Matches in SemOpenAlex for { <https://semopenalex.org/work/W2766591710> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W2766591710 endingPage "1461" @default.
- W2766591710 startingPage "1460" @default.
- W2766591710 abstract "We read with considerable interest the contribution written by Alton Brad Farris, MD, and colleagues1 in the April 2017 issue of the Archives of Pathology & Laboratory Medicine, entitled “Whole Slide Imaging for Analytical Anatomic Pathology and Telepathology: Practical Applications Today, Promises, and Perils.” They have led us, a transnational group of closely collaborating pathologists, urologists, oncologists, engineers, and informaticians, to some considerations on our past and current role in this new era of digitalization of glass slides, that is, whole slide imaging (WSI) of genitourinary neoplasms, in particular, of prostate pathology.2,3This new era of WSI requires knowledge of previous studies that contributed to the current use and role of virtual slides and quantitative tissue analysis, for instance, in prostate cancer detection and grading, as well as in characterization of high-grade prostatic intraepithelial neoplasia and malignancy-associated changes.2 In the past 20 to 30 years, the technologic advancements have reached the point that we can obtain a virtual slide in the range of megabytes to gigabytes accessible, even in tablets and cell phones, for sharing, as well as for joint evaluation in a multidisciplinary setting, including quantitative image analysis.2 Evaluation of prostate histology on virtual slides can offer clues to the diagnostic classification and prognosis and the prediction of response to treatment. To facilitate the collection of quantitative data, machine-vision systems have been developed and Bayesian belief networks and neural networks have been used as diagnostic decision-support systems.2 All these approaches can be seen as the basis for simultaneous, quantitative evaluation of several tissue markers, including immunohistochemical and molecular patterns, in a multiplex system.2There are several advantages currently associated with the digitalization of glass slides in this new era of WSI, including image sharing for teaching, consultation, remote interpretation, and quality assurance.4,5 Additional features are “interactive publication” (similar to online, scientific chats) and image analysis (readers might use measurement systems available in the Web).6 The whole point of a journal article based on WSI is education, that is, the transfer of knowledge, experience, and guidance.7 In our experience, WSI forms an ideal basis for sound communication and represents a major component in medical diagnosis and treatment.There are 2 basic types of glass slides, based on their size and, therefore, of virtual slides derived from them. The most common is the glass slide from material processed in a regular tissue cassette (dimensions, 30 × 25 × 4 mm). The other, far less common, is the glass slide from material processed with a large tissue cassette or megacassette (dimensions, 63 × 47 × 11 mm). The latter is also called large format histology (LFH) or whole mount sections.8 We have applied LFH to basically all types of surgical specimens of bladder, testis, kidney, adrenal gland, penis, and prostate, including their lymphadenectomy. Virtual slides can be obtained from both types of glass slides. The WSI is traditionally based on slides from regular tissue cassettes. In the past few years, we have been able to scan several whole mount sections with a commercially available slide scanner (Figure). This has allowed us to acquire a unique experience in the joint evaluation with clinicians of virtual slides from large-format histology.9,10 Our experience of LFH and WSI is basically related to the field of uropathology. However, it can be performed in all body organs and neoplasms, including breast and its cancers,11 in which LFH can be adopted. To our knowledge, not all commercially available slide scanners allow the users to obtain virtual slides from whole mount sections. However, because of advantages with LFH and WSI,2 we foresee that all the vendors will update their scanners for the purpose of WSI of LFH.The importance of discussing the histologic findings present in radical prostatectomy specimens examined with the whole mount technique and/or with virtual slides has been dealt with in a contribution in European Urology, entitled “Joint Appraisal of the Radical Prostatectomy Specimen by the Urologist and the Uropathologist: Together, We Can Do It Better.”9 In particular, information on the following features with paramount clinical significance has been collected: quality indicators of the surgical procedure, specimen integrity, including missing parts, capsular incision into tumor, and benign glands at the surgical margins; and comparison of pathologic findings with digital rectal examination, transrectal ultrasound, and prostate biopsies findings. An example of this can be seen in a recent publication10 with a virtual slide of the whole mount section of a radical prostatectomy specimen with a periprostatic lymph node showing a metastatic deposit. A high-resolution version of the slide for use with a virtual microscope was available as an eSlide.10The LFH would benefit most from telepathology because of the size constraints and fragility of the larger slides via snail mail. Integrating the LFH slide virtually into the diagnosis of the specimen, with comparisons to the radiologic images, could have implications for diagnosis and staging.2Major advantages associated with digitalization of whole mount sections are not only consultation and remote interpretation, including image analysis, but also the direct integration with data derived, for instance, from surgery and other imaging techniques, such as multiparametric magnetic resonance imaging.12 All of this requires combining and integrating our knowledge of uropathology and uro-oncology with engineering and informatics.3,13 In particular, the integration of LFH-based virtual slides in a more-complex inference framework based on Bayesian belief or neural networks could be a useful tool in the process of sharing and formalizing diagnostic criteria and procedures among various clinicians. The process of translating expert knowledge into such models could also prove a valuable training tool by stimulating the identification of cause-effect links that drive the diagnostic process.In conclusion, the process of merging multiple data and knowledge from disparate sources is what, in nonmedical fields, is called multicriteria decision making and information fusion.13 Information fusion techniques are widely applied in all those fields, such as robotics, in which a vast amount of data need to be contextualized and interpreted as high-level information, such as multiple, concurrent criteria considered in autonomous, multicriteria decision making. The resulting information and diagnostic and therapeutic approaches, when applied to prostate cancer, for instance, examined with whole slide imaging of large format histology, can be more accurate and have less uncertainty than when the sources are evaluated separately and/or individually. The same can be seen in medical fields other than histopathology, such as radiology and even surgery. There are clinically oriented image evaluation and analysis approaches that are common across all medical specialties.3,13" @default.
