Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics

Abstract

Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.

Estimation with Uncertainty via Conditional Generative Adversarial Networks

“…Therefore,Ŷ M cG corresponds to one of the prediction results using cGAN; we can use the sample X instead of the feature network if the dimension of the sample space, i.e., p, is low. Such a modification simply changes the input and output in (7). By sampling different noise vectors Z(i), a probability distribution of predictions can be obtained in a similar manner with BNNs as described in (5).…”

“…While outstanding progress has been made in ANNs in recent years [4,5] and ANNs are widely used for many practical applications [6][7][8][9][10], conventional predictive ANN models have an obvious limitation since their estimation corresponds to a point estimate. Such a limitation causes the restrictions of using ANN for medical diagnosis, law problems, and portfolio management, where the risk of the predictions is also essential in practice.…”

Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review

“…Repeating the preceding process, it creates many small images with different individual features from a single H&E slide image. Low-level features (e.g., lines, edge) in an early layer in the neural network are arranged so that deeper layers represent higher-level image features (e.g., motif, object) [ 35 ]. After repeating the convolution and pooling processes, fully connected layers (classification layer) are generated, and through this layer, the output is created.…”

A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

“…Recently, deep learning methods have shown promising results for the prediction of the mutational status from digitized tissue stained with hematoxylin and eosin as whole slide images (WSI) (6)(7)(8)(9)(10)(11)(12). These WSI are already made routinely in the diagnostic workflow and deep learning methods are cheap, always feasible and very scalable.…”