Laboratory Investigation最新文献

{"title":"Aryl Hydrocarbon Receptor Deficiency Upregulates Intercellular Adhesion Molecule 1 in Retinal Pigment Epithelial Cells and Contributes to Retinal Inflammation","authors":"Tsung-Min Yang ,&nbsp;Te-Chao Fang ,&nbsp;Yu-Cheng Lee ,&nbsp;Chen-Chen Lee ,&nbsp;Yen-Ju Chan ,&nbsp;Ida Fitriana ,&nbsp;Yu-Wen Cheng ,&nbsp;Ching-Hao Li","doi":"10.1016/j.labinv.2025.104197","DOIUrl":"10.1016/j.labinv.2025.104197","url":null,"abstract":"<div><div>Retinal pigment epithelium (RPE) cells, located between the photoreceptors and choroid, play a crucial role in maintaining retinal health and function. They act as immunosuppressive barriers, preventing immune cell infiltration from the choroid. Retinal inflammation contributes to the development of various ocular diseases. The aryl hydrocarbon receptor (AHR) is a well-established ligand-dependent transcription factor that mediates potent anti-inflammatory signals following ligand binding. AHR expression is notably reduced under several conditions that negatively affect the retina. We hypothesized that AHR protein loss may impair RPE cell function, shifting them toward a proinflammatory phenotype. In this study, we investigated the proinflammatory pathways activated by AHR knockout (AHR-KO) and examined associated retinal phenotypic changes in AHR-KO mice. Our findings suggest that AHR deficiency may enhance the activity of αvβ3-integrin, extracellular signal–regulated kinase 1/2, and p65 subunit of nuclear factor kappa B, leading to an upregulation of intercellular adhesion molecule 1 (ICAM1) and promoting monocyte adhesion in vitro. Introducing an AHR-green fluorescent protein into AHR-KO RPE cells or pretreating the cells with pharmacologic inhibitors targeting αvβ3 (cyclo[RGDfk]), focal adhesion kinase (PF573228), phospholipase C (U73122), extracellular signal–regulated kinase 1/2 (U0126), and nuclear factor kappa B (Bay11-7082) prevented ICAM1 induction in AHR-KO RPE cells. These results suggest that the proinflammatory pathway is driven by AHR deficiency. In AHR-KO mice, retinal tissues showed ICAM1 accumulation, microglial activation, and migration, indicating chronic retinal inflammation because of AHR deficiency. These mice also displayed early-onset electroretinogram degeneration. Collectively, our data support the protective role of AHR in maintaining RPE cell physiology and retinal health.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104197"},"PeriodicalIF":5.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lacking TRPA1 Cation Channel Impairs Primary Closure of a Stromal Incision Injury in a Mouse Cornea","authors":"Shu-ichiro Sasaki ,&nbsp;Takayoshi Sumioka ,&nbsp;Shingo Yasuda ,&nbsp;Masayasu Miyajima ,&nbsp;Hiroki Iwanishi ,&nbsp;Peter Sol Reinach ,&nbsp;Yuka Okada ,&nbsp;Shizuya Saika","doi":"10.1016/j.labinv.2025.104193","DOIUrl":"10.1016/j.labinv.2025.104193","url":null,"abstract":"<div><div>The cornea is a highly sensory, innervated, avascular tissue that consists of epithelium, keratocytes, endothelium, and an extracellular matrix. We evaluated the effects of gene knockout (KO) of the <em>TRPA1</em>, a membrane cation channel potentially activated by various external stimuli, on the wound-healing process in corneal stroma following an incision injury in mice. TRPA1 protein was detected markedly in the corneal epithelium and cells in the stroma in a healthy, uninjured wild-type cornea. Deletion of <em>TRPA1</em> gene function delayed wound closure of a full-thickness incision injury in the corneal stroma. Peak of the appearance of Sox10-positive (Schwann cell marker) and Sox2- or p75-expressing (both repair-type Schwann cell markers) cells in the healing stroma was at day 2 postincision injury in a wild-type mouse, but was delayed in a <em>TRPA</em>1-KO mouse during the healing process. Expression of TGFβ1 mRNA was suppressed, in association with a reduction in p-Smad3 expression in stromal cells, by <em>TRPA1</em> gene deletion. We also observed that the loss of <em>TRPA1</em> suppressed the appearance of myofibroblasts and expression of <em>Col1a1</em> and fibronectin in the healing stroma. In vitro gel culture study showed that chemical TRPA1 inhibition attenuated TGFβ-induced fibroblast contractility. These results indicate that TRPA1 is involved in the process of corneal stromal wound healing in response to tissue laceration in mice. The phenotype was associated with the attenuation of the generation of repair Schwann cells, TGFβ signaling in stromal cells, keratocyte-myofibroblast transformation, and collagen-type I expression.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104193"},"PeriodicalIF":5.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning–Based Retinoblastoma Protein Subtyping of Pulmonary Large-Cell Neuroendocrine Carcinoma on Small Hematoxylin and Eosin–Stained Specimens","authors":"Teodora E. Trandafir ,&nbsp;Frank W.J. Heijboer ,&nbsp;Farhan Akram ,&nbsp;Jules L. Derks ,&nbsp;Yunlei Li ,&nbsp;Lisa M. Hillen ,&nbsp;Ernst-Jan M. Speel ,&nbsp;Zsolt Megyesfalvi ,&nbsp;Balazs Dome ,&nbsp;Andrew P. Stubbs ,&nbsp;Anne-Marie C. Dingemans ,&nbsp;Jan H. von der Thüsen","doi":"10.1016/j.labinv.2025.104192","DOIUrl":"10.1016/j.labinv.2025.104192","url":null,"abstract":"<div><div>Molecular subtyping of pulmonary large-cell neuroendocrine carcinoma (LCNEC) based on retinoblastoma protein (pRb) expression may influence systemic treatment decisions. Current histomorphologic assessments of hematoxylin and eosin–stained tissue samples cannot reliably differentiate LCNEC molecular subtypes. This study explores the potential of deep learning (DL) to identify histologic patterns that distinguish these subtypes, by developing a custom convolutional neural network to predict the binary expression of pRb in small LCNEC tissue samples. Our model was trained, cross-validated, and tested on tissue microarray cores from 143 resection specimens and biopsies from 21 additional patients, with corresponding immunohistochemical pRb status. The best-performing DL model achieved a patient-wise balanced accuracy value of 0.75 and an area under the receiver operating characteristic curve value of 0.77 when tested on biopsies, significantly outperforming the hematoxylin and eosin–based subtype classification by lung pathologists. Explainable artificial intelligence techniques further highlighted coarse chromatin patterns and distinct nucleoli as distinguishing features for pRb retained status. Meanwhile, pRb lost cases were characterized by limited cytoplasm and morphologic similarities with small cell lung cancer. These findings suggest that DL analysis of small histopathology samples could ultimately replace immunohistochemical pRb testing. Such a development may assist in guiding chemotherapy decisions, particularly in metastatic cases.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104192"},"PeriodicalIF":5.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Consensus Molecular Subtyping of Muscle-Invasive Bladder Cancer Via 3' RNA Sequencing of Formalin-Fixed Paraffin-Embedded Tissues: Potential Impact for Clinical and Trial Settings","authors":"Miriam Angeloni ,&nbsp;Sven Wach ,&nbsp;Helge Taubert ,&nbsp;Danijel Sikic ,&nbsp;Bernd Wullich ,&nbsp;Christian Matek ,&nbsp;Reiner Strick ,&nbsp;Pamela L. Strissel ,&nbsp;Arndt Hartmann ,&nbsp;Markus Eckstein ,&nbsp;Fulvia Ferrazzi","doi":"10.1016/j.labinv.2025.104191","DOIUrl":"10.1016/j.labinv.2025.104191","url":null,"abstract":"<div><div>Transcriptome-based tumor classification has enhanced the molecular characterization of muscle-invasive bladder cancer (MIBC) subtypes. However, the degraded nature of formalin-fixed paraffin-embedded (FFPE) material and the expensive sequencing costs for routine use have limited the use of subtypes in clinical and trial settings. Here, we present an optimized analysis workflow for MIBC molecular subtype prediction from FFPE samples. FFPE material from 240 MIBC samples was sequenced using QuantSeq 3′ mRNA sequencing with unique molecular identifiers (UMIs) and analyzed via a customized RNA-Seq pipeline. The association of consensus subtypes with histology and immunohistochemical expression of core basal/luminal protein markers was assessed. In addition, subtype robustness was explored by simulating scenarios at lower sequencing depths and without UMIs. Five MIBC consensus subtypes were identified in the cohort. The basal/squamous group showed higher expression of KRT14, KRT5, and CD44, and was mainly divergent squamous. Vice versa, luminal, and stroma-rich subtypes had conventional urothelial or urothelial subtype histology, with higher expression of KRT20, FOXA1, and GATA3. The neuroendocrine-like samples had small cell neuroendocrine histology and were negative for luminal/basal markers. Subtype calling from 24 matched fresh-frozen samples analyzed with full-length RNA-Seq showed 87.5% agreement. Furthermore, the subtypes were robust to decreasing sequencing depths and to the absence of UMIs. Taken together, we provide a robust and cost-effective workflow for MIBC consensus molecular subtyping from FFPE-derived RNA. This workflow can be easily implemented as a molecular pathological assay for patient care, clinical trials, and translational research.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104191"},"PeriodicalIF":5.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1016/S0023-6837(25)00094-7","DOIUrl":"10.1016/S0023-6837(25)00094-7","url":null,"abstract":"","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 5","pages":"Article 104184"},"PeriodicalIF":5.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Prognostic Stratification With 2023 International Federation of Gynecology and Obstetrics Staging in Endometrial Cancer Reflecting Poor Prognosis of Aggressive Histological Types and p53 Abnormality","authors":"Miseon Lee ,&nbsp;Heesoo Yoon ,&nbsp;Ujae Kim ,&nbsp;Jun Kang ,&nbsp;Yeon Bi Han ,&nbsp;Keun Ho Lee ,&nbsp;Sung Jong Lee ,&nbsp;Sook Hee Hong ,&nbsp;Dong Hoon Suh ,&nbsp;Kidong Kim ,&nbsp;Jae Hong No ,&nbsp;Yong Beom Kim ,&nbsp;Hyojin Kim ,&nbsp;Ahwon Lee","doi":"10.1016/j.labinv.2025.104189","DOIUrl":"10.1016/j.labinv.2025.104189","url":null,"abstract":"<div><div>This study compares the distribution and prognostic impact of the 2009 and 2023 International Federation of Gynecology and Obstetrics (FIGO) staging systems for endometrial cancer and their impact on the 2022 European Society for Medical Oncology (ESMO) risk classification. Patients were restaged according to the 2009 FIGO staging system, the 2023 FIGO staging system, and the 2023 FIGO staging system with molecular classification. Risk groups were assigned according to the 2022 ESMO guidelines using each staging system. Among 679 patients, 139 (20.5%) experienced stage migration when transitioning from the 2009 FIGO staging system to the 2023 FIGO staging system with molecular classification, with 121 (17.8%) upstaged and 18 (2.7%) downstaged. Most changes were from FIGO stage I to stage II, primarily due to p53 abnormality, aggressive histological type, or extensive/substantial lymphovascular space invasion. Hazard ratios for overall survival, disease-free survival, and event-free survival increased with advancing stage groups in all systems, showing the greatest differences when the 2023 FIGO staging system with molecular classification was used. The newly introduced FIGO stages IC, IIC (both representing aggressive histological types), and IICmp53abn (associated with p53 abnormality) in the 2023 FIGO staging system were associated with worse outcomes, similar to FIGO stage III. The prognostic predictability of the 2022 ESMO risk group was minimally affected by the transition from the 2009 FIGO to the 2023 FIGO staging system, as the factors introduced in the new FIGO system were already incorporated into the 2022 ESMO risk classification. Only 17 (2.5%) patients experienced a change in their assigned risk group. The 2023 FIGO staging system showed improved prognostic stratification over the 2009 FIGO staging system, particularly by reflecting the poor prognosis of aggressive histological types and p53 abnormality.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104189"},"PeriodicalIF":5.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rigor and Reproducibility of Spatial Transcriptomics Performed on Clinically Sourced Human Tissues","authors":"Kelly D. Smith ,&nbsp;James W. MacDonald ,&nbsp;Xianwu Li ,&nbsp;Emily Beirne ,&nbsp;Galen Stewart ,&nbsp;Theo K. Bammler ,&nbsp;Shreeram Akilesh","doi":"10.1016/j.labinv.2025.104190","DOIUrl":"10.1016/j.labinv.2025.104190","url":null,"abstract":"<div><div>Spatial transcriptomic profiling enables precise quantification of gene expression with simultaneous localization of expression profiles onto tissue structures. Several implementations of these approaches have been released as commercialized platforms that will allow multiple laboratories to improve our understanding of human disease mechanisms. There is also intense interest in applying these methods in clinical trials or as laboratory-developed tests to aid in the diagnosis of disease. However, before