Modern Pathology最新文献

Peripheral T-cell lymphomas with gamma-delta phenotype (GDTCL) are rare lymphoid malignancies. Beyond the well-recognized entities of extranodal lymphomas with gamma-delta phenotype as defined by the fifth edition of the World Health Organization Classification of Hematolymphoid Tumors and 2022 International Consensus Classification, there is a group of poorly defined gamma-delta T-cell lymphomas with predominantly nodal presentation, termed as nodal GDTCL (nGDTCL). In this study, we present a series of 12 cases of Epstein-Barr virus-negative nGDTCL, highlighting the clinical, histopathological, and molecular features of this rare entity. Seven cases reported in the literature were included in the analysis. Of the 12 cases, nGDTCL shows an increased incidence in elderly men, with a median age of 65.5 years. All cases presented primarily with enlarged lymph nodes, and 4 cases (4/12, 33.3%) showed involvement of extranodal sites, including skin, liver, spleen, and bone marrow. Histologically, 9 cases showed a diffuse and monomorphic proliferation of mostly medium-to-large lymphoid cells, whereas 3 cases demonstrated lymphoepithelioid morphology. All cases (12/12, 100%) were positive for CD3 and TCRγδ. CD4, CD8, and CD56 were positive in 66.7% (8/12), 25% (3/12), and 8.3% (1/11) of cases, respectively. Most cases (8/12, 66.7%) showed a noncytotoxic phenotype. Using immunohistochemistry, the majority of cases (6/8, 75.0%) belonged to the peripheral T-cell lymphoma-GATA3 subtype with GATA3 and/or CCR4 expression and a noncytotoxic CD4-positive phenotype. Two cases (2/8, 25%) belonged to the peripheral T-cell lymphoma-TBX21 subtype, of which 1 displayed a cytotoxic CD8-positive phenotype. Next-generation sequencing was performed in 9 cases, and TP53 mutation was detected in 66.7% (6/9) of the cases. Mutations of ATM and KSR2 were identified in 2 cases each. It remains uncertain whether nGDTCL represents a distinct entity, and further studies are needed for better characterization. Nonetheless, nodal-based GDTCL should be distinguished from secondary nodal involvement by other extranodal GDTCL and Epstein-Barr virus-positive T/NK-cell lymphoproliferative diseases.

Although immune checkpoint inhibitors (ICIs) have revolutionized modern oncology, they are also associated with immune-related adverse events (irAEs). Previous histopathologic descriptions of organ-related inflammatory changes do not consider systemic effects of ICIs, because of the absence of comprehensive autopsy studies. We performed a retrospective study on 42 whole-body autopsies of patients treated with ICIs from January 2011 to March 2024 to determine the frequency, organ distribution, and morphology of ICI-associated inflammatory changes as well as their clinical relevance. Twenty-three of 42 (54.8%) patients presented irAEs with inflammatory changes in at least one organ. Most frequent irAEs were ICI-related hypophysitis (N = 12; 28.6%), myocarditis (N = 8; 19.0%), pneumonitis (N = 5; 11.9%), hepatitis (N = 6; 14.3%), and adrenalitis (N = 5; 11.9%). ICI-related inflammation was mainly characterized by lymphohistiocytic and macrophage-rich tissue infiltrates, whereas a granulomatous "sarcoid-like" reaction was observed in 1 patient. Cause of death was attributable to ICI therapy in 7 (16.7%) patients, with ICI-associated myocarditis as the most common cause of death (N = 5; 71.4%). Clinically, irAEs were unsuspected in 5 of 7 ICI-related deaths (71.4%). Among irAEs, myocarditis has been clinically undiagnosed in 5 out of 8 cases (62.5%). Encephalitis was identified only at autopsy in all cases (N = 2). Hypophysitis was clinically unsuspected in 8 of 12 (66.7%) cases. Patients who died from irAEs developed more frequently a complete tumor regression than patients who died from other causes (P = .018). Of note, ICI-related myocarditis and pneumonitis were both associated with a systemic occurrence irAEs. Our study demonstrates that some irAEs, especially myocarditis, hypophysitis, and encephalitis, are clinically underdiagnosed. Autopsy remains a valuable tool to monitor diagnostic accuracy and therapeutic side effects in patients who died under ICI therapy.

