Literature DB >> 27349303

Validation of an electronic program for pathologist training in the interpretation of a complex companion diagnostic immunohistochemical assay.

Eslie Dennis1, Peter Banks2, Lauren B Murata2, Stephanie A Sanchez2, Christie Pennington2, Linda Hockersmith2, Rachel Miller2, Jess Lambe2, Janine Feng2, Monesh Kapadia2, June Clements2, Isabell Loftin2, Shalini Singh2, Ashis Das-Gupta3, William Lloyd4, Kenneth Bloom5.   

Abstract

Companion diagnostics assay interpretation can select patients with the greatest targeted therapy benefits. We present the results from a prospective study demonstrating that pathologists can effectively learn immunohistochemical assay-interpretation skills from digital image-based electronic training (e-training). In this study, e-training was used to train board-certified pathologists to evaluate non-small cell lung carcinoma for eligibility for treatment with onartuzumab, a MET-inhibiting agent. The training program mimicked the live training that was previously validated in clinical trials for onartuzumab. A digital interface was developed for pathologists to review high-resolution, static images of stained slides. Sixty-four pathologists practicing in the United States enrolled while blinded to the type of training. After training, both groups completed a mandatory final test using glass slides. The results indicated both training modalities to be effective. Overall, 80.6% of e-trainees and 72.7% of live trainees achieved passing scores (at least 85%) on the final test. All study participants reported that their training experience was "good" and that they had received sufficient information to determine the adequacy of case slide staining to score each case. This study established that an e-training program conducted under highly controlled conditions can provide pathologists with the skills necessary to interpret a complex assay and that these skills can be equivalent to those achieved with face-to-face training using conventional microscopy. Programs of this type are scalable for global distribution and offer pathologists the potential for readily accessible and robust training in new companion diagnostic assays linked to novel, targeted, adjuvant therapies for cancer patients.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Companion diagnostics; Digital images; E-learning; Electronic training; Proficiency testing; Virtual pathology

Mesh:

Substances:

Year:  2016        PMID: 27349303     DOI: 10.1016/j.humpath.2016.06.013

Source DB:  PubMed          Journal:  Hum Pathol        ISSN: 0046-8177            Impact factor:   3.466


  4 in total

1.  WASH Benefits Bangladesh trial: management structure for achieving high coverage in an efficacy trial.

Authors:  Leanne Unicomb; Farzana Begum; Elli Leontsini; Mahbubur Rahman; Sania Ashraf; Abu Mohd Naser; Fosiul A Nizame; Kaniz Jannat; Faruqe Hussain; Sarker Masud Parvez; Shaila Arman; Moshammot Mobashara; Stephen P Luby; Peter J Winch
Journal:  Trials       Date:  2018-07-06       Impact factor: 2.279

2.  Quality assurance guidance for scoring and reporting for pathologists and laboratories undertaking clinical trial work.

Authors:  Max Robinson; Jacqueline James; Gareth Thomas; Nicholas West; Louise Jones; Jessica Lee; Karin Oien; Alex Freeman; Clare Craig; Philip Sloan; Philip Elliot; Maggie Cheang; Manuel Rodriguez-Justo; Clare Verrill
Journal:  J Pathol Clin Res       Date:  2018-11-29

3.  Programmed Cell Death Ligand 1 Pathologist Training in the Time of COVID-19: Our Experience using a Digital Solution.

Authors:  Dorothy Hayden; Joseph M Herndon; James C Campion; Janine D Feng; Fangru Lian; Jessica L Baumann; Bryan K Roland; Ehab A ElGabry
Journal:  J Pathol Inform       Date:  2021-11-22

4.  Determining PD-L1 Status in Patients With Triple-Negative Breast Cancer: Lessons Learned From IMpassion130.

Authors:  Sunil S Badve; Frédérique Penault-Llorca; Jorge S Reis-Filho; Regula Deurloo; Kalliopi P Siziopikou; Corrado D'Arrigo; Giuseppe Viale
Journal:  J Natl Cancer Inst       Date:  2022-05-09       Impact factor: 11.816

  4 in total

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