Diagnostic value of combining immunostaining for CD3 and nuclear morphometry in mycosis fungoides

Morphometric study in thyroid tumors

Various morphonuclear studies using digital image analysis have been taken into account in order to establish the malignancy of thyroid lesions based on their size and on the chromatographic characteristics of tumor cell nuclei. Nuclear morphometry involves the measurement of nuclear parameters to obtain diagnostically important information in an objective and reproducible manner. The aim of the present study was to evaluate the detailed morphometric analysis of histopathological preparations with lesions of the thyroid gland and to investigate its role in differentiating between benign and malignant thyroid lesions. The present study included 10 benign and 26 malignant thyroid cases with different selected thyroid lesions. Using a microscope connected to a computerized video system, nuclear morphometric parameters including the nuclear area, perimeter, average intensity, red average, width and roundness, were measured and analyzed. The main parameters used in the statistical calculation were significant in distinguishing between benign and malignant thyroid lesions. The association of morphometry in cytological smears for suspected malignant follicular lesions led to increased accuracy in establishing a suspicious malignant diagnosis for follicular lesions. Nuclear morphometry provides an unbiased point of view that increases diagnosis accuracy. Computerized morphometry can positively influence diagnostic accuracy, allowing for a better correlation with clinical and imaging data.

Deep learning as a new tool in the diagnosis of mycosis fungoides

Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differentiate MF from inflammatory diseases. Contemporary computational methods successfully identify cell nuclei in histological specimens. Deep learning methods are especially favored for such tasks. A deep learning model was used to detect nuclei Hematoxylin-Eosin(H-E) stained micrographs. Nuclear properties are extracted after detection. A multi-layer perceptron classifier is used to detect lymphocytes specifically among the detected nuclei. The comparisons for each property between MF and non-MF were carried out using statistical tests the results are compared with the findings in the literature to provide a descriptive analysis as well. Random forest classifier method is used to build a model to classify MF and non-MF lymphocytes. 10 nuclear properties were statistically significantly different between MF and non-MF specimens. MF nuclei were smaller, darker and more heterogenous. Lymphocyte detection algorithm had an average 90.5% prediction power and MF detection algorithm had an average 94.2% prediction power. This project aims to fill the gap between computational advancement and medical practice. The models could make MF diagnoses easier, more accurate and earlier. The results also challenge the manually examined and defined nuclear properties of MF with the help of data abundance and computer objectivity.

Cytological Evaluation of Thyroid Lesions by Nuclear Morphology and Nuclear Morphometry

Introduction:

Fine needle aspiration (FNA) of the thyroid gland is an effective diagnostic method. The Bethesda system for reporting thyroid cytopathology classifies them into six categories and gives implied risk for malignancy and management protocol in each category. Though the system gives specific criteria, diagnostic dilemma still exists. Using nuclear morphometry, we can quantify the number of parameters, such as those related to nuclear size and shape. The evaluation of nuclear morphometry is not well established in thyroid cytology.

Objective:

To classify thyroid lesions on fine needle aspiration cytology (FNAC) using Bethesda system and to evaluate the significance of nuclear parameters in improving the prediction of thyroid malignancy.

Materials and Methods:

In the present study, 120 FNAC cases of thyroid lesions with histological diagnosis were included. Computerized nuclear morphometry was done on 81 cases which had confirmed cytohistological correlation, using Aperio computer software. One hundred nuclei from each case were outlined and eight nuclear parameters were analyzed.

Results:

In the present study, thyroid lesions were common in female with M: F ratio of 1:5 and most commonly in 40–60 yrs. Under Bethesda system, 73 (60.83%) were category II; 14 (11.6%) were category III, 3 (2.5%) were category IV, 8 (6.6%) were category V, and 22 (18.3%) were category VI, which were malignant on histopathological correlation. Sensitivity, specificity, and diagnostic accuracy of Bethesda reporting system are 62.5, 84.38, and 74.16%, respectively. Minimal nuclear diameter, maximal nuclear diameter, nuclear perimeter, and nuclear area were higher in malignant group compared to nonneoplastic and benign group.

Conclusion:

The Bethesda system is a useful standardized system of reporting thyroid cytopathology. It gives implied risk of malignancy. Nuclear morphometry by computerized image analysis can be utilized as an additional diagnostic tool.

