OBJECTIVE: To study the discriminatory power of different methods designed for nuclear shape analysis with reference to the differentiation and grading of brain tumors and the differentiation between proliferating and nonproliferating nuclei. STUDY DESIGN: At least 300 tumor cell nuclei per case were measured by means of a digital image analysis system. Fourier amplitudes no. 1 to 15, moments no. 1 to 7 according to Hu, roundness factor, ellipse shape factor, concavity factor, Feret ratio, fractal dimension and bending energy were determined for each nucleus. The discriminatory power of these parameters was tested in three pairwise comparisons: (1) oligodendrogliomas WHO grade II (n = 13) vs. grade III (n = 11), (2) medulloblastomas WHO grade IV (n = 14) vs. anaplastic ependymomas WHO grade III (n = 12), (3) Ki-67-positive vs. Ki-67-negative tumor cell nuclei in the 14 medulloblastomas. RESULTS: When data from Fourier analysis were included in statistical analysis, cross-validated discriminant analysis led to a 100% correct reclassification for the first and for the second pairwise comparison and to a 75% correct reclassification when comparing Ki-67-positive and Ki-67-negative nucleifrom medulloblastomas. Different combinations of the other shape parameters led to a lower percentage of correctly reclassified cases for all three pairwise comparisons, especially when Fourier analysis was not included in the analysis. CONCLUSION: Fourier analysis provided an optimal statistical discrimination between different brain tumor entities and between data sets from proliferating and nonproliferating tumor cell nuclei. Since nuclear shape is an important criterion for the investigation of tumors, the application of Fourier analysis is therefore recommended for quantitative histologic investigations in neuro-oncology.
OBJECTIVE: To study the discriminatory power of different methods designed for nuclear shape analysis with reference to the differentiation and grading of brain tumors and the differentiation between proliferating and nonproliferating nuclei. STUDY DESIGN: At least 300 tumor cell nuclei per case were measured by means of a digital image analysis system. Fourier amplitudes no. 1 to 15, moments no. 1 to 7 according to Hu, roundness factor, ellipse shape factor, concavity factor, Feret ratio, fractal dimension and bending energy were determined for each nucleus. The discriminatory power of these parameters was tested in three pairwise comparisons: (1) oligodendrogliomas WHO grade II (n = 13) vs. grade III (n = 11), (2) medulloblastomas WHO grade IV (n = 14) vs. anaplastic ependymomas WHO grade III (n = 12), (3) Ki-67-positive vs. Ki-67-negative tumor cell nuclei in the 14 medulloblastomas. RESULTS: When data from Fourier analysis were included in statistical analysis, cross-validated discriminant analysis led to a 100% correct reclassification for the first and for the second pairwise comparison and to a 75% correct reclassification when comparing Ki-67-positive and Ki-67-negative nucleifrom medulloblastomas. Different combinations of the other shape parameters led to a lower percentage of correctly reclassified cases for all three pairwise comparisons, especially when Fourier analysis was not included in the analysis. CONCLUSION: Fourier analysis provided an optimal statistical discrimination between different brain tumor entities and between data sets from proliferating and nonproliferating tumor cell nuclei. Since nuclear shape is an important criterion for the investigation of tumors, the application of Fourier analysis is therefore recommended for quantitative histologic investigations in neuro-oncology.