Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition.

Background: Polyploidy is frequently observed in cancer cells and is closely associated with chromosomal instability, which can lead to cancer progression. Polyploid cancers are more aggressive than diploid cancers, and polyploidy has been shown to be a prognostic marker for hepatocellular carcinoma (HCC). However, polyploidy is challenging to diagnose. Currently, no clinically implementable methods are available for diagnosing polyploidy in cancer.

Methods: We established a method for assessing polyploidization in HCC using deep-learning-based artificial intelligence image recognition models to assess hematoxylin and eosin-stained pathological images. Using 44 HCCs whose ploidy status had been determined by chromosome fluorescence in situ hybridization, we evaluated the ability of our constructed deep learning models to detect HCC ploidy. We then tested the models on an independent group of 169 liver cancers and applied them to a publicly available dataset.

Results: Here we show that our constructed models effectively assess HCC ploidy in a separate cohort and identify a subset with poor prognosis based on the ploidy determinations for 169 HCCs. Our pipeline also identifies HCCs with poor prognosis in the external dataset, with a more significant difference than that for ploidy inferences by genomic analysis. By exploiting the high processing capacity of artificial intelligence, new aspects of polyploid HCC, such as the high prevalence of scirrhous structures, are identified.

Conclusions: Our findings suggest that ploidy assessment using artificial intelligence-based pathological image recognition can serve as a novel diagnostic tool for personalized medicine.