Literature DB >> 30778169

Whole slide imaging equivalency and efficiency study: experience at a large academic center.

Matthew G Hanna1, Victor E Reuter1,2, Meera R Hameed1,2, Lee K Tan1, Sarah Chiang1, Carlie Sigel1, Travis Hollmann1, Dilip Giri1, Jennifer Samboy1,2, Carlos Moradel1, Andrea Rosado1, John R Otilano1, Christine England1, Lorraine Corsale1, Evangelos Stamelos1, Yukako Yagi1,2, Peter J Schüffler1,2, Thomas Fuchs1,2, David S Klimstra1,2, S Joseph Sirintrapun3,4.   

Abstract

Whole slide imaging is Food and Drug Administration-approved for primary diagnosis in the United States of America; however, relatively few pathology departments in the country have fully implemented an enterprise wide digital pathology system enabled for primary diagnosis. Digital pathology has significant potential to transform pathology practice with several published studies documenting some level of diagnostic equivalence between digital and conventional systems. However, whole slide imaging also has significant potential to disrupt pathology practice, due to the differences in efficiency of manipulating digital images vis-à-vis glass slides, and studies on the efficiency of actual digital pathology workload are lacking. Our randomized, equivalency and efficiency study aimed to replicate clinical workflow, comparing conventional microscopy to a complete digital pathology signout using whole slide images, evaluating the equivalency and efficiency of glass slide to whole slide image reporting, reflective of true pathology practice workloads in the clinical setting. All glass slides representing an entire day's routine clinical signout workload for six different anatomic pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on Leica Aperio AT2 at ×40 (0.25 µm/pixel). Integration of whole slide images for each accessioned case is through an interface between the Leica eSlide manager database and the laboratory information system, Cerner CoPathPlus. Pathologists utilized a standard institution computer workstation and viewed whole slide images through an internally developed, vendor agnostic whole slide image viewer, named the "MSK Slide Viewer". Subspecialized pathologists first reported on glass slides from surgical pathology cases using routine clinical workflow. Glass slides were de-identified, scanned, and re-accessioned in the laboratory information system test environment. After a washout period of 13 weeks, pathologists reported the same clinical workload using whole slide image integrated within the laboratory information system. Intraobserver equivalency metrics included top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and the need to order ancillary testing (i.e., recuts, immunohistochemistry). Turnaround time (efficiency) evaluation was defined by the start of each case when opened in the laboratory information system and when the case was completed for that day (i.e., case sent to signout queue or pending ancillary studies). Eight pathologists participated from the following subspecialties: bone and soft tissue, genitourinary, gastrointestinal, breast, gynecologic, and dermatopathology. Glass slides signouts comprised of 204 cases, encompassing 2091 glass slides; and digital signouts comprised of 199 cases, encompassing 2073 whole slide images. The median whole slide image file size was 1.54 GB; scan time/slide, 6 min 24 s; and scan area 32.1 × 18.52 mm. Overall diagnostic equivalency (e.g., top-line diagnosis) was 99.3% between digital and glass slide signout; however, signout using whole slide images showed a median overall 19% decrease in efficiency per case. No significant difference by reader, subspecialty, or specimen type was identified. Our experience is the most comprehensive study to date and shows high intraobserver whole slide image to glass slide equivalence in reporting of true clinical workflows and workloads. Efficiency needs to improve for digital pathology to gain more traction among pathologists.

Entities:  

Mesh:

Year:  2019        PMID: 30778169     DOI: 10.1038/s41379-019-0205-0

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  38 in total

1.  Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study.

Authors:  Serdar Altinay; Laurent Arnould; Noella Bletard; Cecile Colpaert; Franceska Dedeurwaerdere; Benjamin Dessauvagie; Valérie Duwel; Giuseppe Floris; Stephen Fox; Clara Gerosa; Shabnam Jaffer; Eline Kurpershoek; Magali Lacroix-Triki; Andoni Laka; Kathleen Lambein; Gaëtan Marie MacGrogan; Caterina Marchió; Dolores Martin Martinez; Sharon Nofech-Mozes; Dieter Peeters; Alberto Ravarino; Emily Reisenbichler; Erika Resetkova; Souzan Sanati; Anne-Marie Schelfhout; Vera Schelfhout; Abeer M Shaaban; Renata Sinke; Claudia Maria Stanciu-Pop; Claudia Stobbe; Carolien H M van Deurzen; Koen Van de Vijver; Anne-Sophie Van Rompuy; Stephanie Verschuere; Anne Vincent-Salomon; Hannah Wen; Hélène Dano; Caroline Bouzin; Christine Galant; Mieke R Van Bockstal
Journal:  Mod Pathol       Date:  2019-09-18       Impact factor: 7.842

Review 2.  Integrating digital pathology into clinical practice.

