An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

ABSTRACT:

Background

Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy.

Methods

We designed a regional multiple-instance learning algorithm to predict the OBMC based on hematoxylin-eosin (H&E) staining slides alone. We collected 1041 cases from eight different hospitals and labeled 26,431 regions of interest to train the model. The performance of the model was assessed by ten-fold cross validation and external validation. Under the guidance of top3 predictions, we conducted an IHC test on 175 cases of unknown origins to compare the consistency of the results predicted by the model and indicated by the IHC markers. We also a

SUBMITTER: Zhu L 

PROVIDER: S-EPMC9803701 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature