Back to Search
Start Over
Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.
- Source :
-
European journal of cancer (Oxford, England : 1990) [Eur J Cancer] 2021 May; Vol. 149, pp. 94-101. Date of Electronic Publication: 2021 Apr 07. - Publication Year :
- 2021
-
Abstract
- Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.<br />Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone.<br />Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier.<br />Results: The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%.<br />Conclusion: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.<br />Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Haferkamp S. reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Novartis, Roche, BMS, Amgen and MSD outside the submitted work. Hauschild A. reports clinical trial support, speaker's honoraria or consultancy fees from the following companies: Amgen, BMS, Merck Serono, MSD, Novartis, Oncosec, Philogen, Pierre Fabre, Provectus, Regeneron, Roche, OncoSec, Sanofi-Genzyme and Sun Pharma, outside the submitted work. BS reports advisory roles for or has received honoraria from Pierre Fabre Pharmaceuticals, Incyte, Novartis, Roche, BMS and MSD, research funding from BMS, Pierre Fabre Pharmaceuticals and MSD, and travel support from Novartis, Roche, BMS, Pierre Fabre Pharmaceuticals and Amgen, outside the submitted work. JSU is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, outside the submitted work. WS received travel expenses for attending meetings and/or (speaker) honoraria from Abbvie, Almirall, Bristol-Myers Squibb, Celgene, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme and UCB outside the submitted work. FM has received travel support or/and speaker's fees or/and advisor's honoraria by Novartis, Roche, BMS, MSD and Pierre Fabre and research funding from Novartis and Roche. TJB reports owning a company that develops mobile applications (Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
- Subjects :
- Adult
Age Factors
Aged
Databases, Factual
Female
Germany
Humans
Male
Melanoma classification
Middle Aged
Nevus classification
Predictive Value of Tests
Reproducibility of Results
Retrospective Studies
Sex Factors
Skin Neoplasms classification
Image Interpretation, Computer-Assisted
Melanoma pathology
Microscopy
Neural Networks, Computer
Nevus pathology
Skin Neoplasms pathology
Subjects
Details
- Language :
- English
- ISSN :
- 1879-0852
- Volume :
- 149
- Database :
- MEDLINE
- Journal :
- European journal of cancer (Oxford, England : 1990)
- Publication Type :
- Academic Journal
- Accession number :
- 33838393
- Full Text :
- https://doi.org/10.1016/j.ejca.2021.02.032