Head and Neck Tumor Segmentation for Personalized Survival Prediction
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Solution Overview
Problem
Current methods for predicting the progression-free survival of head and neck cancer, particularly oropharyngeal cancer, are not sufficiently accurate due to reliance on population-based statistics and lack of integration of patient-specific data from imaging and electronic health records, leading to suboptimal treatment planning.
Innovation Solution
A hybrid machine learning approach combining multi-task logistic regression (MTLR) and multi-layer artificial neural networks processes both imaging data from CT and PET scans and electronic health records to provide a personalized prognosis score for head and neck cancer, specifically for oropharyngeal cancer, without prior knowledge of tumor location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for prognosis prediction, then the model structure is simple, but the accuracy of PFS prediction is insufficient
Solution Approach 1:
The patent merges multiple data modalities (imaging data from CT/PET scans and tabular EHR data) into a unified deep learning framework. The hybrid architecture combines CNN for image processing with fully connected layers for tabular data, integrating both data types through feature extraction and fusion mechanisms to achieve superior PFS prediction accuracy compared to traditional single-modality models
Solution Approach 2:
The patent transitions from traditional two-dimensional image processing to three-dimensional volumetric analysis by processing CT and PET scan volumes directly through 3D convolutional networks. This dimensional enhancement allows the model to capture spatial relationships and volumetric features that improve prognosis prediction accuracy
2Measurement precision
If existing machine learning methods are used, then the data processing is straightforward, but nonlinear elements in data are not captured
Solution Approach 1:
The patent replaces traditional linear statistical methods with deep neural networks that automatically learn nonlinear transformations. The deep learning architecture uses activation functions, convolutional layers, and pooling operations to capture complex nonlinear patterns in medical imaging and clinical data without requiring manual feature engineering
Solution Approach 2:
The patent employs data augmentation techniques that transform input images through various parameter changes (rotation, flipping, intensity adjustments) to create diverse training samples. This helps the model generalize better and capture nonlinear relationships in the data while maintaining robustness
3Adaptability or versatility
If tumor location information is required for prognosis prediction, then the prediction accuracy can be improved, but the applicability is limited
Solution Approach 1:
The patent performs automatic tumor segmentation and location detection as a preliminary step within the deep learning framework. The model first processes the CT/PET images to identify and segment tumor regions, extracting spatial and volumetric features that are then used for prognosis prediction, eliminating the need for manual segmentation while maintaining accuracy
Solution Approach 2:
The patent creates a universal deep learning framework that handles multiple tasks: image processing, tumor segmentation, feature extraction, and prognosis prediction. This multi-functional approach allows the same system to operate without requiring separate tools for each function, improving both accuracy and applicability
Data Source
AI summary
A system, computer-readable storage medium and method for prognosis of head and neck cancer, includes an input for receiving electronic health records (EHR) of a patient, an input for receiving multimodal images of a head and neck area of the patient, a feature extraction module for converting the electronic health records and multimodal images into at least one feature vector, a hybrid machine learning architecture that includes a multi-task logistic regression (MTLR) model and a multi-layer artificial neural network, the hybrid architecture takes as input the at least one feature vector and outputs a final risk score of prognosis for head and neck cancer for the patient.