83 research outputs found
Routine digital pathology workflow: The Catania experience
No full textIntroduction: Successful implementation of whole slide imaging (WSI) for routine clinical practice has been accomplished in only a few pathology laboratories worldwide. We report the transition to an effective and complete digital surgical pathology workflow in the pathology laboratory at Cannizzaro Hospital in Catania, Italy. Methods: All (100%) permanent histopathology glass slides were digitized at ×20 using Aperio AT2 scanners. Compatible stain and scanning slide racks were employed to streamline operations. eSlide Manager software was bidirectionally interfaced with the anatomic pathology laboratory information system. Virtual slide trays connected to the two-dimensional (2D) barcode tracking system allowed pathologists to confirm that they were correctly assigned slides and that all tissues on these glass slides were scanned. Results: Over 115,000 glass slides were digitized with a scan fail rate of around 1%. Drying glass slides before scanning minimized them sticking to scanner racks. Implementation required introduction of a 2D barcode tracking system and modification of histology workflow processes. Conclusion: Our experience indicates that effective adoption of WSI for primary diagnostic use was more dependent on optimizing preimaging variables and integration with the laboratory information system than on information technology infrastructure and ensuring pathologist buy-in. Implementation of digital pathology for routine practice not only leveraged the benefits of digital imaging but also creates an opportunity for establishing standardization of workflow processes in the pathology laboratory
SlideInspect: From Pixel-Level Artifact Detection to Actionable Quality Metrics in Digital Pathology
No full textThe presence of artifacts in whole slide images (WSIs), such as tissue folds, air bubbles, and out- of-focus regions, can significantly
impact WSI digitization, pathologists' evaluation, and the accuracy of downstream analyses. We present SlideInspect, a novel
AI-based framework for comprehensive artifact detection and quality control in digital pathology. Our system leverages deep
learning techniques to segment multiple artifact types across diverse tissue types and staining methods. SlideInspect provides a
hierarchical output: a color-coded slide quality indicator (green, yellow, red) with recommended actions (no action, re-scan, re-
mount, re-cut) based on artifact type and extent, and pixel-level segmentation masks for detailed analysis. The system operates at
multiple magnifications (1.25× for tissue segmentation, 5× for artifact detection) and also incorporates stain quality assessment
for histological stain evaluation. We validated SlideInspect on a large, multi-centric, multi-scanner dataset of over 3000 WSIs,
demonstrating robust performance across different tissue types, staining methods, and scanning platforms. The system achieves
high segmentation accuracy for various artifacts while maintaining computational efficiency (average processing time: 72.7 s per
WSI). Pathologist evaluations confirmed the clinical relevance and accuracy of SlideInspect's quality assessments. By providing
actionable insights at multiple levels of granularity, SlideInspect significantly improves the efficiency and standardization of
digital pathology workflows. Its vendor-agnostic design and multi-stain capability make it suitable for integration into diverse
clinical and research settings
Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study
No full textIn clinical routine, the quality of whole-slide images plays a key role in the pathologist’s diagnosis, and suboptimal staining may be a limiting factor. The stain normalization process helps to solve this problem through the standardization of color appearance of a source image with respect to a target image with optimal chromatic features. The analysis is focused on the evaluation of the following parameters assessed by two experts on original and normalized slides: (i) perceived color quality, (ii) diagnosis for the patient, (iii) diagnostic confidence and (iv) time required for diagnosis. Results show a statistically significant increase in color quality in the normalized images for both experts (p < 0.0001). Regarding prostate cancer assessment, the average times for diagnosis are significantly lower for normalized images than original ones (first expert: 69.9 s vs. 77.9 s with p < 0.0001; second expert: 37.4 s vs. 52.7 s with p < 0.0001), and at the same time, a statistically significant increase in diagnostic confidence is proven. The improvement of poor-quality images and greater clarity of diagnostically important details in normalized slides demonstrate the potential of stain normalization in the routine practice of prostate cancer assessment
Molecular status of PI3KCA, KRAS and BRAF in ovarian clear cell carcinoma: An analysis of 63 patients
No full textAims To evaluate the incidence of PI3KCA, KRAS and BRAF mutations in primary ovarian clear cell carcinoma (OCCC).
