Advances In Anatomic Pathology最新文献

Treatment-related neuroendocrine prostate cancer is a distinctive category of prostate cancer that arises after intensive suppression of the androgen receptor by next-generation therapeutic inhibition of androgen receptor signaling. The biological processes that set in motion the series of events resulting in transformation of adenocarcinoma to neuroendocrine carcinoma include genomic (loss of tumor suppressors TP53 and RB1, amplification of oncogenes N-MYC and Aurora Kinase A, dysregulation of transcription factors SOX2, achaete-scute-homolog 1, and others) as well as epigenomic (DNA methylation, EZH2 overexpression, and others). Pathologic diagnosis is key to effective therapy for this disease, and this is aided by localizing metastatic lesions for biopsy using radioligand imaging in the appropriate clinical context. As our understanding of biology evolves, there has been increased morphologic recognition and characterization of tumor phenotypes that are present in this advanced post-treatment setting. New and promising biomarkers (delta-like ligand 3 and others) have been discovered, which opens up novel therapeutic avenues including immunotherapy and antibody-drug conjugates for this lethal disease with currently limited treatment options.

Renal cell carcinoma (RCC) with tuberous sclerosis complex (TSC)/mammalian target of rapamycin (MTOR) pathway-related genomic alterations have been classically described in hereditary TSC syndrome setting involving germline mutations, whereby cells with a bi-allelic inactivation of genes originate tumors in a classic tumor-suppressor "two-hit" Knudson paradigm. Initial studies of TSC-associated RCC categorized tumors into 3 broad heterogeneous morphologic groups: RCC with smooth muscle stroma, chromophobe-like, and eosinophilic-macrocytic. Recently, a similar morphologic spectrum has been increasingly recognized in novel and emerging entities characterized by somatic mutations in the TSC1/2 and MTOR in patients who do not suffer from the TSC. Correct recognition of RCC with TSC / MTOR mutations is critical for accurate prognostication because such tumors with aggressive behavior have the potential to be tailored to mTOR inhibitors. Whether TSC/MTOR mutated renal epithelial neoplasms represent a distinct molecular class has been confounded by the fact that TSC1/2 , and the gene encoding the downstream protein MTOR, are mutated secondarily in ∼5% of the more common subtypes of RCC, including the commonest subtype of clear cell RCC. This review summarizes the expanding morphologic spectrum of renal tumors with TSC/mTOR pathway alterations, specifically for sporadically occurring tumors where these genomic alterations likely are primary pathologic events. Finally, a practical surgical pathology approach to handling these tumors, and a conceptual framework of renal epithelial tumors with TSC/MTOR mutations as a "family of tumors", is presented.

Alterations in DNA damage response (DDR) and related genes are present in up to 25% of advanced prostate cancers (PCa). Most frequently altered genes are involved in the homologous recombination repair, the Fanconi anemia, and the mismatch repair pathways, and their deficiencies lead to a highly heterogeneous spectrum of DDR-deficient phenotypes. More than half of these alterations concern non- BRCA DDR genes. From a therapeutic perspective, poly-ADP-ribose polymerase inhibitors have demonstrated robust clinical efficacy in tumors with BRCA2 and BRCA1 alterations. Mismatch repair-deficient PCa, and a subset of CDK12-deficient PCa, are vulnerable to immune checkpoint inhibitors. Emerging data point to the efficacy of ATR inhibitors in PCa with ATM deficiencies. Still, therapeutic implications are insufficiently clarified for most of the non- BRCA DDR alterations, and no successful targeted treatment options have been established.

In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.

TFE3 -rearranged renal cell carcinoma (RCC) is a distinct, uncommon entity with more than 20 different fusion partners identified; however, histomorphology may be suggestive of specific fusion partners in select TFE3 -rearranged RCCs. For example, most MED15 :: TFE3 fusion associated RCCs exhibit multilocular cystic morphology, mimicking multilocular cystic renal neoplasm of low malignant potential. Here we present a case of MED15 :: TFE3 RCC in an older adult and review the literature with an emphasis on practical diagnostic approaches for predominantly cystic, low-grade, clear cell renal tumors.

