Search results for "Digital pathology"

SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images.

2021

High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CN…

Real-world digital pathology: considerations and ruminations of four young pathologists

2022

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Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease

2021

Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic liver disease (74% nonalcoholic fatty liver disease-NAFLD, 26% chronic hepatitis-CH etiologies) was performed. Inflammation was graded according to the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network system and METAVIR score. Whole-slide digital image analysis based on quantitative (I-score: inflammation ratio) and morphometric (C-sco…

Different methods of cell quantification can lead to different results: a comparison of digital methods using a pilot study of dendritic cells in HIV…

2019

Background Although new digital pathology tools have improved the positive cell quantification, there is a heterogeneity of the quantification methods in the literature. The aim of this study was to evaluate and propose a novel dendritic cells quantification method in squamous cell carcinoma comparing it with a conventional quantification method. Material and Methods Twenty-six squamous cell carcinomas HIV-positive cases affecting the oropharynx, lips and oral cavity were selected. Immunohistochemistry for CD1a, CD83, and CD207 was performed. The immunohistochemical stains were evaluated by automated examination using a positive pixel count algorithm. A conventional quantification method (u…

The impact of COVID-19 on the practice of Oral and Maxillofacial Pathology in the United States and Canada.

2022

The COVID-19 pandemic has significantly disrupted the delivery of healthcare, including oral healthcare services. The restrictions imposed for mitigating spread of the virus forced dental practitioners to adopt significant changes in their workflow pattern. The aim of this study was to investigate the impact of the pandemic on the practice of oral and maxillofacial pathology in two countries in regard to educational activities, and clinical and diagnostic pathology services.An online questionnaire was distributed to oral and maxillofacial pathologists in the United States and Canada. The survey was designed by combining dichotomous, multiple choice, and Likert response scale questions. Stat…

Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma

2021

BACKGROUND: Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification…

Deep learning for diagnosis and survival prediction in soft tissue sarcoma.

2021

Background Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS. Patients and methods Our retrospective, multicenter study included a total of 506 histopathological slides from 291 patients with STS. The Cancer Genome Atlas cohort (240 patients) served as training and validation set. A second, multicenter cohort (51 patients) served as an additional test set. The use of the DL model (DLM) as a clinical decision support system was evaluated by nine pathologists with different levels of expertise. For prognosis prediction, 139 slides from 85 patients with leiomyosarcom…

Digitalisering i patologi : Hvilke faktorer mener patologer har størst betydning ved implementering av digital patologi?

2019

Masteroppgave helse- og sosialinformatikk HSI500 - Universitetet i Agder 2019 Diagnostic pathology is essential for diagnosing cancer and often also in guiding choice of treatment method. However many claim it has become a bottleneck within cancer treatment, due partly to the fact that the number of pathologists is not expanding in line with the increasing workload. Digital pathology (DP) is considered to be the solution for increased efficiency within the field of pathology.The implementation of health information systems is complex and involves technical, human and organisational aspects, and information systems of all types have consistently failed to deliver expected gains.In this study…

2015

Background Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. Methods and Results In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively o…

Vitronectin as a molecular player of the tumor microenvironment in neuroblastoma

2019

Background Vitronectin is a multifunctional glycoprotein known in several human tumors for its adhesive role in processes such as cell growth, angiogenesis and metastasis. In this study, we examined vitronectin expression in neuroblastoma to investigate whether this molecule takes part in cell-cell or cell-extracellular matrix interactions that may confer mechanical properties to promote tumor aggressiveness. Methods We used immunohistochemistry and image analysis tools to characterize vitronectin expression and to test its prognostic value in 91 neuroblastoma patients. To better understand the effect of vitronectin, we studied its in vitro expression using commercial neuroblastoma cell lin…