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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.

Cheng-Jiang Wei^1,2, Yan Tang³, Yang-Bai Sun⁴

¹Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

NPJ Digital Medicine

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Summary

This study developed an AI model for early screening of malignant peripheral nerve sheath tumors (MPNSTs) in neurofibromatosis type 1 patients. The model achieved high accuracy, showing potential for whole-body tumor screening.

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Area of Science:

Artificial Intelligence in Oncology
Medical Imaging Analysis
Oncology

Background:

Deep learning shows promise in lesion differentiation but often overlooks complex whole-body backgrounds.
Neurofibromatosis type 1 (NF1) presents challenges due to heterogeneous backgrounds for tumor detection.
Early screening of malignant peripheral nerve sheath tumors (MPNSTs) is crucial.

Purpose of the Study:

To develop accurate MRI-based deep learning models for early automated screening of MPNSTs in NF1 patients.
To address the challenge of complex whole-body backgrounds in tumor detection.
To create a privacy-conscious, lightweight model suitable for clinical deployment.

Main Methods:

A one-step deep learning model incorporating normal tissue/organ labels for contextual information was developed.
Data from 347 subjects across a Chinese seven-center cohort were analyzed.
A lightweight deep neural network architecture was utilized for privacy and hospital deployment.

Main Results:

The model achieved 85.71% accuracy for MPNST diagnosis in the validation cohort.
The model demonstrated 84.75% accuracy in an independent test set.
The developed model outperformed a classic two-step model in accuracy.

Conclusions:

The developed AI model shows high accuracy for MPNST screening in NF1 patients against complex backgrounds.
The approach of incorporating contextual information is effective for tumors with complex backgrounds.
This AI model has potential for screening other whole-body primary and metastatic tumors.