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

• URL: http://arxiv.org/abs/2303.13610v1
• Date: Thu, 23 Mar 2023 18:50:18 GMT
• Title: Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
• Authors: Todd C. Hollon, Cheng Jiang, Asadur Chowdury, Mustafa Nasir-Moin, Akhil Kondepudi, Alexander Aabedi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, Pedro Lowenstein, Maria Castro, Lisa Irina Wadiura, Georg Widhalm, Volker Neuschmelting, David Reinecke, Niklas von Spreckelsen, Mitchel S. Berger, Shawn L. Hervey-Jumper, John G. Golfinos, Matija Snuderl, Sandra Camelo-Piragua, Christian Freudiger, Honglak Lee, Daniel A. Orringer
• Abstract summary: DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
• Score: 59.79875531898648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $93.3\pm 1.6\%$. 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.

Related papers

• Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection [3.1850395068284785]
  Oral cancer is treatable if detected early, but it is often fatal in late stages. Computer-assisted methods are essential for cost-effective and accurate cytological analysis. This study aims to improve AI-based oral cancer detection using multimodal imaging and deep fusion.
  arXiv  Detail & Related papers  (2024-07-02T01:05:35Z)
• Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma [9.082753496844731]
  We propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. We also propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma.
  arXiv  Detail & Related papers  (2023-03-26T23:00:00Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens [5.486402205751873]
  A radiomics approach was extended to optical fluorescence molecular imaging data for tissue classification, termed 'optomics' Optomics seek to improve tumor identification by probing textural pattern differences in EGFR expression conveyed by fluorescence. The improved performance demonstrates that extending the radiomics approach to fluorescence molecular imaging data offers a promising image analysis technique for cancer detection in fluorescence-guided surgery.
  arXiv  Detail & Related papers  (2022-08-29T00:06:25Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology [26.194247664756553]
  Intraoperative diagnosis of skull base tumors can be challenging due to tumor diversity and lack of intraoperative pathology resources. We developed an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses.
  arXiv  Detail & Related papers  (2021-08-08T02:49:29Z)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN [59.60954255038335]
  The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators. Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
  arXiv  Detail & Related papers  (2020-06-26T02:50:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.