المؤلفون: Caputo, Alessandro, Fraggetta, Filippo, Cretella, Pasquale, Cozzolino, Immacolata, Eccher, Albino, Girolami, Ilaria, Marletta, Stefano, Troncone, Giancarlo, Vigliar, Elena, Acanfora, Gennaro, Zarra, Karen Villar, Torres Rivas, Héctor Enrique, Fadda, Guido, Field, Andrew, Katz, Ruth, Vielh, Philippe, Eloy, Catarina, Rajwanshi, Arvind, Gupta, Nalini, Al-Abbadi, Mousa, Bustami, Nadwa, Arar, Tala, Calaminici, Maria, Raine, Juliet I., Barroca, Helena, Canão, Pedro Amoroso, Ehinger, Mats, Rajabian, Nilofar, Dey, Pranab, Medeiros, L. Jeffrey, El Hussein, Siba, Lin, Oscar, D’Antonio, Antonio, Bode-Lesniewska, Beata, Rossi, Esther Diana, Zeppa, Pio

المصدر: Cancer Cytopathology. 131(11):679-692

مصطلحات موضوعية: digital cytopathology, lymph node, reproducibility, virtual microscopy, whole-slide imaging, Medicin och hälsovetenskap, Klinisk medicin, Cancer och onkologi, Medical and Health Sciences, Clinical Medicine, Cancer and Oncology

الوصف: Background: After a series of standardized reporting systems in cytopathology, the Sydney system was recently introduced to address the need for reproducibility and standardization in lymph node cytopathology. Since then, the risk of malignancy for the categories of the Sydney system has been explored by several studies, but no studies have yet examined the interobserver reproducibility of the Sydney system. Methods: The authors assessed interobserver reproducibility of the Sydney system on 85 lymph node fine-needle aspiration cytology cases reviewed by 15 cytopathologists from 12 institutions in eight different countries, resulting in 1275 diagnoses. In total, 186 slides stained with Diff-Quik, Papanicolaou, and immunocytochemistry were scanned. A subset of the cases included clinical data and results from ultrasound examinations, flow cytometry immunophenotyping, and fluorescence in situ hybridization analysis. The study participants assessed the cases digitally using whole-slide images. Results: Overall, the authors observed an almost perfect agreement of cytopathologists with the ground truth (median weighted Cohen κ = 0.887; interquartile range, κ = 0.210) and moderate overall interobserver concordance (Fleiss κ = 0.476). There was substantial agreement for the inadequate and malignant categories (κ = 0.794 and κ = 0.729, respectively), moderate agreement for the benign category (κ = 0.490), and very slight agreement for the suspicious (κ = 0.104) and atypical (κ = 0.075) categories. Conclusions: The Sydney system for reporting lymph node cytopathology shows adequate interobserver concordance. Digital microscopy is an adequate means to assess lymph node cytopathology specimens.

الوصول الحر: https://lup.lub.lu.se/record/9893b635-fe11-4a0d-96bc-b9727323bb6fTest
http://dx.doi.org/10.1002/cncy.22741Test

المؤلفون: Ammendola, Serena, Bariani, Elena, Eccher, Albino, Capitanio, Arrigo, Ghimenton, Claudio, Pantanowitz, Liron, Parwani, Anil, Girolami, Ilaria, Scarpa, Aldo, Barresi, Valeria

المصدر: Virchows Archiv. 478:747-756

مصطلحات موضوعية: Atypical meningioma, Whole slide imaging, Digital, Recurrence, Reproducibility

الوصف: Limited studies on whole slide imaging (WSI) in surgical neuropathology reported a perceived limitation in the recognition of mitoses. This study analyzed and compared the inter- and intra-observer concordance for atypical meningioma, using glass slides and WSI. Two neuropathologists and two residents assessed the histopathological features of 35 meningiomas-originally diagnosed as atypical-in a representative glass slide and corresponding WSI. For each histological parameter and final diagnosis, we calculated the inter- and intra-observer concordance in the two viewing modes and the predictive accuracy on recurrence. The concordance rates for atypical meningioma on glass slides and on WSI were 54% and 60% among four observers and 63% and 74% between two neuropathologists. The inter-observer agreement was higher using WSI than with glass slides for all parameters, with the exception of high mitotic index. For all histological features, we found median intra-observer concordance of >= 79% and similar predictive accuracy for recurrence between the two viewing modes. The higher concordance for atypical meningioma using WSI than with glass slides and the similar predictive accuracy for recurrence in the two modalities suggest that atypical meningioma may be safely diagnosed using WSI.

