Image Cytometry of Fine Needle Aspiration of Thyroid Epithelial Lesions

Abstract

Fine needle aspiration (FNA) biopsy has been documented as a reliable, cost-effective test, diagnosing different thyroid lesions. However, its sensitivity declines much in some settings. This study aimed to investigate the value of computerized morphometry and DNA ploidy in differential diagnosis of different thyroid epithelial follicular lesions diagnosed on FNA smears. In this study, 68 cases of thyroid FNA were cytologically examined, and classified according to their postthyroidectomy histopathologic diagnosis as follow: 10 cases of nodular goiter, 14 cases of adenomas, 10 cases of follicular carcinoma, 20 cases of papillary carcinoma, 10 cases of anaplastic carcinoma, and 4 cases of poorly differentiated carcinoma. Using Leica Qwin 500 image analysis system, nuclear area as a morphologic variable, and the DNA ploidy, DNA index, and proliferation indices were measured for each case. Sensitivity of FNA in the studied cases was optimum (100%) in papillary carcinomas and anaplastic carcinomas. However, the sensitivity declined sharply in discriminating nodular goiter and follicular adenoma from well-differentiated follicular carcinoma, thus, most of these cases were diagnosed as follicular lesions. A significant difference in mean values of nuclear area was found between benign and malignant lesions, with no statistical differences within the benign group. Aneuploidy was significantly associated with malignancy. Therefore, image analysis may become an important ancillary test in the initial evaluation of patients with thyroid epithelial lesions.

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid

“…This diagnostic problem is due to the overlapping cytological features of FA and FC cases. The immuno‐histochemical and molecular markers have been used to distinguish these two lesions with variable success . Immunohistochemistry panel consisting of galectin‐3, HBME‐1, thyroperoxidase, cytokeratin‐19, and keratan‐sulfate were used to evaluate the follicular neoplasm on FNAC smears .…”

“…The immuno-histochemical and molecular markers have been used to distinguish these two lesions with variable success. [2][3][4][5] Immunohistochemistry panel consisting of galectin-3, HBME-1, thyroperoxidase, cytokeratin-19, and keratan-sulfate were used to evaluate the follicular neoplasm on FNAC smears. 6 Combined use of galectin-3 and HBME-1 has higher sensitivity but specificity is near about 90%.…”

Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning

“…In total, 256 numerical features, which included morphological and texture features, were extracted per nucleus. Since nuclear morphological features are widely used in discriminating cancer cells from normal ones in image analysis in digital pathology (Mahfouz et al, 2012), we extracted six of the most popular features in our experiment: area, convexity, circularity, perimeter, eccentricity and equivalent diameter. Additionally, we computed three intensity-derived features (average intensity, standard deviation, and entropy), Haralick features and Gabor features as described in Haralick et al (1973), Boland and Murphy, 2001, and Wang et al, 2010.…”

Cancer diagnosis by nuclear morphometry using spatial information

“…In total, 256 numerical features were extracted per nucleus as follows: MorphologicalFeatures : Nuclear morphological features are widely used in discriminating cancer cells from normal ones in image analysis in digital pathology [31]. We extracted six of the most popular features in our experiment: area, convexity, circularity, perimeter, eccentricity and equivalent diameter.…”