monitoring for recurrence, potentially improving clinical outcomes by preempting chemoresistance. This noninvasive assessment capability of PORCELAIN is crucial in PDAC, where tissue biopsies are often challenging.
Key Learning: PORCELAIN’s ability to predict NTR noninvasively and its transparent interpretability provide a decision-support framework for optimizing therapeutic strategies, particularly for personalized neoadjuvant regimens, addressing critical gaps in PDAC management.
PORCELAIN provides a noninvasive approach to assess NTR in the TIME, offering potential value for clinical management. Its predictions correlate significantly with overall survival, recurrence-free survival, and chemotherapy response. Interpretability features like SHAP and Grad-CAM reveal the driving clinical and imaging features, ensuring transparency and trust in its decision-support capabilities, which is vital for clinical adoption.
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