these technologies can be broadly deployed in clinical research and diagnostics, it is necessary to thoroughly understand their performance in real-world conditions. In this study, we vet the technical reproducibility, data normalization methods, and assay sensitivity focusing predominantly on one widely used spatial transcriptomics methodology, digital spatial profiling. We also compare its performance with a single molecular imager, a newer platform with single-cell resolution. Using clinically sourced human kidney tissues and biopsies as exemplars, we find that digital spatial profiling exhibits high rigor and reproducibility. We show that normalization approaches can impact the biological interpretation of spatial transcriptomics data. Although there is good concordance between multicellular and single-cell resolution methods, there are tradeoffs in cost, execution time, and sensitivity of detection, which may affect which approach is chosen. Our study lays a practical foundation for the incorporation of spatial transcriptomics methods into clinical workflows.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104190"},"PeriodicalIF":5.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144017503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Analysis of Rectal Cancer Biopsies With the Digital Pathology Segmentation Algorithm QuantCRC Associates With Therapy Response and Recurrence","authors":"Reetesh K. Pai ,&nbsp;Cody Eslinger ,&nbsp;Kenneth Lee ,&nbsp;Lama Farhat Farchoukh ,&nbsp;Daniel Walden ,&nbsp;Oluwadunni Emiloju ,&nbsp;Michael Storandt ,&nbsp;Catherine E. Hagen ,&nbsp;Ashlyn Pfeiffer ,&nbsp;Mohammad Bassam Sonbol ,&nbsp;Daniel Ahn ,&nbsp;Tanios Bekaii-Saab ,&nbsp;Andrew Ness ,&nbsp;Joleen Hubbard ,&nbsp;Christina Wu ,&nbsp;Thomas Westerling-Bui ,&nbsp;Riyue Bao ,&nbsp;Fang-Shu Ou ,&nbsp;Rish K. Pai","doi":"10.1016/j.labinv.2025.104187","DOIUrl":"10.1016/j.labinv.2025.104187","url":null,"abstract":"<div><div>We examined whether QuantCRC, a digital pathology segmentation algorithm, on pretherapy rectal cancer biopsies is associated with pathologic complete response (pCR) to neoadjuvant therapy, recurrence-free survival (RFS), and transcriptomic spatial profiling. QuantCRC was evaluated in an observational cohort of 288 pretherapy biopsies and a separate validation cohort of 37 pretherapy biopsies of rectal adenocarcinoma from patients undergoing neoadjuvant therapy. Associations between QuantCRC features and clinical outcomes, pCR, and RFS were analyzed using multivariable logistic regression and Cox proportional hazards modeling, respectively. QuantCRC variables were also correlated with transcriptomic digital spatial profiling of 37 pretreatment biopsies of cT3N⁺ rectal cancer. QuantCRC-derived lymphocytes per square millimeter of tumor epithelium (tumor-infiltrating lymphocytes [TILs]) was significantly associated with pCR (multivariate odds ratio, 1.05; 95% CI, 1.02-1.10; <em>P</em> = .038). QuantCRC-derived TILs were significantly higher in pretherapy biopsies with pCR (91.3 vs 55.9 lymphocytes/mm²; <em>P</em> = .004). The validation cohort confirmed that only QuantCRC-derived TILs in pretherapy biopsies of rectal cancer were significantly associated with complete response to neoadjuvant therapy. QuantCRC %high tumor grade was independently associated with worse RFS (multivariate hazards ratio, 1.27; 95% CI, 1.09-1.47; <em>P</em> = .002). Patients with ≥10.1% high tumor grade identified by QuantCRC had significantly reduced RFS (5-year RFS 69% vs 83%, log-rank <em>P</em> = .007). Transcriptomic profiling identified high interleukin (IL)-6/JAK/STAT3 signaling within immune cells to be associated with worse RFS (adjusted <em>P</em> = .01). Tumors with low IL-6/JAK/STAT3 expression within immune cells had significantly higher TILs compared with tumors with high expression (median, 152 vs 97 TILs/mm²; <em>P</em> = .039). Biopsy-adapted QuantCRC in pretherapy rectal cancer may be helpful in identifying patients who achieve pCR and are at risk for recurrence.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 8","pages":"Article 104187"},"PeriodicalIF":5.