Two-photon optical imaging, spectral and fluorescence lifetime analysis to discriminate urothelial carcinoma grades

Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

Emerging clinical applications in oncology for non-invasive multi- and hyperspectral imaging of cell and tissue autofluorescence

Hyperspectral and multispectral imaging of cell and tissue autofluorescence is an emerging technology in which fluorescence imaging is applied to biological materials across multiple spectral channels. This produces a stack of images where each matched pixel contains information about the sample's spectral properties at that location. This allows precise collection of molecularly specific data from a broad range of native fluorophores. Importantly, complex information, directly reflective of biological status, is collected without staining and tissues can be characterised in situ, without biopsy. For oncology, this can spare the collection of biopsies from sensitive regions and enable accurate tumour mapping. For in vivo tumour analysis, the greatest focus has been on oral cancer, whereas for ex vivo assessment head-and-neck cancers along with colon cancer have been the most studied, followed by oral and eye cancer. This review details the scope and progress of research undertaken towards clinical translation in oncology.

Intracavity enhancement of GFP fluorescence induced by femtosecond laser pulses

The phenomenon of fluorescence is widely used in molecular biology for studying the interaction of light with biological objects. In this article, we present an experimental investigation of the enhancement of laser-induced fluorescence of Clytia gregaria green fluorescent protein. The laser-induced fluorescence method applied in our work combines the advantages of femtosecond laser pulses and a photonic crystal cavity, with the time dependence of the fluorescence signal studied. It is shown that a green fluorescent protein solution placed in a microcavity and excited by femtosecond laser pulses leads to an increase in fluorescence on the microcavity modes, which can be estimated by two orders of magnitude. The dependences of fluorescence signal saturation on the average integrated optical pump power are demonstrated and analyzed. The results obtained are of interest for the development of potential applications of biophotonics and extension of convenient methods of laser-induced fluorescence.

The use of artificial intelligence for the diagnosis of bladder cancer: a review and perspectives

PURPOSE OF REVIEW

White light cystoscopy is the current standard for primary diagnosis and surveillance of bladder cancer. However, cancer changes can be subtle and may be easily missed. With the advancement of deep learning (DL), image recognition by artificial intelligence (AI) proves a high accuracy for image-based diagnosis. AI can be a solution to enhance bladder cancer diagnosis on cystoscopy.

RECENT FINDINGS

An algorithm that classifies cystoscopic images into normal and tumour images is essential for AI cystoscopy. To develop this AI-based system requires a training dataset, an appropriate type of DL algorithm for the learning process and a specific outcome classification. A large data volume with minimal class imbalance, data accuracy and representativeness are pre-requisite for a good dataset. Algorithms developed during the past two years to detect bladder tumour achieved high performance with a pooled sensitivity of 89.7% and specificity of 96.1%. The area under the curve ranged from 0.960 to 0.980, and the accuracy ranged from 85.6 to 96.9%. There were also favourable results in the various attempts to enhance detection of flat lesions or carcinoma-in-situ.

SUMMARY

AI cystoscopy is a possible solution in clinical practice to enhance bladder cancer diagnosis, improve tumour clearance during transurethral resection of bladder tumour and detect recurrent tumours upon surveillance.

Human serum albumin gradient in serous ovarian cancer cryosections measured by fluorescence lifetime

Human serum albumin (HSA) is a depot and carrier for many endogenous and exogenous molecules in blood. Many studies have demonstrated that the transport of HSA in tumor microenvironments contributes to tumor development and progression. In this report, we set up a multimodal nonlinear optical microscope system, combining two-photon excitation fluorescence, second harmonic generation, and two-photon fluorescence lifetime imaging microscopy. The fluorescence lifetime of a small squaraine dye (SD) is used to evaluate HSA concentrations in tumor tissue based on specific binding between SD and HSA. We used SD to stain the cryosections from serous ovarian cancer patients in high-grade (HGSOC) and low-grade (LGSOC), respectively, and found a gradient descent of HSA concentration from normal connective tissue to extracellular matrix to tumor masses from 13 to 2 µM for LGSOC patients and from 36 to 12 µM for HGSOC patients. We demonstrated that multimodal nonlinear optical microscopy can obtain similar results as those from traditional histologic staining, thus it is expected to move to clinical applications.

Diagnostic challenges and treatment strategies in the management of upper-tract urothelial carcinoma

Upper-tract urothelial carcinoma (UTUC) is a rare disease, posing many challenges for the treating physician due to the lack of strong evidence-based recommendations. However, novel molecular discoveries and a better understanding of the clinical behavior of the disease lead to a continuous evolution of therapeutic landscape in UTUC. The aim of the review is to provide a comprehensive update of the current diagnostic modalities and treatment strategies in UTUC with a special focus on recent developments and challenges. A comprehensive literature search including relevant articles up to August 2020 was performed using the MEDLINE/PubMed database. Despite several technological improvements, accurate staging and outcome prediction remain major challenges and hamper appropriate risk stratification. Kidney-sparing surgery can be offered in low risk UTUC; however, physician and patient must be aware of the high rate of recurrence and risk of progression due to tumor biology and understaging. The value and efficacy of intracavitary therapy in patients with UTUC remains unclear due to the lack of high-quality data. In high-risk diseases, radical nephroureterectomy with bladder cuff excision and template lymph node dissection is the standard of care. Perioperative systemic chemotherapy is today accepted as a novel standard for advanced cancers. In metastatic or unresectable disease, the therapeutic landscape is rapidly changing due to several novel agents, such as checkpoint inhibitors. While several diagnostic and treatment challenges remain, progress in endoscopic technology and molecular knowledge have ushered a new age in personalized management of UTUC. Novel accurate molecular and imaging biomarkers are, however, still needed to guide decision making as tissue acquisition remains suboptimal. Next generation sequencing and novel agents are promising to rapidly improve patient outcomes.

Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects

BACKGROUND

Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.

OBJECTIVE

To provide a comprehensive review of machine learning (ML), deep learning (DL) and convolutional neural network (CNN) applications in cystoscopic image recognition.

EVIDENCE ACQUISITION

A detailed search of original articles was performed using the PubMed-MEDLINE database to identify recent English literature relevant to ML, DL and CNN applications in cystoscopic image recognition.

EVIDENCE SYNTHESIS

In total, two articles and one conference abstract were identified addressing the application of AI methods in cystoscopic image recognition. These investigations showed accuracies exceeding 90% for tumor detection; however, future work is necessary to incorporate these methods into AI-aided cystoscopy and compared to other tumor visualization tools. Furthermore, we present results from the RaVeNNA-4pi consortium initiative which has extracted 4200 frames from 62 videos, analyzed them with the U-Net network and achieved an average dice score of 0.67. Improvements in its precision can be achieved by augmenting the video/frame database.

CONCLUSION

AI-aided cystoscopy has the potential to outperform urologists at recognizing and classifying bladder lesions. To ensure their real-life implementation, however, these algorithms require external validation to generalize their results across other data sets.

Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine

This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early-stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non-atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100-pixel points were randomly extracted after binarization. Diagnostic models of non-atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial-least-square discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms. The prediction effects of PLS-DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS-DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early-stage gastric cancer.