Computer-assisted cell morphometry and ploidy analysis in the assessment of thyroid follicular neoplasms

Follicular thyroid lesions: is there a discriminatory potential in the computerized nuclear analysis?

BACKGROUND

Computerized image analysis seems to represent a promising diagnostic possibility for thyroid tumors. Our aim was to evaluate the discriminatory diagnostic efficiency of computerized image analysis of cell nuclei from histological materials of follicular tumors.

METHODS

We studied paraffin-embedded materials from 42 follicular adenomas (FA), 47 follicular variants of papillary carcinomas (FVPC) and 20 follicular carcinomas (FC) by the software ImageJ. Based on the nuclear morphometry and chromatin texture, the samples were classified as FA, FC or FVPC using the Classification and Regression Trees method.

RESULTS

We observed high diagnostic sensitivity and specificity rates (FVPC: 89.4% and 100%; FC: 95.0% and 92.1%; FA: 90.5 and 95.5%, respectively). When the tumors were compared by pairs (FC vs FA, FVPC vs FA), 100% of the cases were classified correctly.

CONCLUSION

The computerized image analysis of nuclear features showed to be a useful diagnostic support tool for the histological differentiation between follicular adenomas, follicular variants of papillary carcinomas and follicular carcinomas.

Preoperative Evaluation of Thyroid Epithelial Lesions by DNA Ploidy and Galectin-3 Expression in FNAC

AIM: This study aimed to investigate the value of DNA ploidy and galectins-3 immunostain in the preoperative evaluation of thyroid epithelial lesions.MATERIAL AND METHODS: Sixty patients presenting with thyroid enlargement were included in this study and referred by clinicians for FNA. Routine cytological evaluation was done on PAP   stained slides according to the WHO criteria and at least three slides were prepared for routine cytological examinations. The nuclear DNA analysis was performed at the Pathology Department, National Research Center using the Leica Qwin 500 Image Analyzer (LEICA Imaging Systems Ltd, Cambridge, England). Galectin-3 expression was investigated in all tissues using streptavidin-biotin technique.RESULTS: Conventional Fine needle aspiration cytology (FNAC) of 60 cases could diagnose malignancy with a sensitivity of 60%, negative predictive value (NPV) 71.4%, and overall diagnostic accuracy of 80%. The aneuploidy was significantly associated with malignancy, with sensitivity 90.9%, specificity 83.3% and accuracy 88.3%. On using galectin-3 immunocytochemichal stain on cell blocks prepared from FNA the values were improved, sensitivity 93.3% specificity 86.7% and overall accuracy 90% and it was noticed that galectin-3 over expression was significantly associated with malignancy.CONCLUSIONS: From the results of this study we can consider that DNA ploidy and Galectin-3 could refine the FNA results and  increase its sensitivity as a screening test from sensitivity(60%) to reach sensitivity (93.3%), thus decreasing the false negative cases. From this study, it is concluded that the application of ancillary techniques as galectin-3 immunocytochemical markers may become a reliable indicator for surgical intervention, DNA ploidy measurements on the other hand may be of value in galectin-3 negative cases to determine the behavior of the lesion in such cases & refine the preoperative assessment by out ruling false negative cases.

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

Follicular lesions of the thyroid remain significant diagnostic challenges in surgical pathology and cytology. The diagnosis often requires considerable resources and ancillary tests including immunohistochemistry, molecular studies, and expert consultation. Visual analyses of nuclear morphological features, generally speaking, have not been helpful in distinguishing this group of lesions. Here we describe a method for distinguishing between follicular lesions of the thyroid based on nuclear morphology. The method utilizes an optimal transport-based linear embedding for segmented nuclei, together with an adaptation of existing classification methods. We show the method outputs assignments (classification results) which are near perfectly correlated with the clinical diagnosis of several lesion types' lesions utilizing a database of 94 patients in total. Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. In addition, the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions. Our methods could be incorporated into a tool for pathologists to aid in distinguishing between follicular lesions of the thyroid. In addition, these results could potentially provide nuclear morphological correlates of biological behavior and reduce health care costs by decreasing histotechnician and pathologist time and obviating the need for ancillary testing.

Image cytometry of fine needle aspiration of thyroid epithelial lesions

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.

An optimal transportation approach for nuclear structure-based pathology

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers.

Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images

Follicular lesions of the thyroid are traditionally difficult and tedious challenges in diagnostic surgical pathology in part due to lack of obvious discriminatory cytological and microarchitectural features. We describe a computerized method to detect and classify follicular adenoma of the thyroid, follicular carcinoma of the thyroid, and normal thyroid based on the nuclear chromatin distribution from digital images of tissue obtained by routine histological methods. Our method is based on determining whether a set of nuclei, obtained from histological images using automated image segmentation, is most similar to sets of nuclei obtained from normal or diseased tissues. This comparison is performed utilizing numerical features, a support vector machine, and a simple voting strategy. We also describe novel methods to identify unique and defining chromatin patterns pertaining to each class. Unlike previous attempts in detecting and classifying these thyroid lesions using computational imaging, our results show that our method can automatically classify the data pertaining to 10 different human cases with 100% accuracy after blind cross validation using at most 43 nuclei randomly selected from each patient. We conclude that nuclear structure alone contains enough information to automatically classify the normal thyroid, follicular carcinoma, and follicular adenoma, as long as groups of nuclei (instead of individual ones) are used. We also conclude that the distribution of nuclear size and chromatin concentration (how tightly packed it is) seem to be discriminating features between nuclei of follicular adenoma, follicular carcinoma, and normal thyroid.