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinicopathologic and Genomic Characteristics of Minute Pulmonary Meningothelial-like Nodules","authors":"Haochen Li ,&nbsp;Zhicheng Huang ,&nbsp;Yadong Wang ,&nbsp;Chao Guo ,&nbsp;Xiaoyu Li ,&nbsp;Weixun Zhou ,&nbsp;Sha Wang ,&nbsp;Na Bai ,&nbsp;Hanlin Chen ,&nbsp;Bowen Li ,&nbsp;Daoyun Wang ,&nbsp;Zhibo Zheng ,&nbsp;Zhongxing Bing ,&nbsp;Yang Song ,&nbsp;Yuan Xu ,&nbsp;Guanghua Huang ,&nbsp;Ka Luk Fung ,&nbsp;Lan Song ,&nbsp;Naixin Liang ,&nbsp;Shanqing Li","doi":"10.1016/j.labinv.2025.104188","DOIUrl":"10.1016/j.labinv.2025.104188","url":null,"abstract":"<div><div>Minute pulmonary meningothelial-like nodules (MPMNs) are benign lung lesions with histologic characteristics similar to meningeal epithelium. MPMNs often lead to misdiagnosis for their similar radiologic characteristics to malignant nodules. The pathogenesis of MPMNs remains unclear, and this research primarily focused on mutations in a limited number of genes, lacking a comprehensive analysis of their mutational landscape. We collected 134 MPMNs from 88 patients with pathologic examinations at Peking Union Medical College Hospital in the past 5 years. We performed whole-exome sequencing on 12 MPMNs and 7 adenocarcinoma lesions from 6 patients. In our PUMCH-MPMN cohort, we provided clinical, pathologic, radiologic, and follow-up characteristics of patients with MPMNs. Our study demonstrated the pathologic diagnostic value of 3 classic MPMN diagnostic markers (epithelial membrane antigen, progesterone receptor, and vimentin) and the novel marker somatostatin receptor 2. It also suggested the preoperative differential diagnostic value of positron emission tomography/computed tomography. We also classified MPMNs into 4 types based on different discovery methods and verified the diagnostic value of traditional and novel immunohistochemical markers. We performed whole-exome sequencing and revealed that MPMNs harbor mutations enriched in cell cycle and cytoskeleton assembly pathways. On the other hand, classical meningioma-related mutations, such as <em>NF2</em> mutations, were not detected. These findings provide new evidence for the hypothesis that MPMNs arise from reactive proliferation rather than share a common origin with meningiomas, contributing to a better understanding of MPMNs among clinicians and pathologists, reducing misdiagnosis, and improving patient care.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 9","pages":"Article 104188"},"PeriodicalIF":5.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning–Powered Whole Slide Image Analysis in Cancer Pathology","authors":"Chengrun Dang ,&nbsp;Zhuang Qi ,&nbsp;Tao Xu ,&nbsp;Mingkai Gu ,&nbsp;Jiajia Chen ,&nbsp;Jie Wu ,&nbsp;Yuxin Lin ,&nbsp;Xin Qi","doi":"10.1016/j.labinv.2025.104186","DOIUrl":"10.1016/j.labinv.2025.104186","url":null,"abstract":"<div><div>Pathology is the cornerstone of modern cancer care. With the advancement of precision oncology, the demand for histopathologic diagnosis and stratification of patients is increasing as personalized cancer therapy relies on accurate biomarker assessment. Recently, rapid development of whole slide imaging technology has enabled digitalization of traditional histologic slides at high resolution, holding promise to improve both the precision and efficiency of histopathologic evaluation. In particular, deep learning approaches, such as Convolutional Neural Network, Graph Convolutional Network, and Transformer, have shown great promise in enhancing the sensitivity and accuracy of whole slide image (WSI) analysis in cancer pathology because of their ability to handle high-dimensional and complex image data. The integration of deep learning models with WSIs enables us to explore and mine morphologic features beyond the visual perception of pathologists, which can help automate clinical diagnosis, assess histopathologic grade, predict clinical outcomes, and even discover novel morphologic biomarkers. In this review, we present a comprehensive framework for incorporating deep learning with WSIs, highlighting how deep learning–driven WSI analysis advances clinical tasks in cancer care. Furthermore, we critically discuss the opportunities and challenges of translating deep learning–based digital pathology into clinical practice, which should be considered to support personalized treatment of cancer patients.</div></div>","PeriodicalId":17930,"journal":{"name":"Laboratory Investigation","volume":"105 7","pages":"Article 104186"},"PeriodicalIF":5.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143971291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}