Morphometric Analysis of Thyroid Follicular Cells with Atypia of Undetermined Significance

BACKGROUND

Atypia of undetermined significance (AUS) is a category that encompasses a heterogeneous group of thyroid aspiration cytology. It has been reclassified into two subgroups based on the cytomorphologic features: AUS with cytologic atypia and AUS with architectural atypia. The nuclear characteristics of AUS with cytologic atypia need to be clarified by comparing to those observed in Hashimoto thyroiditis and benign follicular lesions.

METHODS

We selected 84 cases of AUS with histologic follow-up, 24 cases of Hashimoto thyroiditis, and 26 cases of benign follicular lesions. We also subcategorized the AUS group according to the follow-up biopsy results into a papillary carcinoma group and a nodular hyperplasia group. The differences in morphometric parameters, including the nuclear areas and perimeters, were compared between these groups.

RESULTS

The AUS group had significantly smaller nuclear areas than the Hashimoto thyroiditis group, but the nuclear perimeters were not statistically different. The AUS group also had significantly smaller nuclear areas than the benign follicular lesion group; however, the AUS group had significantly longer nuclear perimeters. The nuclear areas in the papillary carcinoma group were significantly smaller than those in the nodular hyperplasia group; however, the nuclear perimeters were not statistically different.

CONCLUSIONS

We found the AUS group to be a heterogeneous entity, including histologic follow-up diagnoses of papillary carcinoma and nodular hyperplasia. The AUS group showed significantly greater nuclear irregularities than the other two groups. Utilizing these features, nuclear morphometry could lead to improvements in the accuracy of the subjective diagnoses made with thyroid aspiration cytology.

Image analytic study of nuclear area in mantle cell lymphoma

Background

Malignant lymphomas are classified on the basis of morphology, immunohistochemistry, and genetic and molecular biological features. Morphology is considered the most important and basic feature. Lymphomas can be classified as small, medium, or large depending on the cell size, but this criterion tends to be rather subjective. The aim of this study was to investigate the usefulness of an objective approach based on quantitative measurements.

Methods

Twenty specimens of mantle cell lymphoma and 2 specimens of the tonsil were examined. The nuclear area of 6,401 tumor cells of mantle cell lymphoma and 743 normal mantle cells of reactive tonsils were measured by 3 authors by using a user-controlled image-analyzer. The images of the nuclei were outlined using the spline method and the i-solution software, and the data were assessed using ANOVA and Student's t-test.

Results

The mean nuclear areas of mantle cell lymphoma cells measured by the 3 authors were 37.9 [7.9] µm², 37.9 [7.2] µm², and 38.2 [7.7] µm² and those of normal mantle cells in reactive tonsil were 28.6 [2.3] µm², 28.8 [2.0] µm², and 27.0 [3.0] µm². There was no statistical difference between the 3 observations of mantle cell lymphoma (P=0.580) and normal tonsils.

Conclusion

For morphology, nuclear area is considered an important feature in the classification schemes of lymphoma. We showed that nuclear area measurement by using image analyzer can be used as an objective quantitative method. We think that nuclear morphometry may play a significant role in the diagnosis of lymphoma.

The use of synchrotron infrared microspectroscopy in the assessment of cutaneous T-cell lymphoma vs. pityriasis lichenoides chronica

BACKGROUND

The diagnosis of cutaneous lymphomas remains a challenge for both the clinician and dermatopathologist.

OBJECTIVES

To differentiate between frank malignant and premalignant lymphocytes within the skin.

METHODS

This study was performed on 20 patients with a mean age of 50 years. They were divided into two groups: mycosis fungoides (MF) (stage IA, IB and IIA) and pityriasis lichenoides chronica (PLC). Immunophenotyping using antibodies CD3, CD4, CD8, CD20 and CD30 was performed. Synchrotron Fourier transform infrared microspectroscopy (S-FTIRM) was performed on cell nuclei to assess chemical differences between MF and PLC cases as a potential complementary screening tool. Dermal spectra of both MF and PLC were compared using principal components analysis (PCA) of the S-FTIRM data.

RESULTS

All PLC spectra was clustered together. However, the MF spectra formed two clusters, one that grouped with the PLC and the other grouped separately. Moreover, protein and nucleic acids showed highly significant differences between MF (IIA and IB), MF (IA) and PLC.

CONCLUSIONS

The malignant transformation within lymphocytes was identifiable through the spectroscopic analysis of protein, RNA and DNA with S-FTIRM, making it a promising tool for classifying the progression of cutaneous T-cell lymphoma.

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.