Advances In Anatomic Pathology最新文献

Mesenchymal neoplasms within the genitourinary tract include a wide spectrum of tumors, ranging from benign to malignant, and tumors of uncertain malignant potential. Except for stromal tumors of the prostate, which originate from the specific prostatic stroma, these neoplasms generally resemble their counterparts in other body sites. The rarity of these neoplasms and the limitation associated with small biopsy samples present unique diagnostic challenges for pathologists. Accurate diagnosis is paramount, as it significantly influences prognosis and guides management and treatment strategies. This review addresses common diagnostic scenarios, discusses key differential diagnoses, and sheds light on potential diagnostic pitfalls.

Biomarkers play a crucial role in the diagnosis, treatment planning, and prognosis of premalignant and malignant lesions and are increasingly used in neoplasia of the lower female genital tract (LFGT) including the cervix, vagina, and vulva. This review will discuss key biomarkers routinely used in LFGT pathology, including programmed cell death ligand 1 (PD-L1), mismatch repair (MMR), and tumor mutational burden (TMB) testing, which are FDA-approved companion diagnostics for anti-PD-1 checkpoint inhibitors. Recent developments in HER2 testing as a marker for anti-HER2 therapies, and prognostic biomarkers such as p53 in HPV-independent vulvar intraepithelial lesions and carcinomas, are also reviewed.

The majority of neoplasms of the head and neck are of epithelial origin primarily including mucosal squamous cell neoplasms (papillomas; squamous cell carcinoma) as well as salivary gland neoplasms. However, the full spectrum of mesenchymal neoplasms (benign and malignant) typically arising in soft tissue sites may also develop in superficial layers of the upper aerodigestive tract. The diversity of mesenchymal neoplasms arising in the head and neck is beyond the scope of this article, and our focus will be on some of the more common and/or diagnostic problematic mesenchymal tumors occurring in the sinonasal tract, oral cavity/odontogenic, pharynx, larynx, and neck.

STK11 adnexal tumor is a novel malignant neoplasm of uncertain histogenesis frequently arising in a para-adnexal location and associated with Peutz-Jeghers syndrome in ∼50% of patients. Its broad morphologic spectrum and nonspecific immunohistochemical profile has resulted in misclassification in the past as a variety of other neoplasms including those of wolffian, sex cord-stromal, mesothelial, and epithelial derivation. This review focuses on the spectrum of adnexal neoplasms that may develop in Peutz-Jeghers syndrome, with particular emphasis on STK11 adnexal tumor and its differential diagnosis.

Molecularly defined neoplasms are increasingly recognized, given the broader application and performance of molecular studies. These studies allow us to better characterize these neoplasms and learn about their pathogenesis. In the thorax, molecularly defined neoplasms include tumors such as NUT carcinoma, SMARCA4-deficient undifferentiated tumor (DUT), primary pulmonary myxoid sarcoma with EWSR1::CREB1 fusion, hyalinizing clear cell carcinoma, and SMARCB1-deficient neoplasms. Overall, these tumors are rare but are now more often recognized given more widely available immunostains such as NUT (NUT carcinoma), BRG1 (SMARCA4-DUT), and INI-1 (SMARCB1-deficient neoplasm). Furthermore, cytogenetic studies for EWSR1 to support a hyalinizing clear cell carcinoma or primary pulmonary myxoid sarcoma are, in general, easily accessible. This enables pathologists to recognize and diagnose these tumors. The diagnosis of these tumors is important for clinical management and treatment. For instance, clinical trials are available for patients with NUT carcinoma, SMARCA4-DUT, and SMACRB1-deficient neoplasms. Herein, our current knowledge of clinical, morphologic, immunophenotypic, and molecular features of NUT carcinomas, SMARCA4-DUT, primary pulmonary myxoid sarcomas, hyalinizing clear cell carcinoma, and SMARCB1-deficient neoplasms will be reviewed.

This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.

Reproducibility of pulmonary invasive adenocarcinoma diagnosis is poor when applying the World Health Organization (WHO) classification. In this article, we aimed first to explain by 3-dimensional morphology why simple pattern recognition induces pitfalls for the assessment of invasion as applied in the current WHO classification of pulmonary adenocarcinomas. The underlying iatrogenic-induced morphologic alterations in collapsed adenocarcinoma in situ overlap with criteria for invasive adenocarcinoma. Pitfalls in seemingly acinar and papillary carcinoma are addressed with additional cytokeratin 7 and elastin stains. In addition, we provide more stringent criteria for a better reproducible and likely generalizable classification.

The increasing incidence of multiple lung nodules underscores the need for precise differentiation between multiple primary lung cancers (MPLCs) and intrapulmonary metastases (IPMs). This distinction impacts patient prognosis and treatment strategies. The prevalence of multiple lung nodules, ranging from 19.7% to 55.5%, highlights the clinical significance of this challenge. Historically, the role of histopathology, particularly comprehensive histology assessment (CHA), has been pivotal in differentiating MPLCs and IPMs. However, CHA has significant limitations, resulting in a constant search for a better way to distinguish those lesions. The best strategy for delineating MPLCs from IPMs is a multidisciplinary approach combining clinical data, radiology, histology, and molecular methods. Histology provides architectural and cellular characteristics, radiology contributes anatomic context and lesion characterization, and molecular methods reveal molecular features critical for accurate differentiation. Incorporating clinical data further enhances diagnostic precision. This review presents current knowledge and current approaches to multiple lung tumors. It is clear that even with a combination of pathology, radiology, and molecular data, definitive classification of multifocal lung tumors is not always possible.