By Jan P. A. Baak M.D., Ph.D., Jan Oort M.D., Ph.D. (auth.)
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Extra info for A Manual of Morphometry in Diagnostic Pathology
For research projects, such a difference can be very interesting. In a diagnostic setting, however, and especially when a choice of therapy has to be made, it is necessary to introduce a decision rule or threshold - a certain decisive value of the feature - to distinguish the classification "benign" from the classification "malignant". For example, in fig. 1, using the decision threshold T, we might say that a mitotic activity index greater than T indicates malignancy (group 11), whilst tissue with a mitotic activity index less than T is benign.
G. 60, see fig. 10) or with real misclassifications. This type of threshold value decision making which allows the assignment "inconclusive" has recently been advocated (Pauker and Kassirer 1980; Schwarz et aI, 1981; Sappenfield et aI, 1981). Examples ofthe use of numerical classification probabilities and threshold values in tumour diagnosis have been given for endometrial deviations (Baak et al 1979), ovarian tumours (Baak et aI, 1981), breast cancer (Baak et aI, 1982; Boon et aI, 1982a) and thyroid nodules (Boonet aI, 1982 b).
Both Xl and X 2 show separately a considerable overlap, but with the combination of Xl and X 2, the two groups of patients are completely separated. This diagram can be used to classify new patients, but it is often possible to simplify this bivariate plot describing the two groups of patients to a new, one dimensional descriptor - or, in other words, a line. Let, for example, line y = a l Xl + a2x2 give the optimal discrimination. Fig. 5. Combination of features may decrease the overlap. H ~------~~------~------Xl I 1I Then the classification of new patients is reduced to the question of whether the y-value of the patient in the decision rule formulated in y-values is right or left from the decision threshold T?
A Manual of Morphometry in Diagnostic Pathology by Jan P. A. Baak M.D., Ph.D., Jan Oort M.D., Ph.D. (auth.)