Article

ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor Margins

2022-05-10

Abstract excerpt

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Radial sectioning of the resected tumor and surrounding tissue is the most common form of intra-operative and post-operative margin assessment. However, this technique samples only a tiny fraction of the available tissue and therefore may result in incomplete excision...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 5f1aaab5-4b07-56c6-837e-ec32cb149af1
DOI: 10.1101/2022.05.06.22274781

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor MarginsDOI 10.1101/2022.05.06.22274781Read

A clinical-grade universal foundation model for intraoperative pathologySemantic proximity Read

Development and Validation of Machine Learning Models for Predicting 13 or More Sections in Mohs Micrographic SurgerySemantic proximity Read

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor MarginsSemantic proximity Read

Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology ImagesSemantic proximity Read

Image-Based Consensus Molecular Subtyping in Rectal Cancer Biopsies and Response to Neoadjuvant ChemoradiotherapySemantic proximity Read

Aligning computational pathology with clinical practice for colorectal cancerSemantic proximity Read

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics WorkflowsSemantic proximity Read

Artificial intelligence as the next step towards precision pathology.Semantic proximity Read

Molecular margins in head and neck cancer: Current techniques and future directions.Semantic proximity Read

Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosisSemantic proximity Read

Immunotherapy Efficacy Prediction in Cancer: An Artificial Intelligence Approach with Unannotated H&E Whole-Slide ImagesSemantic proximity Read

Spatial biomarker-driven deep learning model via digital pathology predicts response to PI3K inhibitor buparlisib in head and neck squamous cell carcinomaSemantic proximity Read