Abstract 3042: Nuclear morphology predicts prostate cancer metastasis at diagnosis
Authors: Huang Fangjin , Ing Nathan , Miller Eric N. , Salemi Hootan , Lewis Michael S. , Garraway Isla P. , Gertych Arkadiusz , Knudsen Beatrice S. 
Published: August 2018

Background: Prostate needle biopsies (PNBX) that are obtained during the initial diagnostic workup are often the only source of tumor tissue that is available to determine the severity of prostate cancer (PC). Interestingly, tumor grades and pathologic features in PNBXs of men with lethal metastatic disease (M1 stage) are indistinguishable from those of high-grade nonmetastatic tumors of patients who do not progress after treatment (M0 stage). We hypothesize that the morphology of tumor nuclei can be used as a source of biomarker development to distinguish M1 tumors from high-grade localized M0 tumors.

Methods: Our study consists of a cohort of 85 high-grade M0 and 78 M1 cases, within a biorepository of 2150 patients at the Greater Los Angeles Veterans Affairs hospital. PNBX slides were digitized at 40X and pathologists annotated all cancer foci. These annotated regions were divided into smaller image tiles (dimensions) and fed into our digital image analysis pipeline for nuclear segmentation and feature extraction. We applied two distinct feature extraction methods to capture morphologic information within tumor nuclei. The 64 Handcrafted (HC) features describe nuclear properties such as shape, area, and chromatin conformation. The 62 Autoencoder (AE) features are abstract descriptors generated by a deep learning algorithm, which learns to redraw the nuclei. We built 2 machine learning models using AE or/and HC features for classification of M1 versus M0 cases. We divided our patients into 3 groups: training, testing and validation. A 7-fold cross-validation was performed in the training and testing sets and the area under the curve of the receiver operating characteristic (ROC) was calculated in the validation set.

Results: Model 1 utilizes processed features derived from patient-level distributions and generalized linear model. The estimated AUC for predicting M1 stage is 0.79 and 0.74 for HC and AE features, respectively. Model 2 uses the average of each features obtained from dominant group of nuclei within individual tiles and neural network models. Nuclei groups were assigned by unsupervised clustering method. For this model, the AUC of predicting M1 stage is 0.77 and 0.75 for HC and AE features, respectively.

Conclusion: By applying two distinct feature extraction methods and two approaches to summarize features, we obtain similar prediction accuracies. These results demonstrate that quantitative nuclear features contain information to classify M1 and M0 cases, which cannot be classified based on tumor grade or pathologic features.

Citation Format: Fangjin Huang, Nathan Ing, Eric N. Miller, Hootan Salemi, Michael S. Lewis, Isla P. Garraway, Arkadiusz Gertych, Beatrice S. Knudsen. Nuclear morphology predicts prostate cancer metastasis at diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3042.