Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype

Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. Histomorphology is a crucial component of the integrated diagnosis of GBM-IDHwt. Artificial intelligence (AI) methods have shown promise to extract additional prognostic information from histological whole-slide images (WSI) of hematoxylin and eosin-stained glioblastoma tissue. Here, we present an explainable AI-based method to support systematic interpretation of histomorphological features associated with survival. It combines an explainable multiple instance learning (MIL) architecture with a sparse autoencoder (SAE) to relate human-interpretable visual patterns of tissue to survival. The MIL architecture directly identifies prognosis-relevant image tiles and the SAE maps these tiles post-hoc to visual patterns. The MIL method was trained and evaluated using a new real-world dataset that comprised 720 GBM-IDHwt cases from three hospitals and four cancer registries in Germany. The SAE was trained using 1878 WSIs of glioblastoma from five independent public data collections. Despite the many factors influencing survival time, our method showed some ability to discriminate between patients living less than 180 days or more than 360 days solely based on histomorphology (AUC: 0.67; 95% CI: 0.63-0.72). Cox proportional hazards regression confirmed a significant difference in survival time between the predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Our method identified multiple interpretable visual patterns associated with survival. Three neuropathologists separately found that 21 of the 24 most strongly associated patterns could be clearly attributed to seven histomorphological categories. Necrosis and hemorrhage appeared to be associated with shorter survival while highly cellular tumor areas were associated with longer survival.

Automatic Extraction of Rules for Generating Synthetic Patient Data From Real-World Population Data Using Glioblastoma as an Example

The generation of synthetic data is a promising technology to make medical data available for secondary use in a privacy-compliant manner. A popular method for creating realistic patient data is the rule-based Synthea data generator. Synthea generates data based on rules describing the lifetime of a synthetic patient. These rules typically express the probability of a condition occurring, such as a disease, depending on factors like age. Since they only contain statistical information, rules usually have no specific data protection requirements. However, creating meaningful rules can be a very complex process that requires expert knowledge and realistic sample data. In this paper, we introduce and evaluate an approach to automatically generate Synthea rules based on statistics from tabular data, which we extracted from cancer reports. As an example use case, we created a Synthea module for glioblastoma from a real-world dataset and used it to generate a synthetic dataset. Compared to the original dataset, the synthetic data reproduced known disease courses and mostly retained the statistical properties. Overall, synthetic patient data holds great potential for privacy-preserving research. The data can be used to formulate hypotheses and to develop prototypes, but medical interpretation should consider the specific limitations as with any currently available approach.

Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review

In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 70 publicly available research studies that have proposed AI-based methods for whole-slide histopathology images of human gliomas, covering the diagnostic tasks of subtyping (16/70), grading (23/70), molecular marker prediction (13/70), and survival prediction (27/70). All studies were reviewed with regard to methodological aspects as well as clinical applicability. It was found that the focus of current research is the assessment of hematoxylin and eosin-stained tissue sections of adult-type diffuse gliomas. The majority of studies (49/70) are based on the publicly available glioblastoma and low-grade glioma datasets from The Cancer Genome Atlas (TCGA) and only a few studies employed other datasets in isolation (10/70) or in addition to the TCGA datasets (11/70). Current approaches mostly rely on convolutional neural networks (53/70) for analyzing tissue at 20x magnification (30/70). A new field of research is the integration of clinical data, omics data, or magnetic resonance imaging (27/70). So far, AI-based methods have achieved promising results, but are not yet used in real clinical settings. Future work should focus on the independent validation of methods on larger, multi-site datasets with high-quality and up-to-date clinical and molecular pathology annotations to demonstrate routine applicability.