| Autor: |
Morisi A; Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK., Rai T; Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK., Bacon NJ; Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK.; AURA Veterinary, Guildford GU2 7AJ, UK., Thomas SA; Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK.; National Physical Laboratory, Teddington TW11 0LW, UK., Bober M; Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK., Wells K; Centre for Vision Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK., Dark MJ; Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL 32610, USA., Aboellail T; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523, USA., Bacci B; Department of Veterinary Medical Sciences, University of Bologna, 40126 Bologna, Italy., La Ragione RM; Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK.; Department of Microbial Sciences, School of Biosciences, University of Surrey, Guildford GU2 7XH, UK. |
| Abstrakt: |
The definitive diagnosis of canine soft-tissue sarcomas (STSs) is based on histological assessment of formalin-fixed tissues. Assessment of parameters, such as degree of differentiation, necrosis score and mitotic score, give rise to a final tumour grade, which is important in determining prognosis and subsequent treatment modalities. However, grading discrepancies are reported to occur in human and canine STSs, which can result in complications regarding treatment plans. The introduction of digital pathology has the potential to help improve STS grading via automated determination of the presence and extent of necrosis. The detected necrotic regions can be factored in the grading scheme or excluded before analysing the remaining tissue. Here we describe a method to detect tumour necrosis in histopathological whole-slide images (WSIs) of STSs using machine learning. Annotated areas of necrosis were extracted from WSIs and the patches containing necrotic tissue fed into a pre-trained DenseNet161 convolutional neural network (CNN) for training, testing and validation. The proposed CNN architecture reported favourable results, with an overall validation accuracy of 92.7% for necrosis detection which represents the number of correctly classified data instances over the total number of data instances. The proposed method, when vigorously validated represents a promising tool to assist pathologists in evaluating necrosis in canine STS tumours, by increasing efficiency, accuracy and reducing inter-rater variation. |
