Computer-aided classification of malignancy in astrocytomas. II. The value of categorically evaluated histologic and non-histologic features for a numerical classifier.
The present study was carried out in order to obtain a numerical classifier for the assessment of the malignancy in astrocytomas including glioblastomas ('astrocytomas grade 4'). The attempt resulted in 'TESTAST 268', a classifier based on a reference sample of 268 tumours, 67 in each of four malignancy classes. TESTAST 268 aids the identification of astrocytomas with one of four malignancy classes by means of eight classification variables, five histologic and three non-histologic. Identification is achieved with the aid of linear discriminant functions, both according to Bayes' decision rule (BAYTEST) and by canonical discriminant analysis (CANTEST) using the squared Mahalanobis distance. The discriminant functions with the calibration of the reference sample of the 268 tumours may be implemented on personal and even small pocket computers for practical application.
Schmitt
H P
HP
Institute of Neuropathology, University of Heidelberg, Germany.
Oberwittler
C
C
D001254:Astrocytoma / Q000145:classification* / Q000473:pathology; D002114:Calcinosis / Q000473:pathology; D015726:Giant Cells / Q000473:pathology; D005904:Glial Fibrillary Acidic Protein / Q000032:analysis; D006470:Hemorrhage / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008214:Lymphocytes / Q000473:pathology; D008938:Mitosis; D009336:Necrosis / Q000473:pathology; D011379:Prognosis; D012984:Software; D014617:Vacuoles / Q000473:pathology
D005904:Glial Fibrillary Acidic Protein