وصف الملف: electronic

الوصول الحر: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172503Test
https://liu.diva-portal.org/smash/get/diva2:1516918/FULLTEXT01.pdfTest

المؤلفون: Caldonazzi, Nicolò, Rizzo, Paola Chiara, Eccher, Albino, Girolami, Ilaria, Fanelli, Giuseppe Nicolò, Naccarato, Antonio Giuseppe, Bonizzi, Giuseppina, Fusco, Nicola, d'Amati, Giulia, Scarpa, Aldo, Pantanowitz, Liron, Marletta, Stefano

المساهمون: Caldonazzi, Nicolò, Rizzo, Paola Chiara, Eccher, Albino, Girolami, Ilaria, Fanelli, Giuseppe Nicolò, Naccarato, Antonio Giuseppe, Bonizzi, Giuseppina, Fusco, Nicola, D'Amati, Giulia, Scarpa, Aldo, Pantanowitz, Liron, Marletta, Stefano

مصطلحات موضوعية: artificial intelligence, digital pathology, lymph node, metastases

الوصف: Simple Summary In surgical pathology, the assessment of the presence of lymph node metastases is a key aspect in terms of the staging and prognosis of cancer patients. This type of work is time-consuming and prone to error. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic cells, so this task can be automated and standardized, increasing diagnostic quality. This manuscript aims to systematically review the published literature regarding the application of various artificial intelligence systems for the assessment of metastases in lymph nodes in whole slide images. One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.

العلاقة: info:eu-repo/semantics/altIdentifier/pmid/37173958; info:eu-repo/semantics/altIdentifier/wos/WOS:000986224600001; volume:15; issue:9; numberofpages:11; journal:CANCERS; https://hdl.handle.net/11573/1683776Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85159192072

الإتاحة: https://doi.org/10.3390/cancers15092491Test
https://hdl.handle.net/11573/1683776Test

المؤلفون: Antonini, Pietro, Santonicco, Nicola, Pantanowitz, Liron, Girolami, Ilaria, Rizzo, Paola Chiara, Brunelli, Matteo, Bellevicine, Claudio, Vigliar, Elena, Negri, Giovanni, Troncone, Giancarlo, Fadda, Guido, Parwani, Anil, Marletta, Stefano, Eccher, Albino

المساهمون: Antonini, Pietro, Santonicco, Nicola, Pantanowitz, Liron, Girolami, Ilaria, Rizzo, Paola Chiara, Brunelli, Matteo, Bellevicine, Claudio, Vigliar, Elena, Negri, Giovanni, Troncone, Giancarlo, Fadda, Guido, Parwani, Anil, Marletta, Stefano, Eccher, Albino

مصطلحات موضوعية: CAP guideline, WSI, cytopathology, digital pathology, systematic review, validation

الوصف: Whole slide imaging (WSI) allows pathologists to view virtual versions of slides on computer monitors. With increasing adoption of digital pathology, laboratories have begun to validate their WSI systems for diagnostic purposes according to reference guidelines. Among these the College of American Pathologists (CAP) guidelines include three strong recommendations (SRs) and nine good practice statements (GPSs). To date, the application of WSI to cytopathology has been out of scope of the CAP guideline due to limited evidence. Herein we systematically reviewed the published literature on WSI validation studies in cytology.

