Nuclear morphometry in African breast cancer

Offiong Francis Ikpatt, Teijo Kuopio, Yrjö Collan

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Three hundred cases of invasive breast cancer diagnosed between 1983 and 1999 in Calabar, Nigeria were analysed to determine the nuclear morphometric variables, and evaluate the prognostic potential of nuclear morphometry in Nigerian breast cancers. The necessary follow-up was available for 129 patients. The nuclear area was the most valuable variable. In the Nigerian material, the mean nuclear area (MNA) (SD) was 89.2 (34.0) μm2. MNA was significantly higher in tumours of the postmenopausal than premenopausal (p = 0.0405), in LN+ than LN-(p = 0.0202) patients, and in tumours over 3 cm than smaller ones (p < 0.0001). There were also significant differences between different clinical stages, histological grades, and histological types of tumours. Significant correlations were observed between MNA and histological grade (r = 0.64), standard mitotic index (r = 0.45) and tumour size (r = 0.20). MNA as a continuous variable was a statistically significant prognosticator in the whole material (p = 0.0281), and among the postmenopausal patients (p = 0.0238). Univariate coxs regression demonstrated one significant grading cutpoint at MNA = 111 μm2, which divided the material into two groups of different survival. The development of a morphometric grading system optimal for the Nigerian material could use the latter cut-point between nuclear scores 2 and 3 in the grading system. The earlier proven cut-point of 47 μm2 could be used between nuclear scores 1 and 2.

Original languageEnglish (US)
Pages (from-to)145-150
Number of pages6
JournalImage Analysis and Stereology
Issue number2
StatePublished - 2002
Externally publishedYes


  • Africa
  • Breast cancer
  • Morphometry
  • Nuclear area
  • Prognostication

ASJC Scopus subject areas

  • Biotechnology
  • Signal Processing
  • Materials Science (miscellaneous)
  • Mathematics(all)
  • Instrumentation
  • Radiology Nuclear Medicine and imaging
  • Acoustics and Ultrasonics
  • Computer Vision and Pattern Recognition


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