Diagnostic Histopathology最新文献

{"title":"Neoadjuvant chemo-immunotherapy in non-small cell lung cancer: clinical rationale and methods of pathological assessment","authors":"Carol Kwon ,&nbsp;Karin Purshouse ,&nbsp;Iain D Phillips,&nbsp;David A Dorward","doi":"10.1016/j.mpdhp.2025.05.002","DOIUrl":"10.1016/j.mpdhp.2025.05.002","url":null,"abstract":"<div><div>Non-small cell lung cancer (NSCLC) remains a common cancer with poor outcomes, with even early stage, resectable tumours having a high recurrence rate. Over the past decade immunotherapy has been paradigm-changing in advanced, metastatic NSCLC while more recent evidence has demonstrated its important role as a neoadjuvant agent in surgically resectable disease. This has led to a significant shift in clinical practice and, in doing so, has altered requirements in the pathological assessment of surgical resection specimens. In this paper, we summarize the clinical, biological and pathological rationale behind neoadjuvant immunotherapy, describe the evidence base for this change in clinical practice and detail the central role of histopathology. Clinical trials have demonstrated marked event-free and overall survival advantages for combined immunotherapy and chemotherapy with pathological response an important surrogate marker of long-term outcome. We describe the key histopathological and molecular characteristics that render a patient eligible for neoadjuvant treatment as well as the requirements for assessment of surgical specimens to enable the accurate quantification of pathological response. In addition, the potential future roles for alternative measures of disease response are discussed, including circulating tumour DNA, immune cell phenotyping and artificial intelligence-based analyses.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 8","pages":"Pages 458-465"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of artificial intelligence in kidney neoplasms: usability of pathological data in enhancing classification, grading and prognostic and predictive models","authors":"Johannes Kläger,&nbsp;Maximilian C Koeller,&nbsp;Eva Compérat","doi":"10.1016/j.mpdhp.2025.04.005","DOIUrl":"10.1016/j.mpdhp.2025.04.005","url":null,"abstract":"<div><div>Renal cell carcinoma (RCC) is among the most common human malignancies, gold standard in diagnosis is still histology but poses challenges in classification, grading, reproducibility or identification of predictive markers. The increasing use and availability of artificial intelligence (AI) like machine learning and deep learning methods, rose hope of improving those issues. The literature is expanding rapidly and in such experimental setting promising results were shown in distinguishing RCC subtypes and grades and leveraging digital pathology data in AI-integrated multimodal approaches combining histopathologic, genetic, and clinical data enhancing prognostic and predictive models. However, significant limitations hinder clinical implementation, like missing of prospective evaluation, underrepresentation of rare subtypes and evolving classification systems. Also the \"black box\" nature of some AI models and resource intensiveness raise concerns about transparency and feasibility.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 432-437"},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of artificial intelligence in bladder cancer: a histopathologic perspective","authors":"Maximilian C Koeller,&nbsp;Garbiel Wasinger,&nbsp;Eva Compérat","doi":"10.1016/j.mpdhp.2025.04.004","DOIUrl":"10.1016/j.mpdhp.2025.04.004","url":null,"abstract":"<div><div>Artificial Intelligence has shown promising results in the context of cancer diagnostics, especially due to the advancements in Digital and Computational Pathology. With regards to Bladder Cancer, AI Systems have shown to be capable of solving complex problems such as cancer detection, tumor grading, detection of lymph node metastasis or even the prediction of lymph node or mutation status (e.g. FGFR3) based solely on Hematoxylin &amp; Eosin morphology. Furthermore, AI systems can aid pathologists by autonomously generating synoptic reports from Whole Slide Images. Against this backdrop, this review aims to provide a high level, yet comprehensive overview on the latest advancements of AI in bladder cancer, from a histopathological perspective, while discussing the current challenges in this field. In line with this scope, while highly interesting, applications of AI in the context of cystoscopy, cytology, immunohistochemistry, radiology and bioinformatics will not be discussed.