Methods 63 consecutive patients, with a proven diagnosis of OCCC, according to WHO criteria, were included into the study. Pyrosequencing analysis of all three genes hotspot regions were performed on 2.5 mu m sections of formalin-fixed paraffin-embedded tissue from primary OCCC.
Results PI3KCA mutations were found in 20/63 (32%) cases; KRAS mutations were found in 8/63 (13%); no BRAF V600 mutations were found. In particular, 12/20 mutations (60%) of PI3KCA were found in the exon 20, whereas the remaining eight cases presented mutations in exon 9 (8/20; 40%). KRAS pyrosequencing analysis revealed higher incidence of codon 12 mutations (7/8; 90%) than codon 13 mutations (1/8; 10%). In five cases (5/66; 8%), synchronous mutations, affecting PI3KCA and KRAS genes, were found. No differences were found in the distribution of hotspot mutations, according to the stage.
Conclusions The high frequency of PI3KCA mutations, the low rate of mutations in KRAS and the absence of mutations in BRAF, indicate a molecular signature of OCCCs different from other ovarian carcinomas. Detection of driver mutations, such as PI3KCA and KRAS, may be the basis for a targeted therapy, although the clinical and therapeutic implications of these findings have to be supported by further studies
Computer‐assisted urine cytology: Faster, cheaper, better?
No full text: 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. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes
Ovarian Clear Cell Carcinoma: From Morphology to Molecular Biology
No full textOvarian clear cell carcinoma (oCCC) is a distinctive subtype of ovarian carcinoma, with peculiar genetic and environmental risk factors, precursor lesions, molecular events during oncogenesis, patterns of spread, and response to treatment. Because of low response to chemotherapy and poor prognosis in advanced stages, there is growing interest in investigating the molecular pathways involved in oCCC development, in order to individualize novel/molecular targeted therapies. Until now, the main molecular genetic changes associated with oCCC remain to be identified, and, although several molecular changes have been reported in clear cell tumors, most studies have analyzed a limited number of cases; therefore, the true prevalence of those changes is not known. The present review will present the clinicopathologic features of oCCC, from morphology to molecular biology, discussing the diagnostic and treatment challenges of this intriguing ovarian carcinoma
The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board
No full textObjective: 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. Methods: On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education. Results: Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides. Conclusion: Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care
Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology
No full textTissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. In this review we will present how AI has been used to support Tissue Biomarkers evaluation in the specific field of Pathology, give an insight to the intriguing field of AI-based biomarkers and discuss possible advantages, risk and perspectives for Pathology
Not all stains are made equal: impact of stain normalization on prostate cancer diagnosis
No full textObjective: Stain normalization is a technique used to standardize the color appearance of digital whole slide images (WSIs). This study aimed to assess the impact of digital stain normalization on prostate cancer diagnosis by pathologists. Methods: A multi-institutional board of four pathologists evaluated 407 hematoxylin and eosin (H&E) prostate WSIs before and after stain normalization. The presence/absence of prostate adenocarcinoma, the Grade Groups as well as color quality perception and time required for diagnosis were recorded. Results: After normalization, color quality improved significantly for all pathologists (median scores increased from 4-6 to 7-8/10). Average diagnosis time decreased from 50s to 35s (p < 0.001). Inter-pathologist reproducibility for Gleason risk group showed a fair to good level of agreement, with an improvement after normalization. Conclusions: Stain normalization enhanced pathologists' diagnosis of prostate cancer by improving color standardization, reducing diagnosis time, and increasing inter-observer reproducibility. These findings highlight the potential of stain normalization to improve accuracy and efficiency in digital pathology
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