Recent Progress on Molecular Photoacoustic Imaging with Carbon-Based Nanocomposites

Advances in photoacoustic imaging aided by nano contrast agents: special focus on role of lymphatic system imaging for cancer theranostics

Photoacoustic imaging (PAI) is a successful clinical imaging platform for management of cancer and other health conditions that has seen significant progress in the past decade. However, clinical translation of PAI based methods are still under scrutiny as the imaging quality and clinical information derived from PA images are not on par with other imaging methods. Hence, to improve PAI, exogenous contrast agents, in the form of nanomaterials, are being used to achieve better image with less side effects, lower accumulation, and improved target specificity. Nanomedicine has become inevitable in cancer management, as it contributes at every stage from diagnosis to therapy, surgery, and even in the postoperative care and surveillance for recurrence. Nanocontrast agents for PAI have been developed and are being explored for early and improved cancer diagnosis. The systemic stability and target specificity of the nanomaterials to render its theranostic property depends on various influencing factors such as the administration route and physico-chemical responsiveness. The recent focus in PAI is on targeting the lymphatic system and nodes for cancer diagnosis, as they play a vital role in cancer progression and metastasis. This review aims to discuss the clinical advancements of PAI using nanoparticles as exogenous contrast agents for cancer theranostics with emphasis on PAI of lymphatic system for diagnosis, cancer progression, metastasis, PAI guided tumor resection, and finally PAI guided drug delivery.

Organic quantum dots: An ultrasmall nanoplatform for cancer theranostics

Tumours are the second leading cause of death globally, generating alterations in biological interactions and, as a result, malfunctioning of crucial genetic traits. Technological advancements have made it possible to identify tumours at the cellular level, making transcriptional gene variations and other genetic variables more easily investigated. Standard chemotherapy is seen as a non-specific treatment that has the potential to destroy healthy cells while also causing systemic toxicity in individuals. As a result, developing new technologies has become a pressing necessity. QDs are semiconductor particles with diameters ranging from 2 to 10 nanometers. QDs have grabbed the interest of many researchers due to their unique characteristics, including compact size, large surface area, surface charges, and precise targeting. QD-based drug carriers are well known among the many nanocarriers. Using QDs as a delivery approach enhances solubility, lengthens retention time, and reduces the harmful effects of loaded medicines. Several varieties of quantum dots used in drug administration are discussed in this article, along with their chemical and physical characteristics and manufacturing methods. Furthermore, it discusses the role of QDs in biological, medicinal, and theranostic applications.

Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review

Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging.