Literature DB >> 28856554

Inter-observer agreement among pathologists in grading the pathological response to neoadjuvant chemotherapy in breast cancer.

Takeshi Yamaguchi1, Hirofumi Mukai2, Futoshi Akiyama3, Koji Arihiro4, Shinobu Masuda5, Masafumi Kurosumi6, Yoshinori Kodama7, Rie Horii3, Hitoshi Tsuda8.   

Abstract

BACKGROUND: The degree of pathological response to neoadjuvant chemotherapy (NAC) was correlated with the prognosis in breast cancer. There are few studies published on inter-observer variability in the assessment of pathological responses among pathologists.
METHODS: We collected 64 surgically resected specimens from patients who had received NAC. Three pathologists assessed the pathological responses and classified them into 7 grades according to grading system of the Japanese Breast Cancer Society. The levels of concordance among pathologists were categorized into 3 classes: full concordance (all pathologists gave the same grade), partial concordance (two of them gave the same grade), and discordance (all three gave different grades). The inter-observer agreement among pathologists was estimated using the percentage concordance and Cohen's kappa statistics.
RESULTS: Full concordance, partial concordance, and discordance were seen in 28 (43%), 33 (52%), and 3 (5%) specimens, respectively. In most of partial concordance specimens (30 out of 33), the pathological response grades differed by only one level. The kappa value was 0.59. The concordance rate with regard to pCR was 97%.
CONCLUSIONS: Most of the judgments among pathologists differed within one level, but there is room for improving harmonization in the assessment of pathological responses.

Entities:  

Keywords:  Classification; Inter-observer agreement; Neoadjuvant chemotherapy; Pathological complete response; Residual cancer

Mesh:

Substances:

Year:  2017        PMID: 28856554     DOI: 10.1007/s12282-017-0799-3

Source DB:  PubMed          Journal:  Breast Cancer        ISSN: 1340-6868            Impact factor:   4.239


  4 in total

1.  Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Authors:  James A Diao; Jason K Wang; Wan Fung Chui; Andrew H Beck; Hunter L Elliott; Amaro Taylor-Weiner; Victoria Mountain; Sai Chowdary Gullapally; Ramprakash Srinivasan; Richard N Mitchell; Benjamin Glass; Sara Hoffman; Sudha K Rao; Chirag Maheshwari; Abhik Lahiri; Aaditya Prakash; Ryan McLoughlin; Jennifer K Kerner; Murray B Resnick; Michael C Montalto; Aditya Khosla; Ilan N Wapinski
Journal:  Nat Commun       Date:  2021-03-12       Impact factor: 14.919

2.  Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures.

Authors:  Emily Kaczmarek; Jina Nanayakkara; Alireza Sedghi; Mehran Pesteie; Thomas Tuschl; Neil Renwick; Parvin Mousavi
Journal:  BMC Bioinformatics       Date:  2022-01-13       Impact factor: 3.169

3.  Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.

Authors:  Noorul Wahab; Islam M Miligy; Katherine Dodd; Harvir Sahota; Michael Toss; Wenqi Lu; Mostafa Jahanifar; Mohsin Bilal; Simon Graham; Young Park; Giorgos Hadjigeorghiou; Abhir Bhalerao; Ayat G Lashen; Asmaa Y Ibrahim; Ayaka Katayama; Henry O Ebili; Matthew Parkin; Tom Sorell; Shan E Ahmed Raza; Emily Hero; Hesham Eldaly; Yee Wah Tsang; Kishore Gopalakrishnan; David Snead; Emad Rakha; Nasir Rajpoot; Fayyaz Minhas
Journal:  J Pathol Clin Res       Date:  2022-01-10

4.  Predictive Factors of Long-Term Survival after Neoadjuvant Radiotherapy and Chemotherapy in High-Risk Breast Cancer.

Authors:  Jan Haussmann; Wilfried Budach; Carolin Nestle-Krämling; Sylvia Wollandt; Balint Tamaskovics; Stefanie Corradini; Edwin Bölke; David Krug; Tanja Fehm; Eugen Ruckhäberle; Werner Audretsch; Danny Jazmati; Christiane Matuschek
Journal:  Cancers (Basel)       Date:  2022-08-20       Impact factor: 6.575

  4 in total

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