Methodological aspects of using decision trees to characterise leiomyomatous tumors.
The aim of the present work is to present the potential uses of a classification technique labeled the "decision tree" for tumor characterisation when faced with a large number of features. The decision tree technique enables multifeature logical classification rules to be produced by determining discriminatory values for each feature selected. In this report, we propose a methodology that used decision trees to compare and evaluate the information contributed by different types of features for tumor characterisation. This methodology is able to produce a set of hypotheses related to a diagnosis and or prognosis problem. For example, hypotheses can be producted (on the basis of a set of descriptive features) to explain why tumor cases belong to a given histopathological group. To illustrate our purpose, this methodology was applied to the difficult problem of leiomyomatous tumour diagnosis. The aim was to illustrate what kind of diagnostic information can be extracted from a sample data set including 23 smooth muscle tumors (14 benign leiomyomas and 9 malignant leiomyosarcomas) described by a large set of computer-assisted, microscope-generated features. Three groups of features were used relating to: (1) ploidy level determination (10 features), (2) quantitative chromatin pattern description (15 features), and (3) immunohistochemically related antigen specificities (6 features). All these features were quantified by digital cell image analysis. The results suggest that an objective distinction between leiomyomas and leiomyosarcomas can be established by means of simple logical rules depending on only a few features among which the immunohistochemically revealed antigen expression of desmin plays a preponderant part. One of the combinations of features proposed by the methodology is interesting for pathologists, because it includes two features describing the appearance of a nucleus in terms of chromatin distribution homogeneity and density, two features widely used by pathologists in tumor-grading systems.
Decaestecker
C
C
Institute of Interdisciplinary Research and Development in Artificial Intelligence, Erasmus Hospital, Brussels, Belgium.
Remmelink
M
M
Salmon
I
I
Camby
I
I
Goldschmidt
D
D
Petein
M
M
Van Ham
P
P
Pasteels
J L
JL
Kiss
R
R
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000951:Antigens, Neoplasm; D002843:Chromatin; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007889:Leiomyoma / Q000145:classification / Q000175:diagnosis*; D007890:Leiomyosarcoma / Q000145:classification / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D011003:Ploidies; D018235:Smooth Muscle Tumor / Q000145:classification / Q000175:diagnosis*
D000951:Antigens, Neoplasm; D002843:Chromatin