Neural Network Histopathology Model for HPV+ HNSCC Recurrence Prediction
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Solution Overview
Problem
Current treatments for HPV+ HNSCCs often result in life-long disabilities due to high dose radiation and cisplatin use, with inadequate biomarkers to identify recurrence-prone patients, leading to reduced survival and increased morbidity.
Innovation Solution
Development of machine learning-based histopathological recurrence prediction models using a neural network pipeline that incorporates digital pathology data from HPV+ HNSCCs, specifically a deep convolutional neural network (DCNN) trained on annotated digital diagnostic pathology images to predict tumor recurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high dose radiation plus cisplatin therapy is used to treat HPV+ HNSCCs, then oncologic outcomes are improved, but treatment-related morbidity increases due to life-long disabilities
Solution Approach 1:
The machine learning model performs preliminary risk stratification by analyzing histopathological features before treatment escalation decisions are made. By pre-identifying high-risk patients through digital pathology image analysis, the system enables prospective selection of patients who require aggressive therapy, thereby avoiding unnecessary morbidity in low-risk patients while ensuring adequate treatment for those who need it
Solution Approach 2:
The patent replaces traditional clinical biomarkers and pathologist visual assessment with an automated machine learning system based on deep convolutional neural networks. This substitution enables more precise, objective, and scalable risk prediction from digital pathology images, allowing for better differentiation between high-risk and low-risk patients to guide treatment intensity
2Object-affected harmful factors
If radiation dose is decreased or cisplatin is replaced with anti-EGFR therapy to reduce morbidity, then treatment-related morbidity is reduced, but survival is reduced due to lack of adequate biomarkers to identify recurrence-prone patients
Solution Approach 1:
The machine learning model serves as an intermediary tool that bridges the gap between treatment de-escalation goals and patient safety. By providing objective, data-driven risk predictions based on histopathological features, the model enables clinicians to make informed decisions about which patients can safely receive reduced-intensity therapy versus those who require standard or escalated treatment to maintain survival outcomes
3Adaptability or versatility
If traditional biomarkers are used to identify recurrence-prone patients, then treatment decisions can be guided, but predictive accuracy is insufficient leading to inadequate risk stratification
Solution Approach 1:
The patent transforms the basis of risk prediction by shifting from traditional clinical and molecular biomarkers to quantitative features extracted from digital pathology images using machine learning. This parameter transformation enables the system to capture complex histomorphological patterns that are not apparent through conventional assessment, thereby achieving superior predictive accuracy for recurrence risk in HPV+ HNSCC patients
Data Source
AI summary
An example embodiment involves generating tumor image tiles from images of human papillomavirus positive (HPV +) head and neck squamous cell carcinoma (HNSCC) tumors, wherein the tumor image files are respectively labelled with indicators of tumor recurrence. The example embodiment may further involve training a neural network with the tumor image files as labelled. wherein the training results in the neural network learning combinations of histology features characteristic of tumor recurrence. Further steps may involve providing further tumor image tiles to the trained neural network. the neural network generating classifications of the further tumor image tiles based on likelihood of tumor recurrence. and storing the classifications with as respectively associated with the further tumor image tiles.