A method to quickly classify central nervous system (CNS) tumours, combining rapid sequencing and deep-learned AI models, may enable molecular diagnosis in less than 90 minutes, according to a study published in Nature. The findings demonstrate the potential feasibility of obtaining molecular diagnosis of tumours during surgery to assist surgical decision making.
Primary treatment of CNS tumours involves surgical removal of the tumours, which requires careful consideration to strike a balance between maximising the removal of tumorous tissue while minimising the risk of neurological damage and other complications. The current standard practice relies on preoperative imaging and histological analysis during surgery, but these methods are not always conclusive and occasionally incorrect. Sequencing DNA to uncover methylation profiles could reveal information about the origin and prognosis of a tumour, but it usually takes several days to get the results.
To obtain DNA methylation profiles quickly enough to provide a diagnosis during surgery, Jeroen de Ridder from Oncode Institute, Utrecht, The Netherlands and others used a technology called nanopore sequencing. This method is faster, but the data generated has much less coverage of genetic sites compared with traditional sequencing technologies. To enable molecular classification of CNS tumours with such sparse data, the researchers developed a neural network tool named ‘Sturgeon’. “We developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular sub classification of central nervous system tumours based on such sparse profiles,” they write.
