Robust, credible, and interpretable AI-based histopathological prostate cancer grading.

Background: Prostate cancer (PCa) is among the most common cancers in men and its diagnosis requires the histopathological evaluation of biopsies by human experts. While several recent artificial intelligence-based (AI) approaches have reached human expert-level PCa grading, they often display significantly reduced performance on external datasets. This reduced performance can be caused by variations in sample preparation, for instance the staining protocol, section thickness, or scanner used. Another limiting factor of contemporary AI-based PCa grading is the prediction of ISUP grades, which leads to the perpetuation of human annotation errors. Methods: We developed the prostate cancer aggressiveness index (PCAI), an AI-based PCa detection and grading framework that is trained on objective patient outcome, rather than subjective ISUP grades. We designed PCAI as a clinical application, containing algorithmic modules that offer robustness to data variation, medical interpretability, and a measure of prediction confidence. To train and evaluate PCAI, we generated a multicentric, retrospective, observational trial consisting of six cohorts with 25,591 patients, 83,864 images, and 5 years of median follow-up from 5 different centers and 3 countries. This includes a high-variance dataset of 8,157 patients and 28,236 images with variations in sample thickness, staining protocol, and scanner, allowing for the systematic evaluation and optimization of model robustness to data variation. The performance of PCAI was assessed on three external test cohorts from two countries, comprising 2,255 patients and 9,437 images. Findings: Using our high-variance datasets, we show how differences in sample processing, particularly slide thickness and staining time, significantly reduce the performance of AI-based PCa grading by up to 6.2 percentage points in the concordance index (C-index). We show how a select set of algorithmic improvements, including domain adversarial training, conferred robustness to data variation, interpretability, and a measure of credibility to PCAI. These changes lead to significant prediction improvement across two biopsy cohorts and one TMA cohort, systematically exceeding expert ISUP grading in C-index and AUROC by up to 22 percentage points. Interpretation: Data variation poses serious risks for AI-based histopathological PCa grading, even when models are trained on large datasets. Algorithmic improvements for model robustness, interpretability, credibility, and training on high-variance data as well as outcome-based severity prediction gives rise to robust models with above ISUP-level PCa grading performance.

DeePSC: A Deep Learning Model for Automated Diagnosis of Primary Sclerosing Cholangitis at Two-dimensional MR Cholangiopancreatography.

Purpose: To develop, train, and validate a multiview deep convolutional neural network (DeePSC) for the automated diagnosis of primary sclerosing cholangitis (PSC) on two-dimensional MR cholangiopancreatography (MRCP) images. Materials and Methods: This retrospective study included two-dimensional MRCP datasets of 342 patients (45 years ± 14 [SD]; 207 male patients) with confirmed diagnosis of PSC and 264 controls (51 years ± 16; 150 male patients). MRCP images were separated into 3-T (n = 361) and 1.5-T (n = 398) datasets, of which 39 samples each were randomly chosen as unseen test sets. Additionally, 37 MRCP images obtained with a 3-T MRI scanner from a different manufacturer were included for external testing. A multiview convolutional neural network was developed, specialized in simultaneously processing the seven images taken at different rotational angles per MRCP examination. The final model, DeePSC, derived its classification per patient from the instance expressing the highest confidence in an ensemble of 20 individually trained multiview convolutional neural networks. Predictive performance on both test sets was compared with that of four licensed radiologists using the Welch t test. Results: DeePSC achieved an accuracy of 80.5% ± 1.3 (sensitivity, 80.0% ± 1.9; specificity, 81.1% ± 2.7) on the 3-T and 82.6% ± 3.0 (sensitivity, 83.6% ± 1.8; specificity, 80.0% ± 8.9) on the 1.5-T test set and scored even higher on the external test set (accuracy, 92.4% ± 1.1; sensitivity, 100.0% ± 0.0; specificity, 83.5% ± 2.4). DeePSC outperformed radiologists in average prediction accuracy by 5.5 (P = .34, 3 T) and 10.1 (P = .13, 1.5 T) percentage points. Conclusion: Automated classification of PSC-compatible findings based on two-dimensional MRCP was achievable and demonstrated high accuracy on internal and external test sets.Keywords: Neural Networks, Deep Learning, Liver Disease, MRI, Primary Sclerosing Cholangitis, MR Cholangiopancreatography Supplemental material is available for this article. © RSNA, 2023.

Expansion-enhanced super-resolution radial fluctuations enable nanoscale molecular profiling of pathology specimens.

