Morphometric, densitometric and flow cytometric criteria for the automated classification of thyroid lesions

Abstract

An automated classification of 73 thyroid lesions using a logical and mathematical approach was attempted. Densitometric, morphometric and flow cytometric parameters were used in Fisher linear discriminant functions to separate goiters or normal thyroids from adenomas and from carcinomas; the combination of this approach with binary discrimination improved the initial classification to a final efficiency of 81%. This approach, which is useful for classifying individual cells, was thus insufficient for classifying these cases. Analysis of the individual parameters showed that thyroid lesions were mainly in the near-diploid region. Two G0G1 populations were present in both benign and malignant lesions and were particularly frequent (50%) in atypical invasive follicular adenomas, probably related to the additional presence of an invasive clone. Near-triploid peaks were associated with malignancy as well as with high proliferative indexes. Nuclear and nucleolar sizes were larger in carcinomas; however, the percentage of the nucleolar area in the nucleus was greater in adenomas and nodular adenomatous goiters. A corrected staining index correlated with the nuclear size and the ploidy of abnormal cells (r = .50), being higher in malignant lesions.