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The purpose of this paper is to study approaches to the intellectual processing of Russianlanguage textual medical information concerning the thyroid cytopathology description to solve the issues of their classification and generation of the text of the medical report, as well as augmentation of descriptions in their acute shortage. Over the past decade, the field of biomedicine has not undergone significant changes. Approaches to analyzing patients' problems are mostly based on manual processing and expert knowledge of doctors. The paper considers the creation of a machine-learning pipeline containing a full cycle of data preprocessing and model training in the field of thyroid nodules fine-needle aspiration classification according to the Bethesda thyroid cytopathology reporting system. Sequential and transformer neural networks were used to design the architecture of deep learning models. The paper proposes approaches for cleaning and preprocessing raw medical descriptions to the required type. The obtained results show that sequential neural networks have greater accuracy on small data sets, and transformation architectures are superior to others when generating cytopathological reports on large amounts of data. The solution obtained in the study can be used as an additional reference tool for thyroid cytologists. [ABSTRACT FROM AUTHOR]

Background: The Bethesda System for Reporting Thyroid Cytopathology is a commonly used classification for fine needle aspiration (FNA) cytology of suspicious thyroid nodules. The risk of malignancy (ROM) for each category has recently been analyzed in three international databases. This paper compares the diagnostic performance of the Bethesda classification in a high‐volume referral center in Belgium. Methods: All consecutive thyroid procedures were registered in a prospective database from January 2010 till August 2022. Patient and surgical characteristics, preoperative Bethesda categories, and postoperative pathology results were analyzed. Results: Out of 2219 consecutive thyroid procedures, 1226 patients underwent preoperative FNA. Papillary thyroid cancer was the most prevalent malignancy (N = 119, 70.4%), followed by follicular (N = 17, 10.1%) and medullary thyroid cancer (N = 15, 8.9%). Micropapillary thyroid cancer was incidentally found in 46 (3.8%) patients. Bethesda categories I, II, III, IV, V, and VI, respectively, represented 250 (20.4%; ROM 4.4%), 546 (44.5%; ROM 3.8%), 96 (7.8%; ROM 20.8%), 231 (18.8%; ROM 15.2%), 62 (5.1%; ROM 72.6%), and 41 (3.3%; ROM 90.2%) patients. Overall ROM was 13.8%. An negative predictive value (NPV) of 96.2% was found. Overall specificity was 64.2% with a positive predictive value (PPV) of 31.9%. Diagnostic accuracy was 67.8%. Compared to international databases (CESQIP, EUROCRINE, and UKRETS), ROM in this study appeared lower for Bethesda category IV (15.2 vs. 26.7% and p = 0.612). Conclusion: Despite being validated in numerous studies, ROM based on preoperative FNA cytology classified according to the Bethesda classification may vary among surgical centers and countries as this study reveals a higher NPV and lower PPV. [ABSTRACT FROM AUTHOR]

Since the publication of the first edition in 2010, The Bethesda System for Reporting Thyroid Cytopathology has allowed cytopathologists to use a standardized, category-based reporting system for thyroid fine needle aspirations. The third edition builds on the success of the 2 earlier editions and offers several key updates. The most important is the assignment of a single name for each of the 6 diagnostic categories: (i) nondiagnostic; (ii) benign; (iii) atypia of undetermined significance; (iv) follicular neoplasm; (v) suspicious for malignancy; and (vi) malignant. Each of the categories has an implied risk of malignancy (ROM), which has been updated and refined based on data reported after the second edition. The third edition offers an average ROM for each category, in addition to the expected range of cancer risk. The atypia of undetermined significance subcategorization is simplified into 2 subgroups based on the implied ROM and molecular profiling. A discussion of pediatric thyroid disease has been added, and pediatric ROMs and management algorithms are discussed in the relevant sections. Nomenclature has been updated to align with the 2022 World Health Organization Classification of Thyroid Neoplasms. Two new chapters have been added: one that addresses the significant and expanded use of molecular and ancillary testing in thyroid cytopathology, and another that summarizes clinical perspectives and imaging findings in thyroid disease. [ABSTRACT FROM AUTHOR]

The Bethesda system for reporting thyroid cytopathology (TBSRTC) has attempted to standardize thyroid fine needle aspiration cytology (FNAC) reporting internationally into six diagnostic categories and help in clinical decision making. But there exists a significant variation in the reporting percentage and rates of malignancies in each category across the centres which complicates clinical decision making. To study the usefulness of TBSRTC in surgical planning of thyroid nodules and to correlate TBSRTC with final histopathology. 85 patients with thyroid nodules who underwent surgery were evaluated prospectively. Preoperatively FNAC and TBSRTC reporting was done and following surgery histopathology was correlated with cytology. Distribution of 85 patients amongst the six categories of TBSRTC was as follows: 2.35% in Category I, 68.23% II, 7.05% III, 16.47% IV, 2.35% V and 3.52% VI. In 93% (79) of patients TBSRTC correlated with histopathology whereas in 7% (6) it did not. Risk of malignancy calculated was 0% in II, 33.33% in III, 7.14% in IV and 100% in V and VI categories. Sensitivity, specificity, Positive Predictive Value and Negative Predictive Value of TBSRTC was 100% for V, VI categories, whereas it was 100%, 78%, 15% and 100% respectively for III, IV. The diagnostic accuracy in our study is 100% for Category V and VI whereas it is 79% for Category III and IV. TBSRTC proved to be a very good screening platform for triaging patients with thyroid nodules into benign and malignant groups, as it is directly related to risk of malignancy in each category. It has helped in appropriate surgical planning in 96.4% of our patients. [ABSTRACT FROM AUTHOR]

Objectives: Long-term follow-up is important for determining performance characteristics of thyroid fine-needle aspiration (FNA).Methods: Histologic or 3 or more years of clinical follow-up was used to calculate performance characteristics of thyroid FNA before and after implementation of The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). The impact of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) classification was also investigated.Results: Follow-up was obtained for 1,277/1,134 and 1,616/1,393 aspirates/patients (median clinical follow-up, 9.9 and 4.4 years, pre- and post-TBSRTC, respectively). Nondiagnostic, suspicious for follicular neoplasm, and suspicious for malignancy (SFM) diagnoses decreased and benign diagnoses increased post-TBSRTC, while atypical rate remained less than 1%. Negative predictive value for benign nodules and positive predictive value (PPV) for SFM increased significantly. Eleven nodules were reclassified as NIFTP, slightly decreasing PPV/risk of malignancy (ROM).Conclusions: Appropriate ROM for thyroid FNA can be achieved through application of TBSRTC terminology with minimal use of atypical category. [ABSTRACT FROM AUTHOR]