AI-based pathology predicts origins for cancers of unknown primary

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined(1,2). This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour(3). Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour(4-9). However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm-Tumour Origin Assessment via Deep Learning (TOAD)-that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.

Automated summary

Cancer of unknown primary (CUP) is a difficult diagnosis as the primary site of tumor origin cannot be determined. The deep-learning-based algorithm TOAD provides a differential diagnosis for the origin of the primary tumor, achieving high accuracy on test sets and reducing the occurrence of CUP by assisting in assigning differential diagnoses for complicated cases.