Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review

The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords. Sixty‐eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin‐stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi‐institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist

Liver and brain pathology for Patient 1.

<p>Post-mortem liver samples from patient 1 (a and b) showed perivenular foci of enlarged hepatocytes with fine vacuolation (arrows). On lipid staining with oil red O (b) of frozen sections there was diffuse lipid deposition. Sample of the cerebral cortex from the occipital lobe showed full thickness neuronal loss with vacuolation and astrocytosis (c and d). Samples of the hippocampi (e and f) showed segmental neuronal loss, most marked from CA1 (arrow) and gliosis in a similar pattern (f-GFAP). Scale bars, a and b = 100 μm; c and d = 200 μm; e and f = 2 mm.</p

Muscle pathology for patient 3.

<p>Haematoxylin-Eosin stained section (A) showed mild variation in fibre size and several fibres with peripheral accumulation of mitochondria (arrowhead). Gomori trichrome preparation (B) accentuated ragged red fibres (arrowhead) and that for Succinic dehydrogenase (C) showed many ragged blue fibres (arrowheads). COX histochemical preparation (D) revealed frequent COX-deficient fibres (arrowheads) in keeping with mitochondrial myopathy. Scale bar = 50 μm.</p

Brain and muscle pathology for patient 2.

<p>A brain biopsy from patient 2 showed a little neuropil vacuolation (a) and cortical gliosis (b) but no specific diagnostic features. A muscle biopsy showed scattered cytochrome oxidase (COX)-negative fibres (f—arrows) but no other myopathic features (c) and no ragged red (d) or blue (e) fibres. Scale bars = 100 μm.</p

Long range PCR and Southern Blot analysis.

<p><b>A.</b> Long PCR of muscle mt DNA from the four patients. <b>1,</b> 1kb ladder; <b>2,</b> Muscle negative control; <b>3,</b> Patient 2; <b>4,</b> Patient 3; <b>5,</b> water control; <b>6,</b> Patient 4; <b>7,</b> patient 1. <b>B.</b> Depletion of mtDNA obtained from liver of patient 1. <b>1,</b> control; <b>2,</b> control; <b>3,</b> patient 1.</p