Context: Thyroid cancer incidence rates in the United States and globally have increased steadily over the last 40 years, primarily due to a tripling of the incidence of papillary thyroid carcinoma (PTC). Objective: The purpose of this study was to analyze trends in demographic, clinical, pathologic, and molecular characteristics of PTC from 1974 to 2009. Design and Setting: We identified and histologically reviewed 469 consecutive cases of PTC from one US institution from 4 preselected periods (1974 to 1985, 1990 to 1992, 2000, and 2009) and assessed BRAF and RAS point mutations and RET/PTC rearrangements among 341 tumors ≥0.3 cm in size. Changes over time were analyzed using polytomous and binary logistic regression; all analyses were adjusted for age and sex. Results: During this period, the median age of patients at diagnosis increased from 37 to 53 years (P < .001) and the percentage of microcarcinomas (≤1.0 cm) increased from 33% to 51% (P < .001), whereas extrathyroidal extension and advanced tumor stage decreased from 40% to 21% (P = .005) and from 43% to 28% (P = .036), respectively. Changes in tumor histopathology showed a decrease in classic PTC and an increase in the follicular variant (P < .001). The proportion of tumors with a BRAF mutation was stable (∼46%) but increased from 50% to 77% (P = .008) within classic papillary PTCs. The proportion of tumors with RAS mutations increased from 3% to 25% and within follicular pattern tumors from 18% to 44% (P < .001). The proportion of RET/PTC rearrangements decreased from 11% to 2% (P = .038). Conclusions: Similar to US national trends, we found an increasing age at diagnosis and greater detection of smaller-sized intrathyroidal PTCs. However, the overall proportion of BRAF mutations remained stable. Sharply rising percentages of the follicular variant histology and RAS mutations after 2000 suggest new and more recent etiologic factors. The increased incidence is not likely to be due to environmental or therapeutic radiation because the percentage of RET/PTC rearrangements decreased.

The fifth edition of the World Health Organization (WHO) histologic classification of thyroid neoplasms released in 2022 includes newly recognized tumor types, subtypes, and a grading system. Follicular cell-derived neoplasms are categorized into three families (classes): benign tumors, low-risk neoplasms, and malignant neoplasms. The terms “follicular nodular disease” and “differentiated high-grade thyroid carcinoma” are introduced to account for multifocal hyperplastic/neoplastic lesions and differentiated thyroid carcinomas with high-grade features, respectively. The term “Hürthle cells” is replaced with “oncocytic cells.” Invasive encapsulated follicular and cribriform morular variants of papillary thyroid carcinoma (PTC) are now redefined as distinct tumor types, given their different genetic alterations and clinicopathologic characteristics from other PTC subtypes. The term “variant” to describe a subclass of tumor has been replaced with the term “subtype.” Instead, the term “variant” is reserved to describe genetic alterations. A histologic grading system based on the mitotic count, necrosis, and/or the Ki67 index is used to identify high-grade follicular-cell derived carcinomas and medullary thyroid carcinomas. The 2022 WHO classification introduces the following new categories: “salivary gland-type carcinomas of the thyroid” and “thyroid tumors of uncertain histogenesis.” This review summarizes the major changes in the 2022 WHO classification and their clinical relevance.

As in other domains, artificial intelligence is becoming increasingly important in medicine. In particular,deep learning-based pattern recognition methods can advance the field of pathology byincorporating clinical, radiologic, and genomic data to accurately diagnose diseases and predictpatient prognoses. In this review, we present an overview of artificial intelligence, the brief historyof artificial intelligence in the medical domain, recent advances in artificial intelligence applied topathology, and future prospects of pathology driven by artificial intelligence.

Na DG, et al. Korean J Radiol. 2017 Jan-Feb;18(1):217-237. https://doi.org/10.3348/kjr.2017.18.1.217