Multiparametric MRI targeted prostate biopsy: When omit systematic biopsy?

Introduction: To evaluate the detection rate for prostate cancer (PCa) performing multiparametric magnetic resonance imaging (mpMRI) fusion targeted biopsy (TPBx) combined only with ipsilateral systematic prostate biopsy (SPBx).

Materials And Methods: From January 2023 to December 2023, 495 men with clinical suspicion of PCa underwent transperineal SPBx plus TPBx in the presence of PI-RADS score lesions ≥ 3.

Results: In 250/495 men (50.

PSMA PET/CT Accuracy in Diagnosing Prostate Cancer Nodes Metastases.

Background/aim: This study aimed to evaluate the diagnostic accuracy of prostate-specific membrane antigen (PSMA)-directed positron emission tomography/computed tomography (PET/CT) in pelvic nodal staging, using postoperative histopathology data as the reference standard.

Patients And Methods: From January 2020 to June 2024, 78 patients with clinically significant prostate cancer (PCa) (ISUP Grade Group 2) underwent radical prostatectomy plus extended pelvic lymph node dissection (ePLND): 60 (77%) vs. 18 (23%) men had an intermediate vs.

Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.

The development of reliable artificial intelligence (AI) algorithms in pathology often depends on ground truth provided by annotation of whole slide images (WSI), a time-consuming and operator-dependent process. A comparative analysis of different annotation approaches is performed to streamline this process. Two pathologists annotated renal tissue using semi-automated (Segment Anything Model, SAM)) and manual devices (touchpad vs mouse).

Integrated diagnostics, complex biomarkers, and a new frontier for tissue pathology.

What will be the next disruptive technology that will change pathology's routine practice again? In this editorial we make a case for the need of more complex biomarkers in oncology diagnostics, to match the inherent complexity of cancer biology. This complexity will be achieved by the validation of technology able to generate more meaningful biological datapoints (epitomized in tissue pathology by technologies such as multiplex immunofluorescence) and, more important, by the systematic analysis of multimodal technology outputs with artificial intelligence tools, which is the essence of integrated diagnostics. While describing these processes, the authors highlight the pivotal role that histopathology will play, once again, in yet another transformation in diagnostics.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models.

Computer-assisted urine cytology: Faster, cheaper, better?

Recent advancements in computer-assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics.

Feasibility of MALDI-MSI-Based Proteomics Using Bouin-Fixed Pathology Samples: Untapping the Goldmine of Nephropathology Archives.

The application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts.

The biomarkers ATLAS: An audit on 1100 non-small cell lung cancer from an Italian knowledge-based database.

Aims: To date, precision medicine has revolutionized the clinical management of Non-Small Cell Lung Cancer (NSCLC). International societies approved a rapidly improved mandatory testing biomarkers panel for the clinical stratification of NSCLC patients, but harmonized procedures are required to optimize the diagnostic workflow. In this context a knowledge-based database (Biomarkers ATLAS, https://biomarkersatlas.

Digital odyssey: lessons learnt from a reverse transition from a digital to a manual pathology workflow.

In the fully digital Caltagirone pathology laboratory, a reverse shift from a digital to a manual workflow occurred due to a server outage in September 2023. Here, insights gained from this unplanned transition are explored. Surveying the affected pathologists and technicians revealed unanimous preferences for the time-saving and error-reducing capabilities of the digital methodology.

Exploring the Choice of Graft Materials in Tympanoplasty: A Perspective on the Use of Temporalis Fascia and Cartilage Grafts. Comment on Ferlito et al. Type 1 Tympanoplasty Outcomes between Cartilage and Temporal Fascia Grafts: A Long-Term Retrospective Study. 2022, , 7000.

I am writing this letter to discuss and share my thoughts on the recently published research comparing the outcomes of using temporalis fascia and cartilage grafts in type I tympanoplasty [...

Standardized and simplified reporting of next-generation sequencing results in advanced non-small-cell lung cancer: Practical indications from an Italian multidisciplinary group.

Molecular biomarker testing is increasingly becoming standard of care for advanced non-small cell lung cancer (NSCLC). Tissue and liquid biopsy-based next-generation sequencing (NGS) is now highly recommended and has become an integral part of the routine management of advanced NSCLC patients. This highly sensitive approach can simultaneously and efficiently detect multiple biomarkers even in scant samples.

Modelling digital health data: The ExaMode ontology for computational pathology.

Unlabelled: Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices.

Congo Red Staining in Digital Pathology: The Streamlined Pipeline for Amyloid Detection Through Congo Red Fluorescence Digital Analysis.

Renal amyloidosis is a rare condition caused by the progressive accumulation of misfolded proteins within glomeruli, vessels, and interstitium, causing functional decline and requiring prompt treatment due to its significant morbidity and mortality. Congo red (CR) stain on renal biopsy samples is the gold standard for diagnosis, but the need for polarized light is limiting the digitization of this nephropathology field. This study explores the feasibility and reliability of CR fluorescence on virtual slides (CRFvs) in evaluating the diagnostic accuracy and proposing an automated digital pipeline for its assessment.

The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board.

Objective: The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered.

Children under 6 years with acute headache in Pediatric Emergency Departments. A 2-year retrospective exploratory multicenter Italian study.

Background: Preschool age (i.e. children under six years of age) represents a red flag for requiring neuroimaging to exclude secondary potentially urgent intracranial conditions (PUIC) in patients with acute headache.

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition.

Spatial resolution of renal amyloid deposits through MALDI-MSI: a combined digital and molecular approach to monoclonal gammopathies.

Aims: Identification and characterisation of monoclonal gammopathies of renal significance (MGRS) is critical for therapeutic purposes. Amyloidosis represents one of the most common forms of MGRS, and renal biopsy remains the gold standard for their classification, although mass spectrometry has shown greater sensitivity in this area.

Methods: In the present study, a new in situ proteomic technique, matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), is investigated as an alternative to conventional laser capture microdissection MS for the characterisation of amyloids.

Improvements in digital pathology equipment for renal biopsies: updating the standard model.

Introduction: Digital pathology can improve the technical and interpretative workflows in nephropathology by creating hub-spoke networks and virtuous collaboration projects among centers in different geographical regions. New high-resolution fast-scanning instruments combined with currently existing equipment were tested in a nephropathology hub to evaluate possible upgrading in the routine processing phases.

Methods: The scanning performance of two different instruments (Aperio vs hybrid MIDI II) was evaluated and a comparative quality control check was performed on obtained whole slide images.

Data-driven color augmentation for H&E stained images in computational pathology.

Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

Background: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience.

Empowering digital pathology applications through explainable knowledge extraction tools.

Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET).