A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology

Nathan Ing, Jakub M. Tomczak, Eric Miller, Isla P. Garraway, Max Welling, Beatrice S. Knudsen, Arkadiusz Gertych

Abstract: We consider the problem of identifying the patients who are diagnosed with high- grade prostate cancer using the histopathology of tumor in a prostate needle biopsy and are at a very high risk of lethal cancer progression. We hypothesize that the morphology of tumor cell nuclei in digital images from the biopsy can be used to predict tumor aggressiveness and posit the presence of metastasis as a surrogate for disease specific mortality. For this purpose, we apply a compositional multi- instance learning approach which encodes images of nuclei through a convolutional neural network, then predicts the presence of metastasis from sets of encoded nuclei. Through experiments on prostate needle biopsies (PNBX) from a patient cohort with known presence (M1 stage, n = 85) or absence (M0 stage, n = 86) of metastatic disease, we obtained an average area under the receiver operating characteristic curve of 0.71 ± 0.08 for predicting metastatic cases. These results support our hypothesis that information related to metastatic capacity of prostate cancer cells can be obtained through analysis of nuclei and establish a baseline for future research aimed at predicting the risk of future metastatic disease at a time when it might be preventable.

Keywords: Digital pathology, nuclear morphology, multiple instance learning, high grade prostate cancer

Author Affiliation: Cedars-Sinai Medical Center, University of Amsterdam, University of California Los Angeles