Literature DB >> 21107286

A systematic review of the interobserver variability for histology in the differentiation between squamous and nonsquamous non-small cell lung cancer.

Daniel C Paech1, Adèle R Weston, Nick Pavlakis, Anthony Gill, Narayan Rajan, Helen Barraclough, Bronwyn Fitzgerald, Maximiliano Van Kooten.   

Abstract

INTRODUCTION: The importance of identifying non-small cell lung cancer (NSCLC) histologic subtype has increased recently because of the development of target-specific chemotherapeutic agents. This systematic review was undertaken to examine the interobserver variability for histology in differentiating between subtypes of NSCLC, specifically the ability to differentiate squamous from nonsquamous histology.
METHODS: A systematic literature search was undertaken to identify studies that evaluated the reproducibility of histologic diagnosis by pathologists in their reporting of NSCLC subtypes. Studies were screened using a priori defined eligibility criteria. The National Health and Medical Research Council diagnostic levels of evidence were applied and quality assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Data were extracted and reanalyzed to permit comparison of agreement in nonsquamous and squamous cell carcinoma by 2 × 2 tables. Percentage agreement and kappa statistics were calculated for each included study.
RESULTS: Out of 1480 articles identified through the literature search, six were eligible for inclusion. The percentage agreement for all subtypes of NSCLC in the included studies ranged from 67.1 to 89.6% (κ, 0.42-0.84). Based on the primary reanalysis of data (reanalysis 1), agreement between pathologists in differentiating nonsquamous and squamous histology ranged from 77.0 to 94.2% (κ = 0.48-0.88) indicating a moderate to high level of agreement.
CONCLUSION: The reasonably high agreement and kappa statistics for the included studies suggest that pathologists can reproducibly differentiate between nonsquamous and squamous NSCLC. This is clinically important in guiding oncologist decision making in choosing the most appropriate therapy for their patients.

Entities:  

Mesh:

Year:  2011        PMID: 21107286     DOI: 10.1097/JTO.0b013e3181fc0878

Source DB:  PubMed          Journal:  J Thorac Oncol        ISSN: 1556-0864            Impact factor:   15.609


  9 in total

1.  Routine screening pelvic examinations have a negative effect on patients.

Authors:  Robert W Shepherd
Journal:  Can Fam Physician       Date:  2016-08       Impact factor: 3.275

2.  Assessment of chromosomal rearrangements helps to differentiate multiple lung primary cancers from metastases.

Authors:  Laura Bonanno; Alberto Pavan; Stefano Indraccolo
Journal:  Transl Lung Cancer Res       Date:  2019-12

3.  Polarimetric second-harmonic generation microscopy of the hierarchical structure of collagen in stage I-III non-small cell lung carcinoma.

Authors:  Ahmad Golaraei; Leila B Mostaço-Guidolin; Vaishnavi Raja; Roya Navab; Tao Wang; Shingo Sakashita; Kazuhiro Yasufuku; Ming-Sound Tsao; Brian C Wilson; Virginijus Barzda
Journal:  Biomed Opt Express       Date:  2020-03-09       Impact factor: 3.732

4.  Non-small cell lung cancer histological subtype has prognostic impact in patients with brain metastases.

Authors:  Carsten Nieder; Anca L Grosu; Kirsten Marienhagen; Nicolaus H Andratschke; Hans Geinitz
Journal:  Med Oncol       Date:  2012-04-01       Impact factor: 3.064

5.  Clinical Utility of Next-generation Sequencing in Real-world Cases: A Single-institution Study of Nine Cases.

Authors:  Moonsik Kim; Ji Yun Jeong; Nora Jee-Young Park; Ji Young Park
Journal:  In Vivo       Date:  2022 May-Jun       Impact factor: 2.406

6.  A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer.

Authors:  Anna Karlsson; Helena Cirenajwis; Kajsa Ericson-Lindquist; Hans Brunnström; Christel Reuterswärd; Mats Jönsson; Cristian Ortiz-Villalón; Aziz Hussein; Bengt Bergman; Anders Vikström; Nastaran Monsef; Eva Branden; Hirsh Koyi; Luigi de Petris; Patrick Micke; Annika Patthey; Annelie F Behndig; Mikael Johansson; Maria Planck; Johan Staaf
Journal:  Sci Rep       Date:  2019-03-26       Impact factor: 4.379

7.  A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.

Authors:  Fahdi Kanavati; Gouji Toyokawa; Seiya Momosaki; Hiroaki Takeoka; Masaki Okamoto; Koji Yamazaki; Sadanori Takeo; Osamu Iizuka; Masayuki Tsuneki
Journal:  Sci Rep       Date:  2021-04-14       Impact factor: 4.379

8.  Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes.

Authors:  E Kontsek; A Pesti; J Slezsák; P Gordon; T Tornóczki; G Smuk; S Gergely; A Kiss
Journal:  Pathol Oncol Res       Date:  2022-08-17       Impact factor: 2.874

9.  Critical Role of Pathologists in the Accurate Subclassification of Non-Small Cell Lung Carcinoma (NSCLC) for Targeted Therapies: Evidence- Based Practice and the Role of IHC Markers.

Authors:  Qing Kay Li
Journal:  Diagn Pathol Open Access       Date:  2016-12-31
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.