العلاقة: info:eu-repo/semantics/altIdentifier/pmid/36082410; info:eu-repo/semantics/altIdentifier/wos/WOS:000859463100001; volume:34; issue:1; firstpage:5; lastpage:14; numberofpages:10; journal:CYTOPATHOLOGY; https://hdl.handle.net/11380/1317428Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85138679752

الإتاحة: https://doi.org/10.1111/cyt.13178Test
https://hdl.handle.net/11380/1317428Test

المؤلفون: Girolami, Ilaria, Marletta, Stefano, Fiorentino, Vincenzo, Battocchio, Simonetta, Cerbelli, Bruna, Fiamengo, Barbara, Gerosa, Clara, Gianatti, Andrea, Morelli, Luca, Riva, Giulio, Zagami, Maria Giovanna, Fusco, Nicola, Munari, Enrico, L'Imperio, Vincenzo, Pagni, Fabio, Morbini, Patrizia, Martini, Maurizio, Eccher, Albino

المساهمون: Girolami, Ilaria, Marletta, Stefano, Fiorentino, Vincenzo, Battocchio, Simonetta, Cerbelli, Bruna, Fiamengo, Barbara, Gerosa, Clara, Gianatti, Andrea, Morelli, Luca, Riva, Giulio, Zagami, Maria Giovanna, Fusco, Nicola, Munari, Enrico, L'Imperio, Vincenzo, Pagni, Fabio, Morbini, Patrizia, Martini, Maurizio, Eccher, Albino

مصطلحات موضوعية: head and neck squamous cell carcinoma, immunohistochemistry, immunotherapy, programmed death-ligand 1, radio-chemotherapy, systematic review

الوصف: Background: Programmed death-ligand 1 (PD-L1) checkpoint inhibitors represent a mainstay of therapy in head and neck squamous cell cancer (HNSCC). However, little is known about the influence of combined therapy on PD-L1 expression. The study aims to gather evidence on this topic. Methods: A systematic search was carried out in electronic databases Pubmed-MEDLINE and Embase to retrieve studies on the comparison of PD-L1 expression before and after conventional therapy. Data were extracted and a quantitative analysis with pooled odds ratios (ORs) was performed when applicable. Results: Of 5688 items, 15 were finally included. Only a minority of studies assessed PD-L1 with the recommended combined positive score (CPS). The results are highly heterogeneous, with some studies reporting an increase in PD-L1 expression and others reporting a decrease. Three studies allowed for quantitative analysis and showed a pooled OR of 0.49 (CI 0.27-0.90). Conclusions: From the present evidence, a clear conclusion towards an increase or decrease in PD-L1 expression after combined therapy cannot be drawn, but even with few studies available, a trend towards an increase in expression in tumor cells at a cutoff of 1% can be noted in patients undergoing platinum-based therapy. Future studies will provide more robust data on the effect of combined therapy on PD-L1 expression.

العلاقة: info:eu-repo/semantics/altIdentifier/pmid/36836595; info:eu-repo/semantics/altIdentifier/wos/WOS:000940055200001; volume:13; issue:2; firstpage:1; lastpage:12; numberofpages:12; journal:JOURNAL OF PERSONALIZED MEDICINE; https://hdl.handle.net/11380/1317413Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85148937825

الإتاحة: https://doi.org/10.3390/jpm13020363Test
https://hdl.handle.net/11380/1317413Test

المؤلفون: Cazzaniga, Giorgio, Rossi, Mattia, Eccher, Albino, Girolami, Ilaria, L’Imperio, Vincenzo, Van Nguyen, Hien, Becker, Jan Ulrich, Bueno García, María Gloria, Sbaraglia, Marta, Dei Tos, Angelo Paolo, Gambaro, Giovanni, Pagni, Fabio

المساهمون: Università degli Studi di Milano - Bicocca

المصدر: Journal of Nephrology ; ISSN 1724-6059

مصطلحات موضوعية: Nephrology

الوصف: Introduction Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. Methods Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included. Results Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification. Conclusion Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools. Graphical abstract

الإتاحة: https://doi.org/10.1007/s40620-023-01775-wTest

المؤلفون: Eccher, Albino, Antonini, Pietro, Barreca, Antonella, Fabbrizio, Benedetta, Boggi, Ugo, Rizzo, Paola Chiara, Girolami, Ilaria

المساهمون: Università degli Studi di Verona

المصدر: Current Transplantation Reports ; volume 10, issue 2, page 40-50 ; ISSN 2196-3029

