Deep learning for necrosis detection using canine perivascular wall tumour whole slide images.

Autor: Rai T; Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK. t.rai@surrey.ac.uk., Morisi A; School of Veterinary Medicine, University of Surrey, Guildford, GU2 7AL, UK., Bacci B; Department of Veterinary Medical Sciences, University of Bologna, 40126, Bologna, Italy., Bacon NJ; Fitzpatrick Referrals Oncology and Soft Tissue, Guildford, UK., Dark MJ; Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL, USA., Aboellail T; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA., Thomas SA; National Physical Laboratory, London, TW11 0LW, UK.; Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK., Bober M; Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK., La Ragione R; School of Veterinary Medicine, University of Surrey, Guildford, GU2 7AL, UK.; School of Biosciences and Medicine, University of Surrey, Guildford, GU2 7XH, UK., Wells K; Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Jun 23; Vol. 12 (1), pp. 10634. Date of Electronic Publication: 2022 Jun 23.
DOI: 10.1038/s41598-022-13928-1
Abstrakt: Necrosis seen in histopathology Whole Slide Images is a major criterion that contributes towards scoring tumour grade which then determines treatment options. However conventional manual assessment suffers from inter-operator reproducibility impacting grading precision. To address this, automatic necrosis detection using AI may be used to assess necrosis for final scoring that contributes towards the final clinical grade. Using deep learning AI, we describe a novel approach for automating necrosis detection in Whole Slide Images, tested on a canine Soft Tissue Sarcoma (cSTS) data set consisting of canine Perivascular Wall Tumours (cPWTs). A patch-based deep learning approach was developed where different variations of training a DenseNet-161 Convolutional Neural Network architecture were investigated as well as a stacking ensemble. An optimised DenseNet-161 with post-processing produced a hold-out test F1-score of 0.708 demonstrating state-of-the-art performance. This represents a novel first-time automated necrosis detection method in the cSTS domain as well specifically in detecting necrosis in cPWTs demonstrating a significant step forward in reproducible and reliable necrosis assessment for improving the precision of tumour grading.
(© 2022. The Author(s).)
Databáze: MEDLINE
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