Faculty Bibliography

The aggressiveness of prostate cancer is primarily assessed from histopathological data using the Gleason scoring system. Conventional artificial intelligence (AI) approaches can predict Gleason scores, but often lack explainability, which may limit clinical acceptance. Here, we present an alternative, inherently explainable AI that circumvents the need for post-hoc explainability methods. The model was trained on 1,015 tissue microarray core images, annotated with detailed pattern descriptions by 54 international pathologists following standardized guidelines. It uses pathologist-defined terminology and was trained using soft labels to capture data uncertainty. This approach enables robust Gleason pattern segmentation despite high interobserver variability. The model achieved comparable or superior performance to direct Gleason pattern segmentation (Dice score:

Bladder cancer is a common type of urological cancer with high recurrence and mortality rates. Currently, it is diagnosed and monitored using minimal invasive cystoscopies and biopsies. Urinary cytology, the most widely accepted noninvasive and more economic urinary diagnosis method, aims to detect high grade urothelial carcinoma with a high specificity but low sensitivity, especially for detecting low-grade tumors. With advancements in molecular techniques, urine based liquid biopsy, artificial intelligence, and the growing interest in precision cytopathology, identification of urinary biomarkers for effective cancer screening, diagnosis, risk stratification, and therapeutic response monitoring has been a key focus of bladder cancer research and clinical practice guideline development. Urine allows noninvasive access to morphological, transcriptomic, epigenetic, and genomic materials from exfoliated cells in contact with tumor tissue. This review offers a comprehensive evaluation of the current utility of urinary biomarkers and technological innovations in cancer diagnosis and minimal residual disease detection. We also discuss the challenges and prospects for integrating molecular cytopathology into daily clinical practice.

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.

BACKGROUND AND OBJECTIVE/OBJECTIVE:Urinary tumor DNA (utDNA) has emerged as a promising biomarker in the care, diagnosis, early detection, recurrence monitoring, and prognosis of bladder cancer (BCa). Its noninvasive nature, ease of access, and cost effectiveness make it an attractive option for both patients and health care providers. This review describes the current state of utDNA as a marker of BCa. METHODS:Articles published between 2015 and 2025 on current utDNA-based techniques in BCa were identified and analyzed for relevance and insight into utDNA research and usage. KEY FINDINGS AND LIMITATIONS/UNASSIGNED:Recent investigations underscore the noninvasiveness and superior tumor detection capabilities of utDNA, particularly in the detection of minimal residual disease. Moreover, utDNA provides actionable information, such as tumor grade and staging information, to support precise treatment decisions, including targeted immunotherapy regimens and bladder preservation strategies. Although utDNA has shown promising results in small studies, larger studies must be performed before it can be considered as a standard procedure in clinical practice. CONCLUSIONS AND CLINICAL IMPLICATIONS/CONCLUSIONS:Urinary tumor DNA has demonstrated great potential to improve on most, if not all, stages of detection, treatment, and monitoring of BCa. By preserving the low cost and noninvasiveness of urine cytology, and by replacing its suboptimal accuracy with a precision rivaling and often exceeding cystoscopy and circulating tumor DNA-based methods, utDNA offers patients a more comfortable, repeatable, and accurate way of detecting BCa. With increased sensitivity and accuracy, everything from low-grade tumors to the earliest signs of recurrence can be detected more effectively, optimizing patient treatment courses and improving outcomes.

PURPOSE/OBJECTIVE:This study investigates the potential of DALL·E 3, an artificial intelligence (AI) model, to generate synthetic pathologic images of prostate cancer (PCa) at varying Gleason grades. The aim is to enhance medical education and research resources, particularly by providing diverse case studies and valuable teaching tools. METHODS:This study uses DALL·E 3 to generate 30 synthetic images of PCa across various Gleason grades, guided by standard Gleason pattern descriptions. Nine uropathologists evaluated these images for realism and accuracy compared with actual hematoxylin and eosin (H&E)-stained slides using a scoring system. RESULTS:< .05), with Gleason 5 images achieving the highest scores and accurately depicting critical pathologic characteristics. Limitations included a lack of fine nuclear detail, essential for identifying malignancy, which may affect the images' diagnostic utility. CONCLUSION/CONCLUSIONS:DALL·E 3 shows promise in generating customized pathologic images that can aid in education and resource expansion within pathology. However, ethical concerns, such as the potential misuse of AI-generated images for data falsification, highlight the need for responsible oversight. Collaboration between technology firms and pathologists is essential for the ethical integration of AI in pathology practices.

OBJECTIVES/OBJECTIVE:TERT promoter mutations are not infrequently encountered in thyroid carcinomas; however, it is unclear if additional molecular alterations may play a role in determining tumor behavior. METHODS:Fine-needle aspiration (FNA) specimens from 32 patients with TERT promoter mutations detected by ThyroSeq v3 from 4 institutions were included in the study. FNA diagnoses, molecular results, and surgical follow-up were retrospectively reviewed and analyzed. RESULTS:There were 5 benign and 27 malignant neoplasms, including 7 high-grade thyroid carcinomas (HGCs) on histopathologic follow-up. Of 4 cases with an isolated TERT mutation, 3 (75%) cases were malignant. Of 17 cases harboring a co-occurring TERT mutation with 1 additional molecular alteration, 13 (76%) displayed malignancy on histopathologic follow-up. All 11 cases with TERT mutations plus 2 or more additional molecular alterations were malignant on follow-up. Furthermore, HGC was not seen in cases with an isolated TERT mutation, while 80% of cases harboring TERT mutations plus 3 additional molecular alterations showed HGC. CONCLUSIONS:TERT promoter mutations are commonly associated with malignancy, particularly HGCs, when multiple co-occurring molecular alterations are present. However, TERT promoter mutations may occasionally be detected in benign thyroid neoplasms when encountered in isolation or with fewer than 2 additional molecular alterations.

Monogenic diseases of immune dysregulation should be considered in the evaluation of children presenting with recurrent neutrophilic dermatoses in association with systemic signs of inflammation, autoimmune disease, hematologic abnormalities, and opportunistic or recurrent infections. We report the case of a 2-year-old boy presenting with a neutrophilic dermatosis, found to have a novel likely pathogenic germline variant of the IKAROS Family Zinc Finger 1 (IKZF1) gene; the mutation likely results in a loss of function dimerization defective protein based on reports and studies of similar variants. IKZF1 variants could potentially lead to aberrant neutrophil chemotaxis and development of neutrophilic dermatoses. Long-term surveillance is required to monitor the development of hematologic malignancy, autoimmunity, immunodeficiency, and infection in patients with pathogenic IKZF1 germline variants.