Abstract 5045: Genomics and pathology based deep learning to predict cancers of unknown primary

Abstract

Abstract Background: Cancers of unknown primary (CUP) are a type of metastatic cancer. However, their primary anatomical site of origin cannot be clinically determined using routine history inquiries, laboratory tests, endoscopy, and imaging. CUP account for ~3-5% of cancers. Empirical chemotherapy (paclitaxel, carboplatin, etc.) is generally used, although the curative effect is poor. Finding the primary site of origin for CUP patients is of great significance for clinical treatment. Method: Our study employed 10,001 pathological images and 9,775 gene detection data points based on whole-exome sequencing (WES) or YUANSU®(OrigiMed, Shanghai, China) for primary cancer within a database containing 32 common cancers. We applied machine learning algorithms (autoML, Transformers, attention) and constructed two diagnostic models. The models diagnosed by identifying pathological images (Model 1) and genomic data (Model 2). Accuracy for the two models was verified. Result: Both models were evaluated using top-k differential diagnosis accuracy, in other words how often the ground truth label was found for k highest confidence predictions for the model. The pathological model (Model 1) achieved a top-3 accuracy of 83.38% and a top-5 accuracy of 90.36%. Using the same methodology, genomic model (Model 2) results were 87.5% and 92.2%, respectively. Conclusion: Using deep learning, we developed diagnostic models for CUP based on pathological images and genomic data. Accuracy for both the pathological image model (Model 1) and the genomic data model (Model 2) was not satisfactory. To improve diagnostic accuracy, further studies on developing a diagnostic model that combines pathological imaging and genomic data are ongoing.. Citation Format: Yanan Wang, Guanjun Zhang, Xi Liu, Yanfeng Xi, Pan Wang, Yuman Zhang, Xing Li. Genomics and pathology based deep learning to predict cancers of unknown primary [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5045.