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 424-431"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atrophic-pattern prostatic adenocarcinoma: a diagnostic pitfall","authors":"Ka Wing Eric Wong,&nbsp;Tanjot Singh,&nbsp;Jo-An Roulson","doi":"10.1016/j.mpdhp.2025.04.009","DOIUrl":"10.1016/j.mpdhp.2025.04.009","url":null,"abstract":"<div><div>The atrophic pattern of prostatic adenocarcinoma is an uncommon histological pattern of acinar prostatic adenocarcinoma. Due to its deceptively benign histological appearance, it can be misdiagnosed as a benign entity. We report a case of atrophic pattern prostatic adenocarcinoma in an elderly male patient, highlighting key histopathological findings and prognostic implications. This pattern closely resembles benign atrophy, and we discuss the differences in architectural and cytological features, as well as the role of immunohistochemistry as a diagnostic adjunct. It is vital to recognise benign-appearing variants of prostatic adenocarcinoma to prevent misdiagnosis and ensure appropriate clinical management.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 447-450"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case report of a rare epithelioid angiosarcoma of the bladder","authors":"Scarlet Brockmoeller,&nbsp;Selina Bhattaria,&nbsp;Rachel Thomas,&nbsp;William Merchant","doi":"10.1016/j.mpdhp.2025.04.007","DOIUrl":"10.1016/j.mpdhp.2025.04.007","url":null,"abstract":"<div><div>Angiosarcoma is a rare malignant vascular neoplasm, which can arise in the soft tissue of the skin, thorax, breast, digestive, female genital and urinary tracts. It poses a diagnostic challenge due to its variable morphological appearances and immunohistochemical staining pattern. We present here a rare case of a primary epithelioid angiosarcoma of the bladder, and discuss further the morphological appearances, important differential diagnoses, and specific immunohistochemical and genetic characteristics which aid in its correct diagnosis.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 441-443"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence: redefining the future of prostate cancer diagnostics","authors":"Eva Compérat,&nbsp;Rainer Grobholz","doi":"10.1016/j.mpdhp.2025.04.001","DOIUrl":"10.1016/j.mpdhp.2025.04.001","url":null,"abstract":"<div><div>Artificial intelligence (AI) is revolutionizing the diagnosis and management of prostate cancer (PCa), one of the most common cancers worldwide. Despite its high incidence, PCa's mortality rate remains relatively low, yet its heterogeneity poses significant diagnostic and therapeutic challenges. Clinicians face difficulties in distinguishing between indolent and aggressive forms of the disease, compounded by limitations in biomarkers and traditional diagnostic methods, such as serological markers, multiparametric MRI (mpMRI), and histopathological evaluation of prostate biopsies. AI offers innovative solutions by improving diagnostic precision, reducing interobserver variability, and streamlining workflows across multiple domains, including radiology, pathology, immunohistochemistry (IHC), and genomics. In radiology, AI-integrated systems enhance the interpretation of mpMRI, outperforming radiologists using the PI-RADS standard in identifying clinically significant PCa while minimizing false positives. Similarly, in pathology, AI algorithms refine tumor grading by accurately identifying Gleason patterns, perineural invasion, and other diagnostic features. Studies have demonstrated the ability of AI to serve as a second-read system, reducing workloads and supporting pathologists in delivering consistent, high-quality diagnoses. AI's role in IHC includes the evaluation of prognostic markers such as Ki-67 and PTEN, where it improves accuracy and aids in predicting patient outcomes. Tools like virtual multiplexing further advance IHC by enabling simultaneous analysis of multiple biomarkers without compromising morphological integrity. In genomics and proteomics, AI facilitates the identification of novel biomarkers using mass spectrometry, offering non-invasive diagnostic approaches and personalized therapeutic strategies. While AI demonstrates substantial potential in PCa diagnostics, it is not intended to replace clinicians but to serve as an invaluable adjunct. The integration of AI with standardized, diverse datasets and clinical workflows holds the promise of advancing PCa care through enhanced precision, efficiency, and patient outcomes.