Detecting somatic structural variants (SVs), copy number variants (CNVs), and mutations in bone and soft tissue tumors is essential for accurately diagnosing, treating, and prognosticating outcomes. Optical genome mapping (OGM) holds promise to yield useful data on SVs and CNVs but requires fresh or snap-frozen tissues. This study aimed to evaluate the clinical utility of data from OGM compared with current standard-of-care cytogenetic testing. We evaluated 60 consecutive specimens from bone and soft tissue tumors using OGM and karyotyping, fluorescence in situ hybridization, gene fusion assays, and deep next-generation sequencing. OGM accurately identified diagnostic SVs/CNVs previously detected by karyotyping and fluorescence in situ hybridization (specificity = 100%). OGM identified diagnostic and pathogenic SVs/CNVs (∼23% of cases) undetected by karyotyping (cryptic/submicroscopic). OGM allowed the detection and further characterization of complex structural rearrangements including chromoanagenesis (27% of cases) and complex 3- to 6-way translocations (15% of cases). In addition to identifying 321 SVs and CNVs among cases with chromoanagenesis events, OGM identified approximately 9 SVs and 12 CNVs per sample. A combination of OGM and deep next-generation sequencing data identified diagnostic, disease-associated, and pathogenic SVs, CNVs, and mutations in ∼98% of the cases. Our cohort contained the most extensive collection of bone and soft tissue tumors profiled by OGM. OGM had excellent concordance with standard-of-care cytogenetic testing, detecting and assigning high-resolution genome-wide genomic abnormalities with higher sensitivity than routine testing. This is the first and largest study to provide insights into the clinical utility of combined OGM and deep sequencing for the pathologic diagnosis and potential prognostication of bone and soft tissue tumors in routine clinical practice.

The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such nongenerative AI methods, rooted in traditional rules-based systems, enhance diagnostic accuracy, efficiency, and consistency within medicine. Key concepts and the application of supervised learning models (ie, classification and regression) such as decision trees, support vector machines, linear and logistic regression, K-nearest neighbor, and neural networks are explained along with the newer landscape of neural network-based nongenerative foundation models. Unsupervised learning techniques, including clustering, dimensionality reduction, and anomaly detection, are also discussed for their roles in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of nongenerative AI algorithms for analyzing whole slide images are also highlighted. The performance, explainability, and reliability of nongenerative AI models essential for clinical decision-making is also reviewed, as well as challenges related to data quality, model interpretability, and risk of data drift. An understanding of which AI-ML models to employ and which shortcomings need to be addressed is imperative to safely and efficiently leverage, integrate, and monitor these traditional AI tools in clinical practice and research.

Although most patients with pulmonary carcinoid (PC) can be cured by surgery, relapse may occur until 15 years after resection in up to 10% of patients. This is unpredictable at the outset, necessitating extensive follow-up (FU). We sought to determine whether an immunohistochemical marker panel (OTP, CD44, and Ki-67) could better indicate relapse-free survival (RFS) and increase uniformity among pathologists regarding carcinoid classification. To this purpose, all surgically resected PC (2003-2012) were identified in the Dutch cancer/pathology registry, and a matched relapse vs nonrelapse cohort (ratio 1:2, N = 161) was created. Cases were revised by 4 pathologists and additionally for immunohistochemistry (IHC) markers. The marker panel was applied to the complete population-based cohort (N = 536) to investigate the negative predictive value (NPV) of relapse. Median FU was 86.7 months. WHO classification among pathologists revealed poor overall agreement (mitotic count: 0.380, necrosis: 0.476) compared with IHC markers (Ki-67: 0.917, OTP: 0.984, CD44: 0.976). The mean NPV of all pathologists increased from 0.74 (World Health Organization, WHO) to 0.85 (IHC marker panel). IHC risk stratification of the complete cohort, regardless of subtype, showed a statistically significant difference in RFS between patients with high risk (n = 222) and low risk (n = 314), with an NPV of 95.9%. In conclusion, our results support the use of biomarker-driven FU management for patients with PC as the OTP/CD44/KI-67 marker panel can reliably predict which patients will probably not develop relapse over time and may benefit from a more limited postoperative follow-up. Furthermore, IHC marker assessment by pathologists for PC stratification is superior to traditional WHO typing.

