Literature DB >> 30640753

A Multi-Institutional Study to Evaluate Automated Whole Slide Scoring of Immunohistochemistry for Assessment of Programmed Death-Ligand 1 (PD-L1) Expression in Non-Small Cell Lung Cancer.

Clive R Taylor1, Anagha P Jadhav2, Abhi Gholap3, Gurunath Kamble2, Jiaoti Huang4, Allen Gown5, Isha Doshi3, David L Rimm6.   

Abstract

Assessment of programmed death-ligand 1 (PD-L1) expression is a critical part of patient management for immunotherapy. However, studies have shown that pathologist-based analysis lacks reproducibility, especially for immune cell expression. The purpose of this study was to validate reproducibility of the automated machine-based Optra image analysis for PD-L1 immunohistochemistry for both tumor cells (TCs) and immune cells. We compared conventional pathologists' scores for both tumor and immune cell positivity separately using 22c3 antibody on the Dako Link 48 platform for PD-L1 expression in non-small cell lung carcinoma. We assessed interpretation first by pathologists and second by PD-L1 image analysis scores. Lin's concordance correlation coefficients (LCCs) for each pathologist were measured to assess variability between pathologists and between pathologists and Optra automated quantitative scores in scoring both tumor and immune cells. Lin's LCCs to evaluate the correlation between pathologists for TC was 0.75 [95% confidence interval (CI), 0.64-0.81] and 0.40 (95% CI, 0.40-0.62) for immune cell scoring. Pathologists were highly concordant for tumor scoring, but not for immune cell scoring, which is similar to previously reported studies where agreement is higher in TCs than immune cells. The LCCs between conventional pathologists' read and the machine score were 0.80 (95% CI, 0.74-0.85) for TCs and 0.70 (95% CI, 0.60-0.76) for immune cell population. This is considered excellent agreement for TCs and good concordance for immune cells. The automated scoring methods showed concordance with the pathologists' average scores that were comparable to interpathologist scores. This suggests promise for Optra automated assessment of PD-L1 in non-small cell lung cancer.

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Year:  2019        PMID: 30640753     DOI: 10.1097/PAI.0000000000000737

Source DB:  PubMed          Journal:  Appl Immunohistochem Mol Morphol        ISSN: 1533-4058


  8 in total

Review 1.  Development and applications of computer image analysis algorithms for scoring of PD-L1 immunohistochemistry.

Authors:  L J Inge; E Dennis
Journal:  Immunooncol Technol       Date:  2020-05-11

2.  Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer.

Authors:  Xiangyun Wang; Peilin Chen; Guangtai Ding; Yishi Xing; Rongrong Tang; Chaolong Peng; Yizhou Ye; Qiang Fu
Journal:  Medicine (Baltimore)       Date:  2021-05-21       Impact factor: 1.817

3.  Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

Authors:  Liesbeth M Hondelink; Melek Hüyük; Pieter E Postmus; Vincent T H B M Smit; Sami Blom; Jan H von der Thüsen; Danielle Cohen
Journal:  Histopathology       Date:  2021-11-16       Impact factor: 7.778

4.  Automated tumor proportion scoring for PD-L1 expression based on multistage ensemble strategy in non-small cell lung cancer.

Authors:  Boju Pan; Yuxin Kang; Yan Jin; Lin Yang; Yushuang Zheng; Lei Cui; Jian Sun; Jun Feng; Yuan Li; Lingchuan Guo; Zhiyong Liang
Journal:  J Transl Med       Date:  2021-06-07       Impact factor: 5.531

5.  Digital Slide Assessment for Programmed Death-Ligand 1 Combined Positive Score in Head and Neck Squamous Carcinoma: Focus on Validation and Vision.

Authors:  Albino Eccher; Ilaria Girolami; Giancarlo Troncone; Liron Pantanowitz
Journal:  Front Artif Intell       Date:  2021-06-04

6.  Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.

Authors:  Haydee Lara; Zaibo Li; Esther Abels; Famke Aeffner; Marilyn M Bui; Ehab A ElGabry; Cleopatra Kozlowski; Michael C Montalto; Anil V Parwani; Mark D Zarella; Douglas Bowman; David Rimm; Liron Pantanowitz
Journal:  Appl Immunohistochem Mol Morphol       Date:  2021-08-01

7.  Improving the Diagnostic Accuracy of the PD-L1 Test with Image Analysis and Multiplex Hybridization.

Authors:  Matthew P Humphries; Victoria Bingham; Fatima Abdullahi Sidi; Stephanie G Craig; Stephen McQuaid; Jacqueline James; Manuel Salto-Tellez
Journal:  Cancers (Basel)       Date:  2020-04-29       Impact factor: 6.575

8.  Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software.

Authors:  Julia R Naso; Tetiana Povshedna; Gang Wang; Norbert Banyi; Calum MacAulay; Diana N Ionescu; Chen Zhou
Journal:  Pathol Oncol Res       Date:  2021-03-26       Impact factor: 3.201

  8 in total

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