Novel window for cancer nanotheranostics: non-invasive ocular assessments of tumor growth and nanotherapeutic treatment efficacy in vivo

Deep learning models for benign and malign ocular tumor growth estimation

Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image processing methods. Clinicians often face issues in selecting suitable image processing algorithm for medical imaging data. A strategy for the selection of a proper model is presented here. The training data set comprises optical coherence tomography (OCT) and angiography (OCT-A) images of 50 mice eyes with more than 100 days follow-up. The data contains images from treated and untreated mouse eyes. Four deep learning variants are tested for automatic (a) differentiation of tumor region with healthy retinal layer and (b) segmentation of 3D ocular tumor volumes. Exhaustive sensitivity analysis of deep learning models is performed with respect to the number of training and testing images using eight performance indices to study accuracy, reliability/reproducibility, and speed. U-net with UVgg16 is best for malign tumor data set with treatment (having considerable variation) and U-net with Inception backbone for benign tumor data (with minor variation). Loss value and root mean square error (R.M.S.E.) are found most and least sensitive performance indices, respectively. The performance (via indices) is found to be exponentially improving regarding a number of training images. The segmented OCT-Angiography data shows that neovascularization drives the tumor volume. Image analysis shows that photodynamic imaging-assisted tumor treatment protocol is transforming an aggressively growing tumor into a cyst. An empirical expression is obtained to help medical professionals choose a particular model given the number of images and types of characteristics. We recommend that the presented exercise should be taken as standard practice before employing a particular deep learning model for biomedical image analysis.

Nanomedicines and microneedles: a guide to their analysis and application

The fast-advancing progress in the research of nanomedicine and microneedle applications in the past two decades has suggested that the combination of the two concepts could help to overcome some of the challenges we are facing in healthcare. They include poor patient compliance with medication and the lack of appropriate administration forms that enable the optimal dose to reach the target site. Nanoparticles as drug vesicles can protect their cargo and deliver it to the target site, while evading the body's defence mechanisms. Unfortunately, despite intense research on nanomedicine in the past 20 years, we still haven't answered some crucial questions, e.g. about their colloidal stability in solution and their optimal formulation, which makes the translation of this exciting technology from the lab bench to a viable product difficult. Dissolvable microneedles could be an effective way to maintain and stabilise nano-sized formulations, whilst enhancing the ability of nanoparticles to penetrate the stratum corneum barrier. Both concepts have been individually investigated fairly well and many analytical techniques for tracking the fate of nanomaterials with their precious cargo, both in vitro and in vivo, have been established. Yet, to the best of our knowledge, a comprehensive overview of the analytical tools encompassing the concepts of microneedles and nanoparticles with specific and successful examples is missing. In this review, we have attempted to briefly analyse the challenges associated with nanomedicine itself, but crucially we provide an easy-to-navigate scheme of methods, suitable for characterisation and imaging the physico-chemical properties of the material matrix.

Imaging methods to evaluate tumor microenvironment factors affecting nanoparticle drug delivery and antitumor response

Standard small molecule and nanoparticulate chemotherapies are used for cancer treatment; however, their effectiveness remains highly variable. One reason for this variable response is hypothesized to be due to nonspecific drug distribution and heterogeneity of the tumor microenvironment, which affect tumor delivery of the agents. Nanoparticle drugs have many theoretical advantages, but due to variability in tumor microenvironment (TME) factors, the overall drug delivery to tumors and associated antitumor response are low. The nanotechnology field would greatly benefit from a thorough analysis of the TME factors that create these physiological barriers to tumor delivery and treatment in preclinical models and in patients. Thus, there is a need to develop methods that can be used to reveal the content of the TME, determine how these TME factors affect drug delivery, and modulate TME factors to increase the tumor delivery and efficacy of nanoparticles. In this review, we will discuss TME factors involved in drug delivery, and how biomedical imaging tools can be used to evaluate tumor barriers and predict drug delivery to tumors and antitumor response.

National Cancer Institute Alliance for nanotechnology in cancer-Catalyzing research and translation toward novel cancer diagnostics and therapeutics

Nanotechnology has been a burgeoning research field, which is finding compelling applications in several practical areas of everyday life. It has provided novel, paradigm shifting solutions to medical problems and particularly to cancer. In order to accelerate integration of nanotechnology into cancer research and oncology, the National Cancer Institute (NCI) of the National Institutes of Health (NIH) established the NCI Alliance for Nanotechnology in Cancer program in 2005. This effort brought together scientists representing physical sciences, chemistry, and engineering working at the nanoscale with biologists and clinicians working on cancer to form a uniquely multidisciplinary cancer nanotechnology research community. The last 14 years of the program have produced a remarkable body of scientific discovery and demonstrated its utility to the development of practical cancer interventions. This paper takes stock of how the Alliance program influenced melding of disparate research disciplines into the field of nanomedicine and cancer nanotechnology, has been highly productive in the scientific arena, and produced a mechanism of seamless transfer of novel technologies developed in academia to the clinical and commercial space. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging.

Temporal speckle-averaging of optical coherence tomography volumes for in-vivo cellular resolution neuronal and vascular retinal imaging

It has been recently demonstrated that structures corresponding to the cell bodies of highly transparent cells in the retinal ganglion cell layer could be visualized noninvasively in the living human eye by optical coherence tomography (OCT) via temporal averaging. Inspired by this development, we explored the application of volumetric temporal averaging in mice, which are important models for studying human retinal diseases and therapeutic interventions. A general framework of temporal speckle-averaging (TSA) of OCT and optical coherence tomography angiography (OCTA) is presented and applied to mouse retinal volumetric data. Based on the image analysis, the eyes of mice under anesthesia exhibit only minor motions, corresponding to lateral displacements of a few micrometers and rotations of a fraction of 1 deg. Moreover, due to reduced eye movements under anesthesia, there is a negligible amount of motion artifacts within the volumes that need to be corrected to achieve volume coregistration. In addition, the relatively good optical quality of the mouse ocular media allows for cellular-resolution imaging without adaptive optics (AO), greatly simplifying the experimental system, making the proposed framework feasible for large studies. The TSA OCT and TSA OCTA results provide rich information about new structures previously not visualized in living mice with non-AO-OCT. The mechanism of TSA relies on improving signal-to-noise ratio as well as efficient suppression of speckle contrast due to temporal decorrelation of the speckle patterns, enabling full utilization of the high volumetric resolution offered by OCT and OCTA.

Progress in Multimodal En Face Imaging: feature introduction

This feature issue contains papers that report on the most recent advances in the field of en face optical coherence tomography (OCT) and of combinations of modalities facilitated by the en face view. Hardware configurations for delivery of en face OCT images are described as well as specific signal and image processing techniques tailored to deliver relevant clinical diagnoses. The value of the en face perspective for enabling multimodality is illustrated by several combination modalities.