Automating H&E image-based detection and prognostic evaluation of breast cancers hold promise.

Authors: Satabhisa Mukhopadhyay

Medical Laboratory Observer

This is an invited peer-reviewed by-lined scientific review article (published online on September 21, 23 and in the hardcopy on October 2, 23)

Targeting the Forgotten How to empower patients in histopathology diagnostics as we navigate the emergent digital era.

Authors: Angelene Berwick, Graham Holland, Bradford Power, Nicolas M. Orsi

The Pathologist

Prediction of disease recurrence in low risk Oncotype Dx breast cancers from digital H&E-stained whole slide images of pre-treatment resections alone.

Authors: Satabhisa Mukhopadhyay, Tathagata Dasgupta, Elizabeth Walsh, Rebecca Millican-Slater, Andrew Hanby, Joanne Stephenson, Craig A. Bunnell, Nicolas M. Orsi

SABCS 2022

Attrition Rate and Case Completeness for Identifying Invasive Breast Carcinoma from Digitized Whole Slide Images in a Real-World Setting.

Authors: Nicolas Orsi, Satabhisa Mukhopadhyay, Tathagata Dasgupta, Elizabeth Walsh, Angelene Berwick, Matthew Hanna, Rajendra Singh

Automated, Ancillary Test-Free BRAF/NRAS Mutational Status Prediction from Digitized Whole Slide Images of H&E-Stained Malignant Melanomas.

Authors: Satabhisa Mukhopadhyay, Angelene Berwick, Elizabeth Walsh, Tathagata Dasgupta, Nick Bennett, Nicolas M. Orsi

Automated Robust Image Analysis Qualifying H&E Staining Adequacy with Varying Hematoxylin and Eosin Incubation Times.

Authors: Satabhisa Mukhopadhyay, Tathagata Dasgupta, Christopher Higgins, Alec DeGrand, Lisa M. Williams, Nicolas M. Orsi, Judy Lapetino, Erico von Bueren, Sakura Finetek