Margin diagnosis for endoscopic submucosal dissection of early gastric cancer using multiphoton microscopy. Surg Endosc. 2020;34:408-416. [PMID: 30972623 DOI: 10.1007/s00464-019-06783-1]

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.

Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.

Application of Multiphoton Microscopic Imaging in Study of Gastric Cancer

Multiphoton microscopy (MPM) imaging relies on the nonlinear interaction between ultrashort optical pulses and the samples to achieve image contrast. Featuring larger penetration depth, less phototoxicity, 3-dimensional sectioning capability, no need for labeling, MPM become a powerful medical imaging technique that can identify structural characteristics of tissues at the cellular and subcellular levels. In this review paper, we introduce the working principle of MPM imaging, present the current results of MPM imaging applied to the study of gastric tumors, and discuss the future prospects of this interdisciplinary research field.

Balloon-assisted endoscopic submucosal dissection for treating small intestinal lipomas: Report of two cases

BACKGROUND

Most small intestinal lipomas are treated surgically, and some require repeated surgeries for multiple lipomas. However, application of endoscopic submucosal dissection (ESD) technology in the deep small intestine is rarely reported owing to the special anatomical structure of the small intestine, medical equipment limitations, and the lack of relevant experience among endoscopists.

CASE SUMMARY

Two patients with small intestinal lipomas treated at the Air Force Medical Center from November 2015 to September 2019 were selected to undergo balloon-assisted ESD to treat the lipomas and explore the technical feasibility and safety of ESD for treating small intestinal lipomas. The two patients successfully underwent balloon-assisted ESD to treat four small intestinal lipomas, with a complete resection rate of 100% (4/4), without intraoperative or postoperative bleeding, perforation, or other complications. After 3-6 mo of postoperative follow-up, the clinical symptoms caused by the lipomas were significantly relieved or disappeared after treatment.

CONCLUSION

Balloon-assisted ESD is a safe and reliable new method for treating deep intestinal lipomas and shows good clinical feasibility.

Characterization of fibrillar collagen isoforms in infarcted mouse hearts using second harmonic generation imaging

We utilized collagen specific second harmonic generation (SHG) signatures coupled with correlative immunofluorescence imaging techniques to characterize collagen structural isoforms (type I and type III) in a murine model of myocardial infarction (MI). Tissue samples were imaged over a four week period using SHG, transmitted light microscopy and immunofluorescence imaging using fluorescently-labeled collagen antibodies. The post-mortem cardiac tissue imaging using SHG demonstrated a progressive increase in collagen deposition in the left ventricle (LV) post-MI. We were able to monitor structural morphology and LV remodeling parameters in terms of extent of LV dilation, stiffness and fiber dimensions in the infarcted myocardium.

Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree-based pipeline optimization tool

Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time-consuming and labor-intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree-based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high-efficiency computer-aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.

Multiphoton microscopy in surgical oncology- a systematic review and guide for clinical translatability

BACKGROUND

Multiphoton microscopy (MPM) facilitates three-dimensional, high-resolution functional imaging of unlabeled tissues in vivo and ex vivo. This systematic review discusses the diagnostic value, advantages and challenges in the practical use of MPM in surgical oncology.

METHOD AND FINDINGS

A Medline search was conducted in April 2019. Fifty-three original research papers investigating MPM compared to standard histology in human patients with solid tumors were identified. A qualitative synopsis and meta-analysis of 14 blinded studies was performed. Risk of bias and applicability were evaluated. MPM can image fresh, frozen or fixed tissues up to a depth 1000 μm in the z-plane. Best results including functional imaging and virtual histochemistry are obtained by in vivo imaging or scanning fresh tissue immediately after excision. Two-photon excited fluorescence by natural fluorophores of the cytoplasm and second harmonic generation signals by fluorophores of the extracellular matrix can be scanned simultaneously, providing high resolution optical histochemistry comparable to standard histology. Functional parameters like fluorescence lifetime imaging or optical redox ratio provide additional objective information. A major concern is inability to visualize the nucleus. However, in a subpopulation analysis of 440 specimens, MPM yielded a sensitivity of 94%, specificity of 96% and accuracy of 95% for the detection of malignant tissue.

CONCLUSION

MPM is a promising emerging technique in surgical oncology. Ex vivo imaging has high sensitivity, specificity and accuracy for the detection of tumor cells. For broad clinical application in vivo, technical challenges need to be resolved.