Article ID Journal Published Year Pages File Type
4132393 Human Pathology 2016 10 Pages PDF
Abstract

SummaryCompanion 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.

Related Topics
Health Sciences Medicine and Dentistry Pathology and Medical Technology
Authors
, , , , , , , , , , , , , , ,