مصطلحات موضوعية: Transplantation, Nephrology, Hepatology, Immunology, Surgery

الوصف: Although controversial, procurement kidney biopsies and histology are commonly used in kidney allocation from deceased donors. The long series of models developed for this question, incorporating a variety of clinical and histologic variables, failed to properly predict the long-term graft survival. This failure could be explained by many factors, including the lack of expertise in terms of skilled available nephropathologists in the urgent setting of biopsies assessment. Simulation-based learning is a form of experiential learning that provides learners with a real-world-like opportunity to develop and practice their knowledge and skills but in a simulated environment. Digital pathology with whole-slide imaging is a powerful tool for knowledge delivering, as it offers the opportunity to facilitate meeting of general pathologists with experts, with availability of second opinion consultation and tailored training on specific cases. In the back of these considerations, we report on the content of the web-meeting “Digital slide and simulation-based learning in pre-implantation kidney” which was fully dedicated to the evaluation of pre-implantation kidney biopsy, with a very practical approach and a direct interaction between two expert renal transplant pathologists and the audience of general pathologists.

الإتاحة: https://doi.org/10.1007/s40472-023-00392-7Test

المؤلفون: Rizzo, Paola Chiara, Caputo, Alessandro, Maddalena, Eddy, Caldonazzi, Nicolò, Girolami, Ilaria, Dei Tos, Angelo Paolo, Scarpa, Aldo, Sbaraglia, Marta, Brunelli, Matteo, Gobbo, Stefano, Marletta, Stefano, Pantanowitz, Liron, Della Mea, Vincenzo, Eccher, Albino

المساهمون: Associazione Italiana per la Ricerca sul Cancro

المصدر: DIGITAL HEALTH ; volume 9 ; ISSN 2055-2076 2055-2076

مصطلحات موضوعية: Health Information Management, Computer Science Applications, Health Informatics, Health Policy

الوصف: Objective Digital pathology (DP) is currently in the spotlight and is rapidly gaining ground, even though the history of this field spans decades. Despite great technological progress, the adoption of DP for routine clinical diagnostic use remains limited. Methods A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all published studies that encompassed any application of DP. Results Of 4888 articles retrieved, 4041 were included. Relevant articles were categorized as “diagnostic” (147/4041, 4%) where DP was utilized for routine diagnostic workflow and “non-diagnostic” (3894/4041, 96%) for all other applications. The “non-diagnostic” articles were further categorized according to DP application including “artificial intelligence” (33%), “education” (5%), “narrative” (17%) for reviews and editorials, and “technical” (45%) for pure research publications. Conclusion This manuscript provided temporal and geographical insight into the global adoption of DP by analyzing the published scientific literature.

الإتاحة: https://doi.org/10.1177/20552076231194551Test

المؤلفون: Marletta, Stefano, Eccher, Albino, Martelli, Filippo Maria, Santonicco, Nicola, Girolami, Ilaria, Scarpa, Aldo, Pagni, Fabio, L'Imperio, Vincenzo, Pantanowitz, Liron, Gobbo, Stefano, Seminati, Davide, Tos, Angelo Paolo Dei, Parwani, Anil

المصدر: American Journal of Clinical Pathology; Jun2024, Vol. 161 Issue 6, p526-534, 9p

مصطلحات موضوعية: ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, CANCER diagnosis, ALGORITHMS, EARLY detection of cancer

مستخلص: Objectives The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. Methods A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. Results Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. Conclusions The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential. [ABSTRACT FROM AUTHOR]

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المؤلفون: Marletta, Stefano, Pantanowitz, Liron, Santonicco, Nicola, Caputo, Alessandro, Bragantini, Emma, Brunelli, Matteo, Girolami, Ilaria, Eccher, Albino

المساهمون: Marletta, Stefano, Pantanowitz, Liron, Santonicco, Nicola, Caputo, Alessandro, Bragantini, Emma, Brunelli, Matteo, Girolami, Ilaria, Eccher, Albino

مصطلحات موضوعية: Letter

العلاقة: info:eu-repo/semantics/altIdentifier/pmid/38468494; info:eu-repo/semantics/altIdentifier/wos/WOS:001183319000001; volume:Online ahead of print; issue:Mar 11; firstpage:1; lastpage:2; numberofpages:2; journal:PEDIATRIC AND DEVELOPMENTAL PATHOLOGY; https://hdl.handle.net/11562/1121789Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85187430494

الإتاحة: https://doi.org/10.1177/10935266231225791Test
https://hdl.handle.net/11562/1121789Test