Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study.

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems. Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Citation

Ana Leni Frei, Raphaël Oberson, Elias Baumann, Aurel Perren, Rainer Grobholz, Alessandro Lugli, Heather Dawson, Christian Abbet, Ibai Lertxundi, Stefan Reinhard, Aart Mookhoek, Johann Feichtinger, Rossella Sarro, Gallus Gadient, Corina Dommann-Scherrer, Jessica Barizzi, Sabina Berezowska, Katharina Glatz, Susanne Dertinger, Yara Banz, Rene Schoenegg, Laura Rubbia-Brandt, Achim Fleischmann, Guenter Saile, Pierre Mainil-Varlet, Ruggero Biral, Luca Giudici, Alex Soltermann, Audrey Baur Chaubert, Sylvia Stadlmann, Joachim Diebold, Kristof Egervari, Charles Bénière, Francesca Saro, Andrew Janowczyk, Inti Zlobec. Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 2023 Dec;36(12):100335

Mesh Tags

PMID: 37742926

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