Computerized nuclear morphometry in the diagnosis of thyroid lesions with predominant follicular pattern

Background:

Differential diagnosis of thyroid lesions with predominantly follicular pattern is one of the most common problems in thyroid pathology. Development of more objective and reproducible tools for diagnosis is needed. This work is aimed at studying the role of nuclear morphometry in differential diagnosis of different thyroid lesions having predominant follicular pattern.

Material and methods:

Semiautomatic image analysis system was used to measure a total of 8 nuclear parameters in 48 thyroid lesions including seven nodular goiter (NG), 14 follicular adenoma (FA), 14 follicular carcinoma (FC) and 13 follicular variant papillary carcinoma (FVPC).

Results:

The parameters related to nuclear size (area, perimeter, MaxD, MinD, nuclear size) and shape (L/S ratio, Form_AR) were significantly higher in neoplastic group (FA, FC, FVPC) when compared to non-neoplastic group (NG) P<0.05. The perimeter was the most reliable parameter (area under the cure (AUC)=97%) followed by area, MaxD, and size (all have AUC= 96%) then form-AR (90%), LS ratio (86%) and the least reliable was Min D (79%). Within the neoplastic group, most parameters related to size and shape of the nuclei was significantly higher in FVPC than in FA and FC (p ≤ 0.05). Nuclear area and size (AUC 77%) were the most reliable parameters for differentiation between FVPC and FA. The best cut off values for diagnosing FVPC are nuclear area ≥39.9μm² and nuclear size ≥27.7μm². However, there was no quantitative difference between FC and FA.

Conclusion:

Nuclear morphometric parameters may help in the differentiation between neoplastic and non-neoplastic thyroid lesions and between FVPC and follicular neoplasms (FC and FA) but they have no value in the differentiation between FC and FA.

Computerized Morphometric Study of Thyroid Follicular Carcinoma in Correlation with Known Prognostic Factors

This study investigates the correlation between computer-assisted nuclear morphometry and known prognostic factors in thyroid follicular carcinoma. Thirty-six patients with thyroid follicular carcinoma who underwent surgery between 1991 and 2001 were grouped according to sex, age, size of the primary lesion, the presence of vascular invasion, and metastases. Four nuclear parameters were measured and analyzed: mean nuclear area, mean nuclear perimeter, largest to smallest diameter ratio of the nuclei, and coefficient of variation of the nuclear area. Our results indicated that none of the chosen nuclear variables were significantly correlated with the prognostic factors studied. In conclusion, nuclear morphometry does not seem to correlate with known prognostic factors and cannot serve as an additional predicting factor for biologic behavior.

Fractal dynamics in the differential diagnosis of thyroideal follicular neoplasms

In this study, we investigated the sensitivity, specificity, and predictive values of morphometric parameters of thyroideal follicular neoplasms based on concepts of fractal geometry. Thirty-seven follicular adenomas and 36 well-differentiated follicular carcinomas were assessed morphometrically. The nuclear area, nuclear area fraction, nuclear regularity factor, nuclear elongation factor, and slope setting (representing the ratio between the nuclear perimeter and nuclear regularity factor) were subjected to fractal dimensions analysis. By univariate analysis, the nuclear area, nuclear area fraction, nuclear regularity factor and slope values discriminate between adenomas and carcinomas. By multivariate analysis, the nuclear area, nuclear area fraction and slope values possess significant discriminatory powers in distinguishing between adenomas and carcinomas. Incorporating the nuclear area, nuclear area fraction, and slope values leads to a discriminatory power with 92% specificity and 83% sensitivity. The reciprocal relationships between the nuclear area, nuclear perimeter, and nuclear regularity factor of the cells of thyroideal adenomas and carcinomas may be expressed by fractal dimensions. Analysis limited to one parameter provides incomplete data. Expressing variations of the nuclear perimeter as a function of the nuclear regularity factor, the slope values constitute an independent attribute that significantly differentiates thyroideal adenomas from carcinomas.

Postoperative findings and risk for malignancy in thyroid nodules with cytological diagnosis of the so-called "follicular neoplasm"

BACKGROUND

Malignant follicular lesion is not differentiated from benign lesions cytologically. The objective of this study was to assess the rate and the risk of malignancy in thyroid nodules which were cytologically diagnosed as follicular neoplasm by fine-needle aspiration (FNA) cytology.

METHODS

All the patients who had undergone surgery with cytological diagnosis of follicular neoplasm from January 1996 through December 2001 in Asan Medical Center were studied retrospectively. Patients' and nodule characteristics were analyzed for factors associated with the presence of cancer. Two hundred and fifteen patients (196 females, 19 males) were included and their mean age was 39.4 years (range: 12-76).

RESULTS

About half of the patients (102 out of 215, 47.4%) had malignancy with 29 papillary carcinomas, 57 follicular carcinomas, 15 Hürthle cell carcinomas and 1 medullary carcinoma. Previously suggested factors associated with risk for malignancy, such as male gender, large tumor size (> 4 cm) or age of patients (> 45 years), were not associated with increased risk. Diagnosis of Hürthle cell neoplasia on FNA was also not associated with increased risk. Only the extremes in age of the patients (below 20 or above 60 years) were associated with increased risk for malignancy.

CONCLUSION

In our findings, prevalence of carcinoma in thyroid nodule patients with cytological diagnosis of follicular neoplasm was much higher than those reported. Clinical characteristics, such as male gender, age and nodule size, are not useful predictors for the presence of malignancy. Thyroid nodules with cytological diagnosis of follicular neoplasm warrant immediate surgery.