Hoehn, Julia and Krieghoff-Henning, Eva and Jutzi, Tanja B. and von Kalle, Christof and Utikal, Jochen S. and Meier, Friedegund and Gellrich, Frank F. and Hobelsberger, Sarah and Hauschild, Axel and Schlager, Justin G. and French, Lars and Heinzerling, Lucie and Schlaak, Max and Ghoreschi, Kamran and Hilke, Franz J. and Poch, Gabriela and Kutzner, Heinz and Heppt, Markus and Haferkamp, Sebastian and Sondermann, Wiebke and Schadendorf, Dirk and Schilling, Bastian and Goebeler, Matthias and Hekler, Achim and Froehling, Stefan and Lipka, Daniel B. and Kather, Jakob N. and Krahl, Dieter and Ferrara, Gerardo and Haggenmueller, Sarah and Brinker, Titus J. (2021) Original Research Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification. EUROPEAN JOURNAL OF CANCER, 149. pp. 94-101. ISSN 0959-8049, 1879-0852
Full text not available from this repository. (Request a copy)Abstract
Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. 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. 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. 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%. 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. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | HISTOPATHOLOGIC DIAGNOSIS; LEVEL CLASSIFICATION; CUTANEOUS MELANOMA; DISCORDANCE; DERMATOLOGISTS; RECOGNITION; SYSTEM; Histologic whole slide images; Convolutional neural networks; Data fusion; Patient data; Skin cancer classification |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Dermatologie und Venerologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 27 Sep 2022 13:04 |
| Last Modified: | 27 Sep 2022 13:04 |
| URI: | https://pred.uni-regensburg.de/id/eprint/48087 |
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