Axonfinder: Automated Segmentation of Tumor Innervating Neuronal Fibers

Abstract

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

Note:
Funding Information: This project was supported by funding (CEDAR3410918) from the Cancer Early Detection Advanced Research Centre at Oregon Health & Science University, Knight Cancer Institute (S.E.E.).

Conflict of Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical Approval: Our research complies with all relevant ethical regulations set forth by the Knight Cancer Institute at Oregon Health & Science University (OHSU). Patient samples used in this study were collected from individuals undergoing radical prostatectomy, with informed consent obtained in accordance with protocols approved by the OHSU Institutional Review Board (IRB# 4918). No compensation was provided to participants.

Keywords: Neurosignaling, axon, prostate cancer, CyCIF, multiplex imaging, deep learning, image segmentation.