Deep learning for H&E-based meningioma molecular classification and outcome prediction: a retrospective cohort study

The introduction of genomic profiling as a tool for molecular classification and clinical outcome prediction has revolutionised the care of patients with brain tumours. Artificial intelligence (AI) provides advanced avenues to convert complex genomic information into routinely available patient-level information. In this study, we aimed to evaluate whether deep learning could robustly characterise molecular subtypes, predict risk of recurrence, and identify salient chromosome copy number alterations in haematoxylin and eosin (H&E)-stained tissue samples of the most common brain tumour, meningioma.

For this retrospective, multicentre, model development and validation study, we created a cohort of meningioma cases with paired DNA methylation and matched digitised H&E images. The cohort included a training dataset consisting of a cross-section of real-world clinical cases from the National Cancer Institute (USA; n=439) and a dataset comprising real-world clinical cases from the University Health Network (Canada; n=166). A second test dataset consisting of WHO grade 2 meningiomas after gross total resection (from the Mayo Clinic, USA; n=67) was used for additional clinical validation. We trained and validated five dedicated deep learning models to use H&E staining alone to predict the following: molecular classification of meningiomas (ie, molecular groups 1-4 [MG1-4], which are associated with homogeneous biology and clinical outcomes); three relevant chromosome arm aneuploidies (1p loss, 1q gain, and 22q loss); and DNA methylation-based 5-year progression-free survival risk group (high vs low). Area under the receiver operating characteristic curve (AUC) and balanced accuracy were used to assess classifier performance, and differences in progression-free survival between groups predicted to be at high or low risk of recurrence were compared using the log-rank test.

Our deep learning classifier achieved balanced accuracies of 87-97% for predicting meningioma molecular group from H&E staining when an output probability threshold of 0·4 was used. AUCs were 0·98 (95% CI 0·97-1·00) for MG1 versus other molecular groups, 0·96 (0·94-0·99) for MG2 versus other molecular groups, 0·81 (0·74-0·88) for MG3 versus other molecular groups, and 0·88 (0·83-0·94) for MG4 versus other molecular groups. AUCs for chromosome-level alterations were 0·86 (0·80-0·92) for chromosome 1p loss, 0·86 (0·79-0·93) for chromosome 22q loss, and 0·79 (0·65-0·91) for chromosome 1q gain. The dedicated outcome prediction model was prognostic for progression-free survival after adjusting for WHO grade, extent of resection, and patient age (hazard ratio 3·49, 95% CI 1·54-7·91; p=0·0028).

To our knowledge, this study is the first to apply deep learning models that can, beyond diagnosis, identify molecular subtypes and predict outcomes in a single brain tumour entity (meningioma) using H&E staining alone. To date, treatment tailoring and individualised risk prediction for meningioma have only been possible using resource-intensive genomic profiling. The study shows the broad clinical utility of applying AI modelling to readily available H&E-stained samples to democratise access to genomic information globally.

Canadian Institutes of Health Research and Brain Tumour Charity.