Fibroblastic foci (FF) are considered important findings of usual interstitial pneumonia (UIP); however, they are not only specific to UIP but also observed in various fibrotic interstitial lung diseases (ILDs). Previous studies have reported the significance of FF comparing UIP with nonspecific interstitial pneumonia (NSIP) or secondary interstitial pneumonia, such as collagen vascular disease-related ILD (CVD-ILD) or fibrotic hypersensitivity pneumonitis (FHP). However, only few studies have mentioned their location, and no reports have shown significant results regarding their location. This study aimed to compare the spatial distribution of FF across various forms of ILDs, based on anatomical location. Among patients who underwent lung transplantation at Kyoto University Hospital between April 1, 2008, and March 31, 2023, those diagnosed with idiopathic pulmonary fibrosis (IPF) (n = 24), idiopathic NSIP (n = 11), CVD-ILD (n = 36), and FHP (n = 12) were included, and 744 slides were obtained. FF were classified into 4 categories: peripheral, such as subpleural/paraseptal; intralobular, along the alveolar wall (aFF); centrilobular (cFF); and distorted or dense fibrotic lesions. The number of total and each location's FF/cm² were counted, and the percentage of each location's FF was calculated. IPF showed more total FF and peripheral FF than NSIP. FHP had more cFF than CVD (P = .026) and NSIP (P = .018). The dFF was higher in IPF than that in CVD (P = .018) and NSIP (P = .039). The aFF/total FF ratio was higher in CVD than that in FHP (P = .021) and IPF (P < .001). A high cFF/total FF ratio was correlated with FHP versus IPF (P = .032). In conclusion, FF with existing peripheral and distorted/dense fibrosis were more closely related to IPF, whereas cFF were highly correlated with FHP. Moreover, a high aFF/total FF ratio was suggestive of CVD.

Uterine carcinosarcomas (UCS) are high-grade biphasic neoplasms with generally poor outcomes. Based on The Cancer Genome Atlas molecular classification of endometrial carcinomas, the majority of UCS are classified as copy-number high/serous-like (p53-abnormal); however, a small subset represent other molecular subtypes, including those that harbor POLE mutations. We identified 11 POLE-mutated (POLEmut) UCS across 3 institutions and assessed the clinical, histopathologic, immunohistochemical, and molecular features of these tumors. POLEmut UCS occurred in adult women (median age, 64 years; range, 48-79 years) and usually presented as The International Federation of Gynecology and Obstetrics 2009 clinical stage IA (n = 4) or IB (n = 3). Almost all tumors were predominantly carcinomatous (n = 10), with most showing endometrioid morphology (n = 7), followed by ambiguous (n = 4) and serous (n = 3) histotypes. By immunohistochemistry, 7 tumors showed aberrant or subclonally aberrant expression of p53, 6 of which harbored pathogenic mutations in TP53 by sequencing. Other frequent mutations included PIK3CA (10/11), PTEN (8/11), RB1 (7/11), ARID1A (7/11), ATM (6/11), PIK3RA (5/11), and FBXW7 (4/11). Two tumors demonstrated loss of mismatch repair protein expression, and 1 had subclonal loss. Heterologous differentiation was uncommon, and only chondrosarcomatous type (n = 2) was observed. Mean and median follow-ups were 24.3 and 14.1 months, respectively (range, 1.4-61.1 months). Ten patients (91%) had no recurrences or death from disease, although 3 of these had follow-up periods <1 year. One patient, with the subclonal POLE variant, presented with stage IV disease and died 1.4 months after surgery. In conclusion, POLEmut UCS demonstrate unique morphologic and immunohistochemical features compared with their p53-abnormal counterparts and may have significant prognostic differences. Our study supports full molecular classification of UCS. We also raise awareness for potentially assessing POLE mutation allele frequency and clonality in consideration of classifying a tumor as POLEmut.

