Literature DB >> 34375608

Multiplex computational pathology for treatment response predication.

Ming Y Lu1, Houssein A Sater2, Faisal Mahmood3.   

Abstract

Recently published in Science, AstroPath outlines a standardized workflow for multiplex immunofluorescence (mIF) panel development, imaging, and analysis; showcases its potential in biomarker discovery for predicting response to anti-PD-1 treatment; and paves the way for large-scale computational pathology studies on high-quality mIF datasets using data-driven machine-learning techniques.
Copyright © 2021 Elsevier Inc. All rights reserved.

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Year:  2021        PMID： 34375608     DOI： 10.1016/j.ccell.2021.07.014

Source DB:  PubMed          Journal:  Cancer Cell        ISSN： 1535-6108            Impact factor:   31.743


  3 in total

1.  Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Authors:  Yanan Wang; Yu Guang Wang; Changyuan Hu; Ming Li; Yanan Fan; Nina Otter; Ikuan Sam; Hongquan Gou; Yiqun Hu; Terry Kwok; John Zalcberg; Alex Boussioutas; Roger J Daly; Guido Montúfar; Pietro Liò; Dakang Xu; Geoffrey I Webb; Jiangning Song
Journal:  NPJ Precis Oncol       Date:  2022-06-23

Review 2.  Multiplexed In Situ Spatial Protein Profiling in the Pursuit of Precision Immuno-Oncology for Patients with Breast Cancer.

Authors:  Davide Massa; Anna Tosi; Antonio Rosato; Valentina Guarneri; Maria Vittoria Dieci
Journal:  Cancers (Basel)       Date:  2022-10-06       Impact factor: 6.575

3.  Histology segmentation using active learning on regions of interest in oral cavity squamous cell carcinoma.

Authors:  Jonathan Folmsbee; Lei Zhang; Xulei Lu; Jawaria Rahman; John Gentry; Brendan Conn; Marilena Vered; Paromita Roy; Ruta Gupta; Diana Lin; Shabnam Samankan; Pooja Dhorajiva; Anu Peter; Minhua Wang; Anna Israel; Margaret Brandwein-Weber; Scott Doyle
Journal:  J Pathol Inform       Date:  2022-09-27
  3 in total

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