A microfluorometric scanning method for the detection of cancer cells in smears of exfoliated cells

Towards Artificial Intelligence Applications in Next Generation Cytopathology

Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with the computer assisted smear test using early generation models. Today, advanced machine learning, working on large image data sets, has been shown to perform classification, detection, and segmentation with remarkable accuracy and generalization in several domains. Deep learning algorithms, as a branch of machine learning, are thus attracting attention in digital pathology and cytopathology, providing feasible solutions for accurate and efficient cytological diagnoses, ranging from efficient cell counts to automatic classification of anomalous cells and queries over large clinical databases. The integration of machine learning with related next-generation technologies powered by AI, such as augmented/virtual reality, metaverse, and computational linguistic models are a focus of interest in health care digitalization, to support education, diagnosis, and therapy. In this work we will consider how all these innovations can help cytopathology to go beyond the microscope and to undergo a hyper-digitalized transformation. We also discuss specific challenges to their applications in the field, notably, the requirement for large-scale cytopathology datasets, the necessity of new protocols for sharing information, and the need for further technological training for pathologists.

Optical properties of natural small molecules and their applications in imaging and nanomedicine

Natural small molecules derived from plants have fascinated scientists for centuries due to their practical applications in various fields, especially in nanomedicine. Some of the natural molecules were found to show intrinsic optical features such as fluorescence emission and photosensitization, which could be beneficial to provide spatial temporal information and help tracking the drugs in biological systems. Much efforts have been devoted to the investigation of optical properties and practical applications of natural molecules. In this review, optical properties of natural small molecules and their applications in fluorescence imaging, and theranostics will be summarized. First, we will introduce natural small molecules with different fluorescence emission, ranging from blue to near infrared emission. Second, imaging applications in biological samples will be covered. Third, we will discuss the applications of theranostic nanomedicines or drug delivering systems containing fluorescent natural molecules acting as imaging agents or photosensitizers. Finally, future perspectives in this field will be discussed.

Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools

Introduction

The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated.

Materials and Methods

To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process.

Results

We describe several "use cases" that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms.

Conclusions

The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.

Fluorescent natural products as probes and tracers in biology

Fluorescence is a remarkable property of many natural products in addition to their medicinal and biological value. Herein, we provide a review of these peculiar secondary metabolites to stimulate prospecting of them as original fluorescent tracers, endowed with unique photophysical properties and with applications in most fields of biology.

Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology

Introduction:

Historically, effective clinical utilization of image analysis and pattern recognition algorithms in pathology has been hampered by two critical limitations: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise.

Results:

In this report, we present a novel, general case pattern recognition algorithm, Spatially Invariant Vector Quantization (SIVQ), that overcomes the aforementioned knowledge deficit. Fundamentally based on conventional Vector Quantization (VQ) pattern recognition approaches, SIVQ gains its superior performance and essentially zero-training workflow model from its use of ring vectors, which exhibit continuous symmetry, as opposed to square or rectangular vectors, which do not. By use of the stochastic matching properties inherent in continuous symmetry, a single ring vector can exhibit as much as a millionfold improvement in matching possibilities, as opposed to conventional VQ vectors. SIVQ was utilized to demonstrate rapid and highly precise pattern recognition capability in a broad range of gross and microscopic use-case settings.

Conclusion:

With the performance of SIVQ observed thus far, we find evidence that indeed there exist classes of image analysis/pattern recognition algorithms suitable for deployment in settings where pathologists alone can effectively incorporate their use into clinical workflow, as a turnkey solution. We anticipate that SIVQ, and other related class-independent pattern recognition algorithms, will become part of the overall armamentarium of digital image analysis approaches that are immediately available to practicing pathologists, without the need for the immediate availability of an image analysis expert.

Automated image processing. Past, present, and future of blood cell morphology identification

Automated image processing analysis for leukocyte differential counting started 30 years ago principally as a mimic of the traditional microscopic method. Several types of systems were used in the 1970s and 1980s. In the late 1990s, two new image processing systems were developed with new technology for cell image analysis. They possess an intelligent neural network software and can be connected to an Ethernet for telehematologic diagnosis and consultation.

Fluorescence microscopy in exfoliative cytology

Images

Laser photodynamic therapy for bladder cancer

The lack of a tumor-localization aid for transitional cell carcinoma of the bladder and the known cytotoxic effect of light-activated intracellular hematoporphyrin derivative (HpD) prompted the initiation of a trial of HpD photodynamic therapy in patients with focal and diffuse bladder cancer. An argon-ion, pumped-dye laser was used as a source of red light. In 15 patients with focal disease and circumscribed treatment, 3-month follow-up cystoscopic examination showed disappearance of the lesions, but the subsequent recurrence rate seemed high. Therefore, whole-bladder HpD photodynamic therapy was administered to 14 patients with diffuse resistant carcinoma in situ. Three of these patients had focal recurrent disease. The only adverse effects of therapy were cutaneous photosensitivity and bladder irritability. HpD seems to be a good tumor-localizing aid for transitional cell carcinoma of the bladder, and HpD photodynamic therapy effectively destroys this type of carcinoma.

Photodynamic therapy for early squamous cell cancer of the upper aerodigestive tract

Photodynamic therapy (PDT) is a treatment modality that results in selective destruction of malignant cells by combining a photosensitizing agent (hematoporphyrin derivative), which is taken up by the cells, and a laser light. This therapeutic modality has been effectively used in managing cancers of the skin, bronchi and bladder. In the head and neck area, however, its use has been confined to the palliation of advanced lesions which have proved refractory to conventional therapy. While this is unquestionably a valid role, its true value, therapeutically, may be in the management of early cancers arising in the upper aerodigestive tract. At the University of Cincinnati, PDT has been used on a variety of such early cancers where, for a variety of reasons, conventional therapy was not possible. A preliminary experience with this modality in managing lesions of the larynx, oral cavity and oropharynx has demonstrated that this is a viable alternative.

Cesar: cervical smear analyser and reader. A new approach to evaluating cells in cytological preparations

Cesar presents an automated multiparameter approach to pattern recognition in the rapid detection of malignant cells from cervical and other cancers. Cells are cybernetically classified. They are collected into a container into which a nuclear stain may be incorporated and laid as a track on 35 mm. film using a disposable mapping pen device. The film track (Cytotrack) may be scanned using conventional optics and provision may be made for electronic sensing devices measuring a number of parameters, some of which are not normally used in conventional microscopy.