The diagnosis and treatment of sinonasal small round epithelial/neuroepithelial malignancies depend on the expression of conventional neuroendocrine markers (NEMs), such as synaptophysin, chromogranin A, INSM1, and CD56/NCAM1. However, these tumors remain diagnostically challenging because of overlapping histologic and immunohistochemical features. The transcriptional regulators ASCL1, NEUROD1, POU2F3, and YAP1 are novel NEM (nNEM) used for the subtyping of small-cell lung cancer (SCLC). Here, we assessed the immunoexpression of nNEM in 76 sinonasal malignancies, including 27 olfactory neuroblastomas (ONB), 14 small-cell neuroendocrine carcinomas (SCNEC), 2 large-cell neuroendocrine carcinomas, 12 sinonasal undifferentiated carcinomas (SNUC), 7 olfactory carcinomas (OC), 11 SWI/SNF-deficient carcinomas, and 3 neuroendocrine tumors. We correlated nNEM expression with the extent of neuroendocrine (NE) differentiation, as defined by averaged conventional NEM expression (NE-high: H-score, ≥150; NE-low: H-score, <150). Dominant NE subtypes were defined by the nNEM with the highest H-score. Coexpression of 2 nNEM with <100 H-score difference defined a codominant NE subtype. NE differentiation positively correlated with NEUROD1 and negatively with YAP1 expression (P < .0001). ONB were NE-high (96%), and all were NEUROD1-dominant/POU2F3-negative/ASCL1-negative (low)/YAP1-negative (low). In contrast to ONB, all OC were NE-low, mostly (71%) codominant subtypes, NEUROD1-low (negative) (100%, P = .0001), and YAP1 high (71%; P = .0001). Most notably, all SNUC were POU2F3-(co)dominant/NEUROD1-negative irrespective of the IDH2 mutations. Sinonasal tumors with high POU2F3 expression showed enrichment for "tuft cell carcinoma" and tuft cell signatures (P = .009). Similar to SCLC, SCNEC was heterogeneous in terms of nNEM expression comprising several molecular subtypes, including ASCL1-(co)dominant (43%) cases. All SWI/SNF-deficient carcinomas were consistently ASCL1/NEUROD1/POU2F3-negative and YAP1-positive. ASCL1/NEUROD1/POU2F3/YAP1 are useful markers in the differential diagnosis of ONB, SNUC, OC, and SWI/SNF-deficient carcinomas. Subsets of SNUC and large-cell neuroendocrine carcinomas may represent tuft cell-like carcinomas, suggesting that the tuft cell could be explored as the cell of origin for these tumors. The therapeutic vulnerabilities associated with POU2F3 expression in SCLC suggest that a similar approach might be considered for POU2F3-positive carcinomas of the sinonasal tract. Given their diagnostic and possible therapeutic relevance, nNEM have the potential to transform the way we approach the diagnosis and management of sinonasal small round epithelial/neuroepithelial malignancies.

The rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) in medicine has prompted medical professionals to increasingly familiarize themselves with related topics. This also demands grasping the underlying statistical principles that govern their design, validation, and reproducibility. Uniquely, the practice of pathology and medicine produces vast amount of data that can be exploited by AI/ML. The emergence of generative AI, especially in the area of large language models and multimodal frameworks, represents approaches that are starting to transform medicine. Fundamentally, generative and traditional (eg, nongenerative predictive analytics) ML techniques rely on certain common statistical measures to function. However, unique to generative AI are metrics such as, but not limited to, perplexity and BiLingual Evaluation Understudy score that provide a means to determine the quality of generated samples that are typically unfamiliar to most medical practitioners. In contrast, nongenerative predictive analytics ML often uses more familiar metrics tailored to specific tasks as seen in the typical classification (ie, confusion metrics measures, such as accuracy, sensitivity, F1 score, and receiver operating characteristic area under the curve) or regression studies (ie, root mean square error and R²). To this end, the goal of this review article (as part 4 of our AI review series) is to provide an overview and a comparative measure of statistical measures and methodologies used in both generative AI and traditional (ie, nongenerative predictive analytics) ML fields along with their strengths and known limitations. By understanding their similarities and differences along with their respective applications, we will become better stewards of this transformative space, which ultimately enables us to better address our current and future needs and challenges in a more responsible and scientifically sound manner.