A deep learning system can classify primary and metastatic cancers using passenger mutation patterns, but how does it really compare to current pathological diagnosis in a well-designed diagnostic accuracy study?
In a recent study published in Nature Communications by Jiao W et al, a deep learning classifier was trained to predict cancer type based on somatic passenger mutations identified using whole genome sequencing (WGS) as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. The data show patterns of somatic passenger mutations differ between tumours with different cell of origin. Overall, the system had an accuracy of 91% in a cross-validation setting using the training set, and 88% and 83% using external validation sets of primary and metastatic tumours respectively. Surprisingly, this is claimed to be twice as accurate as trained pathologists, based on a 27 year old reference from 1993 prior to availability and routine utilisation of immunohistochemistry (IHC) in diagnostic pathology and is not a reflection of current diagnostic standards. We discuss the vital role of pathology in patient care and the importance of using international standards if deep learning methods are to be used in the clinical setting.