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  1. Book ; Online ; E-Book: Whole slide imaging

    Parwani, Anil V.

    current applications and future directions

    2022  

    Author's details Anil V. Parwani, editor
    Keywords Diagnostic imaging/Digital techniques ; Pathology/Technological innovations ; Pathology
    Subject code 616.0754
    Language English
    Size 1 online resource (253 pages)
    Publisher Springer
    Publishing place Cham, Switzerland
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 3-030-83332-1 ; 3-030-83331-3 ; 978-3-030-83332-9 ; 978-3-030-83331-2
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book: Pathology informatics

    Parwani, Anil V.

    (Clinics in laboratory medicine ; volume 36, number 1 (March 2016))

    2016  

    Author's details editor Anil V. Parwani
    Series title Clinics in laboratory medicine ; volume 36, number 1 (March 2016)
    Collection
    Language English
    Size ix, 197 Seiten, Illustrationen
    Publisher Elsevier
    Publishing place Philadelphia, Pennsylvania
    Publishing country United States
    Document type Book
    HBZ-ID HT018951297
    ISBN 978-0-323-44408-8 ; 0-323-44408-3
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 1695 -36,1- not available for loan Zeitschriften-Lesesaal

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  3. Book: Practical informatics for cytopathology

    Pantanowitz, Liron / Parwani, Anil V.

    (Essentials in cytopathology)

    2014  

    Author's details Liron Pantanowitz ; Anil V. Parwani ed
    Series title Essentials in cytopathology
    Language English
    Size XVI, 204 S. : Ill., graph. Darst., Kt.
    Publisher Springer
    Publishing place New York u.a.
    Publishing country United States
    Document type Book
    HBZ-ID HT018173247
    ISBN 978-1-4614-9580-2 ; 1-4614-9580-6 ; 9781461495819 ; 1461495814
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2014 A 773 available for loan (reading room) Lesesaal EG

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  4. Article ; Online: Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications.

    Satturwar, Swati / Parwani, Anil V

    Advances in anatomic pathology

    2024  Volume 31, Issue 2, Page(s) 136–144

    Abstract: In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that ... ...

    Abstract In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.
    MeSH term(s) Male ; Humans ; Prostate ; Artificial Intelligence ; Retrospective Studies ; Prostatic Neoplasms ; Algorithms
    Language English
    Publishing date 2024-01-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1212493-x
    ISSN 1533-4031 ; 1072-4109
    ISSN (online) 1533-4031
    ISSN 1072-4109
    DOI 10.1097/PAP.0000000000000425
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4118: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article: Cytomorphology of papillary renal neoplasm with reverse polarity.

    Satturwar, Swati / Parwani, Anil V

    CytoJournal

    2023  Volume 20, Page(s) 43

    Abstract: Papillary renal neoplasm with reverse nuclear polarity (PRNRP) is an emerging oncocytic renal tumor. Cytomorphologic features of this tumor have not been described in the literature before. The objective of this study was to review the cytomorphology of ... ...

    Abstract Papillary renal neoplasm with reverse nuclear polarity (PRNRP) is an emerging oncocytic renal tumor. Cytomorphologic features of this tumor have not been described in the literature before. The objective of this study was to review the cytomorphology of a case PRNRP and compare with cytomorphologic features of papillary renal cell carcinomas (pRCCs) reported in the literature. 1 case of core needle biopsy (CNB) with touch preparation (TP) of a renal mass diagnosed as PRNRP was reviewed retrospectively. Clinical presentation, cytomorphologic features, ancillary tests and histopathology results were analyzed. The touch preparation was cellular and showed tight 3-D clusters of cuboidal epithelial cells with variable presence of fibrovascular cores (FC), granular eosinophilic cytoplasm, round apically located grade 1 nuclei compared to cases of pRCC that consistently showed presence of FCs lined by cuboidal to columnar epithelial cells with variable degree of cytologic atypia. Features characteristic of pRCC like foamy macrophages, hemosiderin laden macrophages, nuclear grooves or psammoma bodies were not present. No necrosis or mitosis were identified. By immunohistochemistry (IHC) the tumor cells were positive for cytokeratin 7, GATA-3 and AMACR (focal) and negative for CA-IX, CD117 and vimentin. Cytomorphologic features of PRNRP are unique and characterized by tight 3-D clusters (with or without FCs) of cuboidal cells with small round apically located nuclei and finely granular oncocytic cytoplasm. Specific diagnosis of PRNRP on cytology or CNB is feasible along with use of ancillary tests IHC and /or molecular tests.
    Language English
    Publishing date 2023-11-23
    Publishing country United States
    Document type Case Reports
    ZDB-ID 2158838-7
    ISSN 1742-6413 ; 0974-5963
    ISSN (online) 1742-6413
    ISSN 0974-5963
    DOI 10.25259/Cytojournal_9_2023
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Artificial intelligence in diagnostic pathology.

