# Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

arXiv: https://arxiv.org/abs/2303.13610 · HTML: https://mlsanity.com/paper/2303.13610/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence](https://mlsanity.com/paper/2108.03555/) (2021)

## Built on by (most cited first, 7 of 7)

- [Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2404.06114/) (2024)
- [A self-supervised framework for learning whole slide representations](https://mlsanity.com/paper/2402.06188/) (2024)
- [Step-Calibrated Diffusion for Biomedical Optical Image Restoration](https://mlsanity.com/paper/2403.13680/) (2024)
- [Super-resolution of biomedical volumes with 2D supervision](https://mlsanity.com/paper/2404.09425/) (2024)
- [Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification](https://mlsanity.com/paper/2501.11014/) (2025)
- [Joint Modelling Histology and Molecular Markers for Cancer Classification](https://mlsanity.com/paper/2502.07979/) (2025)
- [Intelligent Histology for Tumor Neurosurgery](https://mlsanity.com/paper/2507.03037/) (2025)

## Abstract (arXiv)

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (< 90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n=153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.