Authors:  Matthew G Hanna; Orly Ardon; Victor E Reuter; Sahussapont Joseph Sirintrapun; Christine England; David S Klimstra; Meera R Hameed
Journal:  Mod Pathol       Date:  2021-10-01       Impact factor: 7.842

3.  Rapid Histological Assessment of Prostate Specimens in the Three-dimensional Space by Hydrophilic Tissue Clearing and Confocal Microscopy.

Authors:  Yu-Ching Peng; Yu-Chieh Lin; Yu-Ling Hung; Chien-Chung Fu; Margaret Dah-Tsyr Chang; Yen-Yin Lin; Teh-Ying Chou
Journal:  J Histochem Cytochem       Date:  2022-07-30       Impact factor: 4.137

4.  A low-cost pathological image digitalization method based on 5 times magnification scanning.

Authors:  Kai Sun; Yanhua Gao; Ting Xie; Xun Wang; Qingqing Yang; Le Chen; Kuansong Wang; Gang Yu
Journal:  Quant Imaging Med Surg       Date:  2022-05

5.  Pathological Evaluation of Rectal Cancer Specimens Using Micro-Computed Tomography.

Authors:  Masao Yoshida; Emine Cesmecioglu; Canan Firat; Hirotsugu Sakamoto; Alexei Teplov; Noboru Kawata; Peter Ntiamoah; Takashi Ohnishi; Kareem Ibrahim; Efsevia Vakiani; Julio Garcia-Aguilar; Meera Hameed; Jinru Shia; Yukako Yagi
Journal:  Diagnostics (Basel)       Date:  2022-04-14

Review 6.  Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Authors:  Alton B Farris; Juan Vizcarra; Mohamed Amgad; Lee A D Cooper; David Gutman; Julien Hogan
Journal:  Histopathology       Date:  2021-03-08       Impact factor: 5.087

7.  Molecular profiling of the intestinal mucosa and immune cells of the colon by multi-parametric histological techniques.

Authors:  Łukasz Zadka; Karolina Chrabaszcz; Igor Buzalewicz; Ewelina Wiercigroch; Natalia Glatzel-Plucińska; Łukasz Szleszkowski; Agnieszka Gomułkiewicz; Aleksandra Piotrowska; Krzysztof Kurnol; Piotr Dzięgiel; Tomasz Jurek; Kamilla Malek
Journal:  Sci Rep       Date:  2021-05-28       Impact factor: 4.379

8.  Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification.

Authors:  Marios A Gavrielides; Brigitte M Ronnett; Russell Vang; Fahime Sheikhzadeh; Jeffrey D Seidman
Journal:  J Pathol Inform       Date:  2021-03-22

9.  Implementation of Collodion Bag Protocol to Improve Whole-slide Imaging of Scant Gynecologic Curettage Specimens.

Authors:  Iny Jhun; David Levy; Harumi Lim; Quintina Herrera; Erika Dobo; Dominique Burns; William Hetherington; Ronald Macasaet; April J Young; Christina S Kong; Ann K Folkins; Eric Joon Yang
Journal:  J Pathol Inform       Date:  2021-01-08

10.  Deep Learning in Pancreatic Tissue: Identification of Anatomical Structures, Pancreatic Intraepithelial Neoplasia, and Ductal Adenocarcinoma.

Authors:  Mark Kriegsmann; Katharina Kriegsmann; Georg Steinbuss; Christiane Zgorzelski; Anne Kraft; Matthias M Gaida
Journal:  Int J Mol Sci       Date:  2021-05-20       Impact factor: 5.923

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.