Nearest-neighbor classification for identification of aggressive versus nonaggressive low-grade astrocytic tumors by means of image cytometry-generated variables.

The authors investigated whether cytometry-related variables generated by means of computer-assisted microscopic analysis of Feulgen-stained nuclei can contribute significant information toward the characterization of low-grade astrocytic tumor aggressiveness. This investigation was conducted using the nearest-neighbor rule (a traditional classification method used in pattern recognition) to analyze a series of 250 supratentorial astrocytic tumors from adult patients. This series included 39 low-grade astrocytomas and 211 high-grade astrocytic tumors (including 47 anaplastic astrocytomas and 164 glioblastomas multiforme [GBMs]). The results show that the 3-nearest-neighbors rule enabled a subgroup of "atypical" astrocytomas to be distinguished from the "typical" tumors. The atypical astrocytoma species exhibited a DNA content (DNA ploidy level) and morphonuclear characteristics that were statistically more similar to the characteristics of GBMs than to those exhibited by the typical astrocytomas. An analysis of survival data revealed that patients with atypical astrocytomas survived for a significantly shorter period (p < 0.001) than patients with typical lesions of this kind. In fact, patients with atypical astrocytomas had a survival period similar to that of patients with anaplastic astrocytomas, whereas patients with typical astrocytomas had a survival period significantly longer (p < 0.0001) than those associated with anaplastic astrocytomas and GBMs.

[1]  Long-term survival in patients with glioblastoma multiforme. , 1993, Neurosurgery.

[2]  Charles B. Wilson,et al.  Correlation between the bromodeoxyuridine labeling index and the MIB‐1 and Ki‐67 proliferating cell indices in cerebral gliomas , 1994, Cancer.

[3]  B. Scheithauer,et al.  Surgical Pathology of the Nervous System and its Coverings , 1976 .

[4]  S. Coons,et al.  Regional heterogeneity in the DNA content of human gliomas , 1993, Cancer.

[5]  P. Burger,et al.  Comparison of cytologic composition with microfluorometric DNA analysis of the glioblastoma multiforme and anaplastic astrocytoma , 1987, Cancer.

[6]  R. Kiss,et al.  Identification of High Versus Lower Risk Clinical Subgroups in a Group of Adult Patients with Supratentorial Anaplastic Astrocytomas , 1995, Journal of neuropathology and experimental neurology.

[7]  K. Wallner,et al.  Good performance status of long-term disease-free survivors of intracranial gliomas. , 1993, International journal of radiation oncology, biology, physics.

[8]  M. Berger,et al.  Detection of proliferating cell nuclear antigen in gliomas and adjacent resection margins. , 1993, Neurosurgery.

[9]  R. Kiss,et al.  Histopathologic grading and DNA ploidy in relation to survival among 206 adult astrocytic tumor patients , 1992 .

[10]  R. Kiss,et al.  Assessment of nuclear size, nuclear DNA content and proliferation index in stereotaxic biopsies from brain tumours , 1993, Neuropathology and applied neurobiology.

[11]  S. Vandenberg Current Diagnostic Concepts of Astrocytic Tumors , 1992, Journal of neuropathology and experimental neurology.

[12]  G. McLachlan Discriminant Analysis and Statistical Pattern Recognition , 1992 .

[13]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[14]  S. Green,et al.  Glioblastoma multiforme and anaplastic astrocytoma pathologic criteria and prognostic implications , 1985, Cancer.

[15]  S. Coons,et al.  Regional Heterogeneity in the Proliferative Activity of Human Gliomas as Measured by the Ki‐67 Labeling Index , 1993, Journal of neuropathology and experimental neurology.

[16]  R. Kiss,et al.  Prognostic scoring in adult astrocytic tumors using patient age, histopathological grade, and DNA histogram type. , 1994, Journal of neurosurgery.

[17]  P. Kleihues,et al.  The Use of the Monoclonal Antibody Ki-67 in the Identification of Proliferating Cells: Application to Surgical Neuropathology , 1986, The American journal of surgical pathology.

[18]  R. Kiss,et al.  Characterization of factors in routine laboratory protocols that significantly influence the Feulgen reaction. , 1993, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[19]  B. Scheithauer,et al.  Prognostic factors in gliomas. A multivariate analysis of clinical, pathologic, flow cytometric, cytogenetic, and molecular markers , 1994, Cancer.

[20]  M. Prados,et al.  Prognostic significance of the proliferative potential of intracranial gliomas measured by bromodeoxyuridine labeling , 1993, International journal of cancer.

[21]  C. D. James,et al.  Gene and chromosomal alterations associated with the development of human gliomas , 1993, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[22]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[23]  K. Wallner,et al.  Erratum: Good performance status of long-term disease-free survivors of intracranial gliomas (International Journal of Radiation Oncology Biology (1993) 26 (129-133)) , 1993 .

[24]  C Decaestecker,et al.  Stereotactic biopsies from astrocytic tumors. Diagnostic information contributed by the quantitative chromatin pattern description. , 1995, Analytical and quantitative cytology and histology.

[25]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .