Computer-Assisted Cell Morphometry and Ploidy Analysis in the Assessment of Thyroid Follicular Neoplasms

Abstract

Conventional cytology cannot discriminate between benign and malignant follicular neoplasms. Our study evaluated the diagnostic role of computer-assisted image analysis in the presurgical assessment of thyroid follicular neoplasms. Fifty-eight patients (14 males, 44 females, age range, 45-75 years) who underwent surgery for cytologic diagnosis of thyroid follicular neoplasm were studied. All patients were first evaluated on clinical grounds and assigned a high/low suspicion of malignancy on the basis of gender, age, and nodule size. Cell image analysis was subsequently performed using a Cytometrica BYK Gulden microscope image processor on Feulgen-stained thyroid cytologic smears. A different population of 50 benign and 50 malignant, histologically evaluated nodules was studied in order to establish image analysis criteria suggestive of thyroid malignancy. Ploidy histogram, proliferation index (PI), nuclear area coefficient of variation (NACV), and anisocariosis ratio (AR) were studied. Thyroid cancer was diagnosed in 16 of 58 follicular neoplasms. Only 7 of these lesions were clinically suspicious (43.7%), whereas 14 of 16 (87.5%) malignant tumors were identified by image analysis. Positive and negative predictive values of image analysis versus clinical evaluation were 46.6% versus 30.4% and 92.8% versus 74.3%, respectively. The distribution of ploidy pattern was different in benign versus malignant follicular neoplasms (χ ² 8.25, p = 0.016), malignant lesions showing an increased frequency of heteroclonal aneuploid DNA content (37.5% vs. 7.1%). Increased PI (mean ± standard deviation (SD) = 11.3 ± 5.7 vs. 7.1 ± 6.1; p < 0.01) and NACV (mean ± SD = 25.28 ± 1.89 vs. 20.14 ± 0.91; p < 0.01) levels were also observed in malignant follicular neoplasms. In conclusion, computer-assisted image analysis may profitably support clinical evaluation in the assessment of thyroid follicular neoplasms.