Expansion microscopy physically enlarges biological specimens to achieve nanoscale resolution using diffraction-limited microscopy systems1. However, optimal performance is usually reached using laser-based systems (for example, confocal microscopy), restricting its broad applicability in clinical pathology, as most centres have access only to light-emitting diode (LED)-based widefield systems. As a possible alternative, a computational method for image resolution enhancement, namely, super-resolution radial fluctuations (SRRF)2,3, has recently been developed. However, this method has not been explored in pathology specimens to date, because on its own, it does not achieve sufficient resolution for routine clinical use. Here, we report expansion-enhanced super-resolution radial fluctuations (ExSRRF), a simple, robust, scalable and accessible workflow that provides a resolution of up to 25 nm using LED-based widefield microscopy. ExSRRF enables molecular profiling of subcellular structures from archival formalin-fixed paraffin-embedded tissues in complex clinical and experimental specimens, including ischaemic, degenerative, neoplastic, genetic and immune-mediated disorders. Furthermore, as examples of its potential application to experimental and clinical pathology, we show that ExSRRF can be used to identify and quantify classical features of endoplasmic reticulum stress in the murine ischaemic kidney and diagnostic ultrastructural features in human kidney biopsies.

Deep learning-based molecular morphometrics for kidney biopsies.

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. We identified previously unknown morphometric signatures of podocyte depletion in patients with ANCA-GN, which allowed patient classification and, in combination with routine clinical tools, showed potential for risk stratification. Our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.

Teste de biocompatibilidade e resistência de membranas de látex em cães / Biocompatibility and resistance test of latex membranes in dogs

O látex natural extraído da seringueira (Hevea brasiliensis) possui propriedades indutoras de neovascularização e regeneração tecidual, comprovadas em várias espécies e em diferentes tecidos do organismo. Este estudo testou a biocompatibilidade e a resistência de três membranas de látex em seis cadelas, sendo duas dessas membranas ainda não utilizadas e uma já testada em estudos prévios. Os implantes foram colocados entre os músculos cutâneo e reto do abdome através de procedimento cirúrgico e, após 45 dias, foram removidos e submetidos a exames histológicos. Durante esse período, os animais foram avaliados quanto à dor e às alterações clínicas e macroscópicas nas membranas. Testes mecânicos de tração para determinação de deformação e resistência foram efetuados em amostras-controle. Pelos resultados obtidos, concluiu-se que as membranas testadas são compatíveis para substituir a bainha muscular em cães, exceto a membrana 2, por apresentar características de rejeição.

The natural latex extracted of the Rubber Tree, has properties to induce characteristics of tissue vascularization and regeneration that had been comproved in different species and tissues. This study tested the biocompatibility of three latex membranes in six dogs. Two of these membranes have not been used yet, while one of them has been tested in previous studies. Membranes were implanted between the cutaneous and the rectum of the abdomen muscles by a surgical procedure. In a forty five days period, the animals were evaluated for pain, clinical and macroscopic alterations of the membranes and, after the membranes were removed in order to submit them to histological exams. Mechanical traction tests were realized in control membranes to measure deformation and resistance. The results indicated that membranes are compatible, and able to substitute the muscular sheath in dogs, excepting membrane 2, because of it rejecting characteristics.

Peritonite em cães / Peritonitis in dogs

A peritonite em cães ainda é caracterizada como uma severa complicação de afecções na cavidade abdominal. Define-se a enfermidade como uma inflamação do peritônio, na maioria das vezes com prognóstico reservado, e que pode ser fatal. Ela se apresenta de diferentes formas, sendo a séptica a mais comum, em que microorganismos patogênicos proliferam rapidamente, e determinam processo infeccioso grave. Devido à importância da peritonite em cães, são abordados a etiopatogenia, os métodos diagnósticos e a conduta terapêutica mais apropriada. Para um prognóstico favorável, o diagnóstico precoce e o tratamento eficaz são fundamentais.

Peritonitis in dogs is still characterized as a severe complication of diseases in the abdominal cavity. Peritonitis is an inflammation of the peritoneum which most often is given an adverse prognostic with the potential of fatal evolution. It presents itself in three forms: aseptic, septic and combined. The septic form is the most common, in which the pathogenic microorganisms rapidly proliferate incurring a severe infectious process. Because of the great importance of this disease, the etiopathogeny, diagnosis methods, and more effective therapy was reviewed with emphasis in new forms of treatment. A favorable prognostic depends on the early diagnostic and the correct treatment for each occurring form.