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 405-409"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entering the era of spatial transcriptomics: opportunities and challenges for pathology","authors":"Anna M Sozanska,&nbsp;Carlo Pescia,&nbsp;Emily Thomas,&nbsp;Daniel J Royston,&nbsp;Rosalin A Cooper","doi":"10.1016/j.mpdhp.2025.03.001","DOIUrl":"10.1016/j.mpdhp.2025.03.001","url":null,"abstract":"<div><div>Recent years have seen the emergence of spatial transcriptomic (ST) platforms that are capable of comprehensive whole-section gene expression profiling of tissues with up to subcellular resolution. ST platforms offer novel insights into tissue microenvironments that define health and disease. This article will introduce the principles of ST and outline the commonly used platforms with their relative strengths and weaknesses. It will also discuss key considerations for experimental design and data analysis workflows. Finally, the importance of pathological oversight of ST workflows will be discussed, including the future potential of ST in clinical practice.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 5","pages":"Pages 257-266"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in haematopathology: current perspective and future directions","authors":"Carlo Pescia,&nbsp;Anna M Sozanska,&nbsp;Emily Thomas,&nbsp;Rosalin A Cooper","doi":"10.1016/j.mpdhp.2025.03.002","DOIUrl":"10.1016/j.mpdhp.2025.03.002","url":null,"abstract":"<div><div>Artificial intelligence (AI) is revolutionizing pathology by improving diagnostic accuracy and efficiency, particularly in pattern recognition within visual data. Whilst the role of AI in hematopathology is advancing, it is still behind the progress seen in solid tumor analysis. In areas such as lymph node and bone marrow pathology AI can support quantitative analysis, differential diagnosis, molecular prediction, and the exploration of novel biomarkers. However, challenges remain, including the need for extensive clinical validation, overcoming dataset limitations, and addressing potential overfitting issues. Future advancements will hinge on integrating both supervised and unsupervised learning techniques, improving digital pathology adoption to increase the availability of multicenter datasets, and harnessing emerging technologies such as spatial omics. Overall, AI is poised to augment pathologists' capabilities, offering more precise diagnostic tools and insights. However, the ultimate interpretation of data will remain a pathologist's domain, with AI-based methods serving as a complementary tool rather than a replacement for conventional microscopy.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 5","pages":"Pages 267-276"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Malignant Hurtle cell struma ovarii: oncocytic thyroid carcinoma arising in an ovarian dermoid","authors":"Emily Leung,&nbsp;Suzannah Hazeldine,&nbsp;Sonali Natu,&nbsp;Tim Bracey","doi":"10.1016/j.mpdhp.2025.03.007","DOIUrl":"10.1016/j.mpdhp.2025.03.007","url":null,"abstract":"<div><div>Mature cystic teratoma, also known as ovarian dermoid cyst, is the most common ovarian germ cell tumour, accounting for 20% of ovarian neoplasms. Most are benign, with malignant transformation occurring in 1–2% of cases. Transformation to thyroid carcinoma is rare and usually of papillary subtype. Here we report a case of oncocytic (Hurtle cell) thyroid follicular carcinoma arising from a mature cystic teratoma. The patient presented with post-menopausal bleeding and underwent a salpingo-oophorectomy. Histological examination revealed the mass to be a struma ovarii dermoid cyst with an oncocytic thyroid carcinoma infiltrating into the surrounding ovarian parenchyma. To our knowledge this is the first report of follicular thyroid carcinoma with widespread oncocytic differentiation in the literature.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 5","pages":"Pages 304-306"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case report on a schwannoma-like/Verocay body-prominent benign leiomyoma in the rectum","authors":"Alan King Lun Liu,&nbsp;Joanne Chapman","doi":"10.1016/j.mpdhp.2025.03.008","DOIUrl":"10.1016/j.mpdhp.2025.03.008","url":null,"abstract":"<div><div>Verocay bodies are typically found in schwannoma and are often heralded as classical features of the lesion but they are not pathognomonic and can occasionally be found in other neoplasms, including leiomyomas and gastrointestinal stromal tumours (GIST) within the gastrointestinal (GI) tract. Hence, it is important to bear this differential diagnosis in mind and utilize an immunohistochemical panel to aid with the diagnosis. Here we present an unusual case of a 62-year-old male with an incidental rectal mass found on digital rectal examination upon presenting to urology with increased lower urinary tract symptoms. Histology of the rectal mass showed a spindle cell proliferation with prominent Verocay bodies seen throughout the specimen. Immunohistochemistry demonstrated that the tumour was positive for actin and desmin, negative for S100, DOG1, CD34 and CD117 (c-KIT), confirming it to being a Verocay body-prominent leiomyoma.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 5","pages":"Pages 307-309"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}