Can galectin-3 be a useful marker for conventional papillary thyroid microcarcinoma?

EphB3 protein is a potential ancillary diagnostic biomarker for thyroid cancers

OBJECTIVE

To investigate the expression of ephrin type B receptor 3 (EphB3) in thyroid tumors and its usage as an ancillary diagnostic biomarker for thyroid tumors.

METHODS

Formalin-fixed and paraffin-embedded (FFPE) tissue samples (78 cases) and FNAC samples (57 cases) were assessed with the EphB3 antibody using immunohistochemistry. PTC and other thyroid follicular tumors were compared regarding their EphB3 expression. Sanger sequencing was used to assess for the presence of a BRAF V600E mutation.

RESULTS

EphB3 was positive in 81.8 % (27/33) of papillary thyroid carcinoma (PTC), 83.3 % (5/6) of medullary thyroid carcinoma (MTC), 25 % (1/4) of hyperplastic/adenomatoid nodule (HN), 14.3 % (1/7) of follicular adenoma (FA), and negative in follicular tumors of uncertain malignant potential (FT-UMP) (0/13), noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP) (0/7), thyroid follicular carcinoma (TFC) (0/4), Hashimoto's thyroiditis (0/4), and normal thyroid follicular tissues (0/33). In cellular blocks, EphB3 was positive in 87.1 % (20/23) of PTC, 75 % (3/4) of MTC, 20 % (2/10) of HN, and negative in atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) (0/20) and normal thyroid follicular cells (0/10).

CONCLUSION

EphB3 is expressed in the majority of PTC, but less so in benign follicular nodules. EphB3 expression in fine needle aspiration cytology (FNAC) specimens can be used as a diagnostic tool to differentiate thyroid cancer from other follicular lesions in its differential diagnosis, especially AUS/FLUS and PTC.

Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

(1) Background: Differential diagnosis using immunohistochemistry (IHC) panels is a crucial step in the pathological diagnosis of hematolymphoid neoplasms. In this study, we evaluated the prediction accuracy of the ImmunoGenius software using nationwide data to validate its clinical utility. (2) Methods: We collected pathologically confirmed lymphoid neoplasms and their corresponding IHC results from 25 major university hospitals in Korea between 2015 and 2016. We tested ImmunoGenius using these real IHC panel data and compared the precision hit rate with previously reported diagnoses. (3) Results: We enrolled 3052 cases of lymphoid neoplasms with an average of 8.3 IHC results. The precision hit rate was 84.5% for these cases, whereas it was 95.0% for 984 in-house cases. (4) Discussion: ImmunoGenius showed excellent results in most B-cell lymphomas and generally showed equivalent performance in T-cell lymphomas. The primary reasons for inaccurate precision were atypical IHC profiles of certain cases, lack of disease-specific markers, and overlapping IHC profiles of similar diseases. We verified that the machine-learning algorithm could be applied for diagnosis precision with a generally acceptable hit rate in a nationwide dataset. Clinical and histological features should also be taken into account for the proper use of this system in the decision-making process.

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

BACKGROUND

Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions.

METHODS

We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input.

RESULTS

We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.

CONCLUSION

The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database

Background

Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.

Methods

A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.

Results

IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.

Conclusions

Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease-specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

Diagnostic significance of CK19, galectin-3, CD56, TPO and Ki67 expression and BRAF mutation in papillary thyroid carcinoma

The aim of the present study was to examine the rate of BRAF mutation and the expression profiles of CK19, galectin-3, CD56, thyroid peroxidase (TPO) and Ki67 in papillary thyroid carcinoma (PTC) and papillary thyroid micro-carcinoma (PTMC). A total of 246 cases of thyroid disease were collected, including PTC, PTMC, nodular goiter (NG) and Hashimoto thyroiditis (HT). The results revealed that CK19 expression was 116/120 in PTC, 61/64 in PTMC, 2/34 in NG and 1/28 in HT. Galectin-3 positive expression was 115/120 in PTC, 60/64 in PTMC, 6/34 in NG and 4/28 in HT. TPO positive expression was 8/120 in PTC, 1/64 in PTMC, 30/34 in NG and 25/28 in HT. CD56-positive expression was 12/120 in PTC, 3/64 in PTMC, 33/34 in NG and 26/28 in HT. Ki67 labeling index was 2.52±0.46% in PTC (120 cases), 2.62±0.52% in PTMC (64 cases), 2.55±0.44% in NG (34 cases) and 2.58±0.48% in HT (28 cases). BRAF mutation rate was 93/120 in PTC, 47/64 in PTMC, 3/34 in NG and 2/28 in HT. These results suggested that expression patterns of CK19, galectin-3, CD56 and TPO and BRAF mutation exhibit diagnosis value in thyroid disease. However, Ki67-positive rate exhibits no notable diagnosis value in thyroid disease.

Galectin-3 as a novel biomarker for disease diagnosis and a target for therapy (Review)

Galectin-3 is a member of the galectin family, which are β‑galactoside‑binding lectins with ≥1 evolutionary conserved carbohydrate‑recognition domain. It binds proteins in a carbohydrate‑dependent and ‑independent manner. Galectin‑3 is predominantly located in the cytoplasm; however, it shuttles into the nucleus and is secreted onto the cell surface and into biological fluids including serum and urine. It serves important functions in numerous biological activities including cell growth, apoptosis, pre‑mRNA splicing, differentiation, transformation, angiogenesis, inflammation, fibrosis and host defense. Numerous previous studies have indicated that galectin‑3 may be used as a diagnostic or prognostic biomarker for certain types of heart disease, kidney disease and cancer. With emerging evidence to support the function and application of galectin‑3, the current review aims to summarize the latest literature regarding the biomarker characteristics and potential therapeutic application of galectin‑3 in associated diseases.

Thyroid Fine-Needle Aspiration Cytology Practice in Korea

We reviewed the current status of thyroid fine-needle aspiration cytology (FNAC) in Korea. Thyroid aspiration biopsy was first introduced in Korea in 1977. Currently, radiologists aspirate the thyroid nodule under the guidance of ultrasonography, and cytologic interpretation is only legally approved when a cytopathologist makes the diagnosis. In 2008, eight thyroid-related societies came together to form the Korean Thyroid Association. The Korean Society for Cytopathology and the endocrine pathology study group of the Korean Society for Pathologists have been updating the cytologic diagnostic guidelines. The Bethesda System for Reporting Thyroid Cytopathology was first introduced in 2009, and has been used by up to 94% of institutions by 2016. The average diagnosis rates are as follows for each category: I (12.4%), II (57.9%), III (10.4%), IV (2.9%), V (3.7%), and VI (12.7%). The malignancy rates in surgical cases are as follows for each category: I (28.7%), II (27.8%), III (50.6%), IV (52.3%), V (90.7%), and VI (100.0%). Liquid-based cytology has been used since 2010, and it was utilized by 68% of institutions in 2016. The categorization of thyroid lesions into "atypia of undetermined significance" or "follicular lesion of undetermined significance" is necessary to draw consensus in our society. Immunocytochemistry for galectin-3 and BRAF is used. Additionally, a molecular test for BRAF in thyroid FNACs is actively used. Core biopsies were performed in only 44% of institutions. Even the institutions that perform core biopsies only perform them for less than 3% of all FNACs. However, only 5% of institutions performed core biopsies up to three times more than FNAC.