Ex vivo confocal microscopy performs real-time assessment of renal biopsy in non-neoplastic diseases

The histomorphology of the senescent kidney - the clinical relevance of specimen and biopsy findings in the elderly native kidneys

PURPOSE OF REVIEW

Renal pathology is crucial in diagnosing the ageing kidney. Recent technological advances enabled high-resolution molecular investigations into the complex mechanisms of ageing and senescence.

RECENT FINDINGS

The pathological analysis of large kidney tissue collections coupled with computer-assisted morphometry contributed to the establishment of age-related reference values for glomerular or vascular sclerosis, interstitial fibrosis, and tubular atrophy. Furthermore, new high-throughput proteomic and transcriptomic platforms have entered the field of pathology. When coupled with morphology information, these techniques facilitated the study of extracellular matrix modifications and the senescent immune system in the ageing kidney. Finally, iatrogenic complications are now frequent indications for diagnostic kidney biopsies in older patients, potentially accelerating kidney senescence.

SUMMARY

Recent pathology literature supports identifying and prognosticating sclerosing processes in ageing kidneys.

The Puzzle of Preimplantation Kidney Biopsy Decision-Making Process: The Pathologist Perspective

Kidney transplantation is the best treatment for end-stage renal disease since it offers the greatest survival benefit compared to dialysis. The gap between the number of renal transplants performed and the number of patients awaiting renal transplants leads to a steadily increasing pressure on the scientific community. Kidney preimplantation biopsy is used as a component of the evaluation of organ quality before acceptance for transplantation. However, the reliability and predictive value of biopsy data are controversial. Most of the previously proposed predictive models were not associated with graft survival, but what has to be reaffirmed is that histologic examination of kidney tissue can provide an objective window on the state of the organ that cannot be deduced from clinical records and renal functional studies. The balance of evidence indicates that reliable decisions about donor suitability must be made based on the overall picture. This work discusses recent trends that can reduce diagnostic timing and variability among players in the decision-making process that lead to kidney transplants, from the pathologist's perspective.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

Ex vivo confocal microscopy detects basic patterns of acute and chronic lesions using fresh kidney samples

BACKGROUND

Ex vivo confocal microscopy is a real-time technique that provides high-resolution images of fresh, non-fixed tissues, with an optical resolution comparable to conventional pathology. The objective of this study was to investigate the feasibility of using ex vivo confocal microscopy in fusion mode (FuCM) and the haematoxylin and eosin (H&E)-like digital staining that results for the analysis of basic patterns of lesion in nephropathology.

METHODS

Forty-eight renal samples were scanned in a fourth-generation ex vivo confocal microscopy device. Samples were subjected to confocal microscopy imaging and were then processed using conventional pathology techniques. Concordance between the techniques was evaluated by means of the percentage of agreement and the κ index.

RESULTS

Agreement between conventional microscopy and H&E-like digital staining was strong (κ = 0.88) in the evaluation of acute tubular damage and was substantial (κ = 0.79) in the evaluation of interstitial fibrosis, interstitial inflammation, arterial and arteriolar lesions. H&E-like digital staining also allows rapid identification of extracapillary proliferation (κ = 0.88), necrosis and segmental sclerosis (κ = .88) in the glomerular compartment, but the results reported here are limited because of the small number of cases with these glomerular findings.

CONCLUSIONS

FuCM proved to be as effective as conventional techniques in evaluating the presence of acute tubular necrosis and interstitial fibrosis changes, but in fresh tissue. The ease of acquisition of ex vivo confocal microscopy images suggests that FuCM may be useful for rapid evaluation of kidney biopsies and to restructure the clinical workflow in renal histopathology.

A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).

New imaging technologies for robotic kidney cancer surgery

Objective

Kidney cancers account for approximately 2% of all newly diagnosed cancer in 2020. Among the primary treatment options for kidney cancer, urologist may choose between radical or partial nephrectomy, or ablative therapies. Nowadays, robotic-assisted partial nephrectomy (RAPN) for the management of renal cancers has gained popularity, up to being considered the gold standard. However, RAPN is a challenging procedure with a steep learning curve.

Methods

In this narrative review, different imaging technologies used to guide and aid RAPN are discussed.

Results

Three-dimensional visualization technology has been extensively discussed in RAPN, showing its value in enhancing robotic-surgery training, patient counseling, surgical planning, and intraoperative guidance. Intraoperative imaging technologies such as intracorporeal ultrasound, near-infrared fluorescent imaging, and intraoperative pathological examination can also be used to improve the outcomes following RAPN. Finally, artificial intelligence may play a role in the field of RAPN soon.

Conclusion

RAPN is a complex surgery; however, many imaging technologies may play an important role in facilitating it.

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment

SIGNIFICANCE

There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology).

AIM

Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption.

APPROACH

We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations.

RESULTS

While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow.

CONCLUSIONS

The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.

Ex Vivo Fluorescence Confocal Microscopy in Specimens of the Liver: A Proof-of-Concept Study

Ex vivo Fluorescence Confocal Microscopy (FCM) is a technique providing high-resolution images of native tissues. The method is increasingly used in surgical settings in areas of dermatology and urology. Only a few publications exist about examinations of tumors and non-neoplastic lesions of the liver. We report on the application of FCM in biopsies, surgical specimens and autopsy material (33 patients, 39 specimens) of the liver and compare the results to conventional histology. Our preliminary examinations indicated a perfect suitability for tumor diagnosis (ĸ = 1.00) and moderate/good suitability for the assessment of inflammation (ĸ = 0.4-0.6) with regard to their severity and localization. Macro-vesicular steatosis was reliably detected, micro-vesicular steatosis tended to be underestimated. Cholestasis and eosinophilic granules in granulocytes were not represented in the scans. The tissue was preserved as native material and maintained its quality for downstream histological, immunohistological and molecular examinations. In summary, FCM is a material sparing method that provides rapid feedback to the clinician about the presence of tumor, the degree of inflammation and structural changes. This can lead to faster therapeutic decisions in the management of liver tumors, treatment of hepatitis or in liver transplant medicine.

Exploring the utility of Deep Red Anthraquinone 5 for digital staining of ex vivo confocal micrographs of optically sectioned skin

We investigated the utility of the fluorescent dye Deep Red Anthraquinone 5 (DRAQ5) for digital staining of optically sectioned skin in comparison to acridine orange (AO). Eight fresh-frozen thawed Mohs discard tissue specimens were stained with AO and DRAQ5, and imaged using an ex vivo confocal microscope at three wavelengths (488 nm and 638 nm for fluorescence, 785 nm for reflectance). Images were overlaid (AO + Reflectance, DRAQ5 + Reflectance), digitally stained, and evaluated by three investigators for perceived image quality (PIQ) and histopathological feature identification. In addition to nuclear staining, AO seemed to stain dermal fibers in a subset of cases in digitally stained images, while DRAQ5 staining was more specific to nuclei. Blinded evaluation showed substantial agreement, favoring DRAQ5 for PIQ (82%, Cl 75%-90%, Gwet's AC 0.74) and for visualization of histopathological features in (81%, Cl 73%-89%, Gwet's AC 0.67), supporting its use in digital staining of multimodal confocal micrographs of skin.