NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification

📰 ArXiv cs.AI

NeoNet is a deep learning framework for non-invasive prediction of perineural invasion via 3D MRI-based generation-driven classification

advanced Published 1 Apr 2026

Action Steps

Collect and preprocess 3D MRI scans of patients with suspected perineural invasion
Develop and train NeoNet using generation-driven classification to predict PNI
Evaluate the performance of NeoNet using metrics such as accuracy, sensitivity, and specificity
Integrate NeoNet into clinical workflows for non-invasive diagnosis and treatment planning

Who Needs to Know This

This research benefits radiologists, oncologists, and medical imaging analysts who can utilize NeoNet for accurate diagnosis and treatment planning, and software engineers who can implement and integrate the framework into clinical workflows

Key Insight

💡 NeoNet enables accurate prediction of perineural invasion without the need for invasive diagnostic procedures