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

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

The use of digital pathology and machine learning for the detection and characterisation of canine soft tissue sarcomas

Digital pathology (DP) and whole slide images (WSIs) are rapidly becoming the first option for routine diagnostics. Successful application of artificial intelligence (AI) methods such as machine learning algorithms (ML) to WSIs have the potential to create new supportive diagnostic clinical tools that can improve diagnostic accuracy, reproducibility and objectivity and provide new insights into human and canine cancer.This thesis focused on the detection and characterisation of canine soft tissue sarcomas (cSTSs) using ML. Currently, the diagnosis of cSTSs is based on histological assessment. In particular, by assessing certain histological features such as the degree of differentiation, necrosis score and mitotic score, it is possible to define a final tumour grade, which aids in prognostication for patients. Due to the subjectivity of the scoring system, grade disagreements are reported in human and cSTS cases. These results were confirmed in our study where we found only a fair level (κ = 0.36) of diagnostic concordance between pathologists in grading these tumours illustrating the need for automated image analysis tools. In order to achieve this goal, the first essential step was to create an appropriate and comprehensive digital slide dataset. The study gathered a large-scale dataset of 1166 histopathological WSIs of cSTSs (n=752), canine mast cell tumours (MCTs) (n=359) and canine apocrine gland anal sac adenocarcinoma (AGASACAs) (n=55) with related clinical information. Once the slide panel was assembled, the study focused on providing a tool for automatic detection of tumour necrosis and mitosis in annotated histopathological WSI of STS using ML. The ML algorithm applied (DenseNet161) had an accuracy for necrosis and mitosis detection of 92.7% and 50.0%, respectively. In conclusion, the results presented here have demonstrated that digital pathology and ML algorithms can potentially be used as a diagnostic support tool for the detection and characterisation of cSTSs

Detection of Necrosis in Digitised Whole-Slide Images for Better Grading of Canine Soft-Tissue Sarcomas Using Machine-Learning

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