A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data

Complex high-dimensional datasets that are challenging to analyze are frequently produced through ‘-omics’ profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in ‘-omics’ data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in ‘-omics’ data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected genes in a model to classify the three subtypes and compared the results to models built using all genes and all differentially expressed genes. Genes identified with the molecular feature selection tool performed better than the other models in this study in all comparison metrics: accuracy, precision, recall, and F1-score using a significantly smaller set of genes. In addition, we developed a simple graphical user interface for the molecular feature selection tool, which is available for free download. This user-friendly interface is a valuable tool for the identification of potential biomarkers in gene expression datasets and is an asset for biomarker discovery studies.

An End-to-End Solution for Automatic Contouring of Tumor Region in Intraoperative Images of Breast Lumpectomy

Breast-conserving surgery, also known as lumpectomy, is an early stage breast cancer treatment that aims to spare as much healthy breast tissue as possible. A risk associated with lumpectomy is the presence of cancer positive margins post operation. Surgical navigation has been shown to reduce cancer positive margins but requires manual segmentation of the tumor intraoperatively. In this paper, we propose an end-to-end solution for automatic contouring of breast tumor from intraoperative ultrasound images using two convolutional neural network architectures, the U-Net and residual U-Net. The networks are trained on annotated intraoperative breast ultrasound images and evaluated on the quality of predicted segmentations. This work brings us one step closer to providing surgeons with an automated surgical navigation system that helps reduce cancer-positive margins during lumpectomy.

Soft Tissue Characterization with Temporal Enhanced Ultrasound through Periodic Manipulation of Point Spread Function: A Feasibility Study

Temporal enhanced ultrasound (TeUS) is a tissue characterization approach based on analysis of a temporal series of US data. Previously we demonstrated that intrinsic or external micro-motions of scatterers in the tissue contribute towards the tissue classification properties of TeUS. This property is beneficial to detect early stage cancer, for example, where changes in nuclei configuration (scatteres) dominate tissue properties. In this study, we propose an analytical derivation and experiments to acquire TeUS through manipulation of US imaging parameters, which may be simpler to translate to clinical applications. The feasibility of the proposed method is demonstrated on tissue-mimicking phantoms. Using an autoencoder classifier, we are able to classify phantoms of varying elasticities and scattering sizes.