Literature DB >> 14614056

Comparison of pathologist-detected and automated computer-assisted image analysis detected sentinel lymph node micrometastases in breast cancer.

Donald L Weaver1, David N Krag, Edward A Manna, Taka Ashikaga, Seth P Harlow, Kenneth D Bauer.   

Abstract

Sentinel lymph node biopsy has stimulated interest in identification of micrometastatic disease in lymph nodes, but identifying small clusters of tumor cells or single tumor cells in lymph nodes can be tedious and inaccurate. The optimal method of detecting micrometastases in sentinel nodes has not been established. Detection is dependent on node sectioning strategy and the ability to locate and confirm tumor cells on histologic sections. Immunohistochemical techniques have greatly enhanced detection in histologic sections; however, comparison of detection methodology has not been undertaken. Automated computer-assisted detection of candidate tumor cells may have the potential to significantly assist the pathologist. This study compares computer-assisted micrometastasis detection with routine detection by a pathologist. Cytokeratin-stained sentinel lymph node sections from 100 patients at the University of Vermont were evaluated by automated computer-assisted cell detection. Based on original routine light microscopy screening, 20 cases that were positive and 80 cases that were negative for micrometastases were selected. One-level (43 cases) or two-level (54 cases) cytokeratin-stained sections were examined per lymph node block. All 100 patients had previously been classified as node negative by using routine hematoxylin and eosin stained sections. Technical staining problems precluded computer-assisted cell detection scanning in three cases. Computer-assisted cell detection detected 19 of 20 (95.0%; 95% confidence interval, 75-100%) cases positive by routine light microscopy. Micrometastases missed by computer-assisted cell detection were caused by cells outside the instrument's scanning region. Computer-assisted cell detection detected additional micrometastases, undetected by light microscopy, in 8 of 77 (10.4%; 95% confidence interval, 5-20%) cases. The computer-assisted cell detection-positive, light microscopy-missed detection rate was similar for cases with one (3 of 30; 10.0%) or two (5 of 47; 10.6%) cytokeratin sections. Metastases detected by routine light microscopy tended to be larger (0.01-0.50 mm) than did metastases detected only by computer-assisted cell detection (0.01-0.03 mm). In a selected series of patients, automated computer-assisted cell detection identified more micrometastases than were identified by routine light microscopy screening of cytokeratin-stained sections. Computer-assisted detection of events that are limited in number or size may be more reliable than detection by a pathologist using routine light microscopy. Factors such as human fatigue, incomplete section screening, and variable staining contribute to missing metastases by routine light microscopy screening. Metastases identified exclusively by computer-assisted cell detection tend to be extremely small, and the clinical significance of their identification is currently unknown.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 14614056     DOI: 10.1097/01.MP.0000092952.21794.AD

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


  16 in total

Review 1.  Virtual microscopy as an enabler of automated/quantitative assessment of protein expression in TMAs.

Authors:  Catherine Conway; Lynne Dobson; Anthony O'Grady; Elaine Kay; Sean Costello; Daniel O'Shea
Journal:  Histochem Cell Biol       Date:  2008-08-05       Impact factor: 4.304

2.  Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model.

Authors:  Carlos López; Joaquín Jaén Martinez; Marylène Lejeune; Patricia Escrivà; Maria T Salvadó; Lluis E Pons; Tomás Alvaro; Jordi Baucells; Marcial García-Rojo; Xavier Cugat; Ramón Bosch
Journal:  Histochem Cell Biol       Date:  2009-08-04       Impact factor: 4.304

3.  Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis.

Authors:  Naiyun Zhou; Xiaxia Yu; Tianhao Zhao; Si Wen; Fusheng Wang; Wei Zhu; Tahsin Kurc; Allen Tannenbaum; Joel Saltz; Yi Gao
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-03-01

Review 4.  Histopathological image analysis: a review.

Authors:  Metin N Gurcan; Laura E Boucheron; Ali Can; Anant Madabhushi; Nasir M Rajpoot; B Yener
Journal:  IEEE Rev Biomed Eng       Date:  2009-10-30

5.  Computer-assisted validation of the synovitis score.

Authors:  Lars Morawietz; Frank Schaeper; Joerg H Schroeder; Tserenchunt Gansukh; Nachin Baasanjav; Manfred G Krukemeyer; Thorsten Gehrke; Veit Krenn
Journal:  Virchows Arch       Date:  2008-02-19       Impact factor: 4.064

6.  Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring.

Authors:  Anthony E Rizzardi; Arthur T Johnson; Rachel Isaksson Vogel; Stefan E Pambuccian; Jonathan Henriksen; Amy Pn Skubitz; Gregory J Metzger; Stephen C Schmechel
Journal:  Diagn Pathol       Date:  2012-06-20       Impact factor: 2.644

7.  The value of immunohistochemistry in sentinel lymph node histopathology in breast cancer.

Authors:  M B Klevesath; L G Bobrow; S E Pinder; A D Purushotham
Journal:  Br J Cancer       Date:  2005-06-20       Impact factor: 7.640

8.  Effects of tissue decalcification on the quantification of breast cancer biomarkers by digital image analysis.

Authors:  Arkadiusz Gertych; Sonia Mohan; Shawn Maclary; Sambit Mohanty; Kolja Wawrowsky; James Mirocha; Bonnie Balzer; Beatrice S Knudsen
Journal:  Diagn Pathol       Date:  2014-11-25       Impact factor: 2.644

9.  Quantitative comparison and reproducibility of pathologist scoring and digital image analysis of estrogen receptor β2 immunohistochemistry in prostate cancer.

Authors:  Anthony E Rizzardi; Xiaotun Zhang; Rachel Isaksson Vogel; Suzanne Kolb; Milan S Geybels; Yuet-Kin Leung; Jonathan C Henriksen; Shuk-Mei Ho; Julianna Kwak; Janet L Stanford; Stephen C Schmechel
Journal:  Diagn Pathol       Date:  2016-07-11       Impact factor: 2.644

10.  Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images.

Authors:  Weiming Mi; Junjie Li; Yucheng Guo; Xinyu Ren; Zhiyong Liang; Tao Zhang; Hao Zou
Journal:  Cancer Manag Res       Date:  2021-06-10       Impact factor: 3.989

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.