- W2766591710 created "2017-11-10" @default.
- W2766591710 creator A5012233019 @default.
- W2766591710 creator A5030533194 @default.
- W2766591710 creator A5058161742 @default.
- W2766591710 creator A5061623935 @default.
- W2766591710 creator A5063109707 @default.
- W2766591710 creator A5063795761 @default.
- W2766591710 creator A5071106037 @default.
- W2766591710 creator A5091054206 @default.
- W2766591710 date "2017-11-01" @default.
- W2766591710 modified "2023-09-26" @default.
- W2766591710 title "Whole Slide Imaging of Large Format Histology in Prostate Pathology: Potential for Information Fusion" @default.
- W2766591710 cites W1988195092 @default.
- W2766591710 cites W2050452966 @default.
- W2766591710 cites W2081673917 @default.
- W2766591710 cites W2111574404 @default.
- W2766591710 cites W2124902026 @default.
- W2766591710 cites W2144946698 @default.
- W2766591710 cites W2342612957 @default.
- W2766591710 cites W2567120720 @default.
- W2766591710 cites W2585204983 @default.
- W2766591710 cites W2610656258 @default.
- W2766591710 doi "https://doi.org/10.5858/arpa.2017-0198-le" @default.
- W2766591710 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29072947" @default.
- W2766591710 hasPublicationYear "2017" @default.
- W2766591710 type Work @default.
- W2766591710 sameAs 2766591710 @default.
- W2766591710 citedByCount "13" @default.
- W2766591710 countsByYear W27665917102019 @default.
- W2766591710 countsByYear W27665917102020 @default.
- W2766591710 countsByYear W27665917102021 @default.
- W2766591710 countsByYear W27665917102022 @default.
- W2766591710 countsByYear W27665917102023 @default.
- W2766591710 crossrefType "journal-article" @default.
- W2766591710 hasAuthorship W2766591710A5012233019 @default.
- W2766591710 hasAuthorship W2766591710A5030533194 @default.
- W2766591710 hasAuthorship W2766591710A5058161742 @default.
- W2766591710 hasAuthorship W2766591710A5061623935 @default.
- W2766591710 hasAuthorship W2766591710A5063109707 @default.
- W2766591710 hasAuthorship W2766591710A5063795761 @default.
- W2766591710 hasAuthorship W2766591710A5071106037 @default.
- W2766591710 hasAuthorship W2766591710A5091054206 @default.
- W2766591710 hasBestOaLocation W27665917101 @default.
- W2766591710 hasConcept C121608353 @default.
- W2766591710 hasConcept C126322002 @default.
- W2766591710 hasConcept C142724271 @default.
- W2766591710 hasConcept C2776235491 @default.
- W2766591710 hasConcept C57742111 @default.
- W2766591710 hasConcept C71924100 @default.
- W2766591710 hasConceptScore W2766591710C121608353 @default.
- W2766591710 hasConceptScore W2766591710C126322002 @default.
- W2766591710 hasConceptScore W2766591710C142724271 @default.
- W2766591710 hasConceptScore W2766591710C2776235491 @default.
- W2766591710 hasConceptScore W2766591710C57742111 @default.
- W2766591710 hasConceptScore W2766591710C71924100 @default.
- W2766591710 hasIssue "11" @default.
- W2766591710 hasLocation W27665917101 @default.
- W2766591710 hasLocation W27665917102 @default.
- W2766591710 hasOpenAccess W2766591710 @default.
- W2766591710 hasPrimaryLocation W27665917101 @default.
- W2766591710 hasRelatedWork W2009680238 @default.
- W2766591710 hasRelatedWork W2096168011 @default.
- W2766591710 hasRelatedWork W2101884351 @default.
- W2766591710 hasRelatedWork W2105867074 @default.
- W2766591710 hasRelatedWork W2125893551 @default.
- W2766591710 hasRelatedWork W2372550570 @default.
- W2766591710 hasRelatedWork W2408693058 @default.
- W2766591710 hasRelatedWork W2419190835 @default.
- W2766591710 hasRelatedWork W2766590933 @default.
- W2766591710 hasRelatedWork W4313334971 @default.
- W2766591710 hasVolume "141" @default.
- W2766591710 isParatext "false" @default.
- W2766591710 isRetracted "false" @default.
- W2766591710 magId "2766591710" @default.
- W2766591710 workType "article" @default.