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Further evaluation of quantitative nuclear image features for classification of lung carcinomas

  • F. B. J. M. Thunnissen*
  • , P. C. Diegenbach
  • , A. H. van Hattum
  • , J. Tolboom
  • , D. M. van der Sluis
  • , W. Schaafsma
  • , H. J. Houthoff
  • , Jan R. A. Baak
  • *Corresponding author for this work
  • Maastricht UMC+
  • University of Amsterdam
  • Amsterdam UMC - Vrije Universiteit Amsterdam
  • University of Groningen
  • Amsterdam UMC - University of Amsterdam

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The usefulness of quantitative nuclear image features (QNI) for the histological classification of lung carcinomas was investigated. As no clear distinction could be established between the distributions of these features for the nuclei of squamous cell, adenocarcinoma, and large cell carcinoma, the attention was restricted to the discrimination between small cell lung carcinoma (SCLC and non-small cell carcinoma (NSCLC). This discrimination is the crucial one in discussions about the choice of treatment. The differences between SCLC and NSCLC are statistically highly significant for various QNI features. The use of more than one QNI feature hardly raised the discriminatory performance with respect to the distinction between SCLC and NSCLC. Inferences were made about the probability and confidence interval of SCLC for a given QNI feature. It is concluded that in cases of uncertainty or disagreement, nuclear characteristics are useful for the discrimination between SCLC and NSCLC. © 1992, Gustav Fischer Verlag · Stuttgart · Jena · New York. All rights reserved.
Original languageEnglish
Pages (from-to)531-535
JournalPathology, research and practice
Volume188
Issue number4-5
DOIs
Publication statusPublished - 1992
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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