    Shafi, Saba / Parwani, Anil V

    Diagnostic pathology

    2023  Volume 18, Issue 1, Page(s) 109

    Abstract: Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools ... ...

    Abstract Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.
    MeSH term(s) Male ; Humans ; Artificial Intelligence ; Diagnostic Imaging/methods ; Pathology, Clinical ; Prostate ; Neoplasms
    Language English
    Publishing date 2023-10-03
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2210518-9
    ISSN 1746-1596 ; 1746-1596
    ISSN (online) 1746-1596
    ISSN 1746-1596
    DOI 10.1186/s13000-023-01375-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Artificial intelligence's impact on breast cancer pathology: a literature review.

    Soliman, Amr / Li, Zaibo / Parwani, Anil V

    Diagnostic pathology

    2024  Volume 19, Issue 1, Page(s) 38

    Abstract: This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key ... ...

    Abstract This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI's full potential in BC pathology. Despite the existing hurdles, AI's multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI's transformative capabilities in breast cancer diagnosis and assessment.
    MeSH term(s) Humans ; Artificial Intelligence ; Reproducibility of Results ; Triple Negative Breast Neoplasms ; Neural Networks, Computer ; Lymphatic Metastasis
    Language English
    Publishing date 2024-02-22
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2210518-9
    ISSN 1746-1596 ; 1746-1596
    ISSN (online) 1746-1596
    ISSN 1746-1596
    DOI 10.1186/s13000-024-01453-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Overcoming barriers to digital pathology.

    Parwani, Anil

    MLO: medical laboratory observer

    2018  Volume 48, Issue 9, Page(s) 38

    MeSH term(s) Attitude of Health Personnel ; Attitude to Computers ; Computer Literacy ; Humans ; Image Interpretation, Computer-Assisted ; Pathology/education ; Pathology/instrumentation ; Pathology/trends
    Language English
    Publishing date 2018-07-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 603205-9
    ISSN 0580-7247
    ISSN 0580-7247
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 2587: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Catechism (Quiz 16).

    Challa, Bindu / Parwani, Anil

    Indian journal of pathology & microbiology

    2022  Volume 65, Issue 2, Page(s) 516–518

    Language English
    Publishing date 2022-04-18
    Publishing country India
    Document type Journal Article
    ZDB-ID 197621-7
    ISSN 0974-5130 ; 0377-4929
    ISSN (online) 0974-5130
    ISSN 0377-4929
    DOI 10.4103/ijpm.ijpm_995_21
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 862: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  10. Book: Breast cytopathology

    Ali, Syed Z. / Parwani, Anil V.

    (Essentials in cytopathology series ; 4)

    2007  

    Author's details Syed Z. Ali ; Anil V. Parwani
    Series title Essentials in cytopathology series ; 4
    Collection
    Keywords Breast Diseases / diagnosis ; Breast / cytology ; Cytodiagnosis / methods
    Language English
    Size XV, 175 S. : zahlr. Ill.
    Publisher Springer
    Publishing place New York, NY
    Publishing country United States
    Document type Book
    HBZ-ID HT015542503
    ISBN 978-0-387-71594-0 ; 0-387-71594-0 ; 9780387715957 ; 0387715959
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2011 A 4651 order for loan Magazin

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