Social media use in pathology has continued to grow and become more mainstream among pathologists, trainees, and medical students over the past decade. Twitter has historically been (and still seems to be) a positive platform for the social media pathology community to engage with each other virtually (ie, PathTwitter). However, as a new era of Twitter leadership began to unfold in October 2022, a young platform called "Mastodon" began to gain notice within this community as the hashtag #PathMastodon became prevalent. Founded in 2016 by Eugen Rochko, Mastodon is a decentralized, open-sourced, ads-free platform intended to promote public knowledge in a safe and public manner. When compared with Twitter, however, Mastodon is globally much smaller, and its medical professional server called "Med-Mastodon" is more cumbersome with certain features (eg, tracking analytics and username changes). Nevertheless, this new platform, which looks and feels much like Twitter, has great potential to provide continued medical education and virtual excellence among the social media pathology community. Thus, the purpose of this review is to provide a relevant synopsis of how Mastodon, Med-Mastodon, and #PathMastodon may benefit pathologists, trainees, and medical students who use social media. A qualitative analysis of pertinent peer-reviewed and non-peer-reviewed materials relative to the topic will be performed. In addition, we will provide a comparison of Mastodon and Twitter, provide example figures of #PathMastodon and related posts, and elaborate on the importance this discussion brings to the social media pathology community.

The classification of thymoma continues to be a source of controversy in pathology. The difficulties in histologic classification are evident from the number of proposals that have been offered over the years, as well as for the continuous changes and modifications introduced by the World Health Organization to their classification system over the past 20 years. We analyze here some of the issues involved in the classification of these tumors and the difficulties encountered for practicing pathologists in deciphering the "letters and numbers" system devised by the World Health Organization. We would like to propose an alternate approach to thymoma histologic classification that capitalizes on the basic observation of their cytologic features and incorporates the pattern of growth resulting from the interplay of the tumor cells with other cellular constituents as a secondary characteristic. The proposed histologic classification provides a simplified, reproducible means of histologically categorizing these tumors and can be easily understood by most practicing pathologists in simple and clear morphologic terms.

Large Language Models are forms of artificial intelligence that use deep learning algorithms to decipher large amounts of text and exhibit strong capabilities like question answering and translation. Recently, an influx of Large Language Models has emerged in the medical and academic discussion, given their potential widespread application to improve patient care and provider workflow. One application that has gained notable recognition in the literature is ChatGPT, which is a natural language processing "chatbot" technology developed by the artificial intelligence development software company OpenAI. It learns from large amounts of text data to generate automated responses to inquiries in seconds. In health care and academia, chatbot systems like ChatGPT have gained much recognition recently, given their potential to become functional, reliable virtual assistants. However, much research is required to determine the accuracy, validity, and ethical concerns of the integration of ChatGPT and other chatbots into everyday practice. One such field where little information and research on the matter currently exists is pathology. Herein, we present a literature review of pertinent articles regarding the current status and understanding of ChatGPT and its potential application in routine diagnostic pathology. In this review, we address the promises, possible pitfalls, and future potential of this application. We provide examples of actual conversations conducted with the chatbot technology that mimic hypothetical but practical diagnostic pathology scenarios that may be encountered in routine clinical practice. On the basis of this experience, we observe that ChatGPT and other chatbots already have a remarkable ability to distill and summarize, within seconds, vast amounts of publicly available data and information to assist in laying a foundation of knowledge on a specific topic. We emphasize that, at this time, any use of such knowledge at the patient care level in clinical medicine must be carefully vetted through established sources of medical information and expertise. We suggest and anticipate that with the ever-expanding knowledge base required to reliably practice personalized, precision anatomic pathology, improved technologies like future versions of ChatGPT (and other chatbots) enabled by expanded access to reliable, diverse data, might serve as a key ally to the diagnostician. Such technology has real potential to further empower the time-honored paradigm of histopathologic diagnoses based on the integrative cognitive assessment of clinical, gross, and microscopic findings and ancillary immunohistochemical and molecular studies at a time of exploding biomedical knowledge.

The pathologic assessment of colorectal carcinoma specimens plays a crucial role in the therapeutic management of patients and disease prognostication. The TNM staging system is used globally and is a critical component of colorectal carcinoma pathology reporting. However, our experience informs us that there are significant variations in the assignment of the TNM stage, both between pathologists and between hospital centers. We identify several potential reasons for this, among them suboptimal gross and microscopic assessment of colorectal resection specimens and, later, nonuniformity in applying criteria set forth in pathologic TNM staging guidelines. In addition, some defining characteristics of the staging system remain poorly defined. We aim to enlist those issues with potential remedies to improve reproducibility and, therefore, multidisciplinary discussion.