Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study

Author:

Wies ChristophORCID,Schneider Lucas,Haggenmüller Sarah,Bucher Tabea-ClaraORCID,Hobelsberger Sarah,Heppt Markus V.,Ferrara GerardoORCID,Krieghoff-Henning Eva I.,Brinker Titus J.ORCID

Abstract

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.

Funder

Federal Ministry of Health, Berlin, Germany

Ministry of Social Affairs, Health and Integration of the Federal State Baden-Württemberg, Germany

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference35 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries;H Sung;CA Cancer J Clin,2021

2. Epidemiology of Melanoma;K Saginala;Med Sci,2021

3. Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study;JG Elmore;BMJ,2017

4. Pathology review significantly affects diagnosis and treatment of melanoma patients: an analysis of 5011 patients treated at a melanoma treatment center;MG Niebling;Ann Surg Oncol,2014

5. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification;J Höhn;Eur J Cancer Oxf Engl 1990,2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3