Pub Date : 2025-08-01Epub Date: 2025-06-04DOI: 10.1016/j.mpdhp.2025.05.002
Carol Kwon , Karin Purshouse , Iain D Phillips, David A Dorward
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.
{"title":"Neoadjuvant chemo-immunotherapy in non-small cell lung cancer: clinical rationale and methods of pathological assessment","authors":"Carol Kwon , Karin Purshouse , Iain D Phillips, 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}
Pub Date : 2025-07-01Epub Date: 2025-05-06DOI: 10.1016/j.mpdhp.2025.04.004
Maximilian C Koeller, Garbiel Wasinger, Eva Compérat
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 & 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.
{"title":"The use of artificial intelligence in bladder cancer: a histopathologic perspective","authors":"Maximilian C Koeller, Garbiel Wasinger, 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 & 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":"2025-07-01","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}
Pub Date : 2025-07-01Epub Date: 2025-05-05DOI: 10.1016/j.mpdhp.2025.04.009
Ka Wing Eric Wong, Tanjot Singh, Jo-An Roulson
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.
{"title":"Atrophic-pattern prostatic adenocarcinoma: a diagnostic pitfall","authors":"Ka Wing Eric Wong, Tanjot Singh, 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":"2025-07-01","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}
Pub Date : 2025-07-01Epub Date: 2025-05-05DOI: 10.1016/j.mpdhp.2025.04.007
Scarlet Brockmoeller, Selina Bhattaria, Rachel Thomas, William Merchant
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.
{"title":"A case report of a rare epithelioid angiosarcoma of the bladder","authors":"Scarlet Brockmoeller, Selina Bhattaria, Rachel Thomas, 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":"2025-07-01","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}
Pub Date : 2025-07-01Epub Date: 2025-05-02DOI: 10.1016/j.mpdhp.2025.04.001
Eva Compérat, Rainer Grobholz
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.
{"title":"Artificial intelligence: redefining the future of prostate cancer diagnostics","authors":"Eva Compérat, 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":"2025-07-01","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}
Pub Date : 2025-07-01Epub Date: 2025-04-26DOI: 10.1016/j.mpdhp.2025.04.003
Gabriel Wasinger, Maximilian C Koeller, Eva Compérat
Artificial intelligence (AI) is driving a revolution in pathology, transforming traditional workflows and addressing critical challenges in the field. This review highlights the integration of AI into immunohistochemistry (IHC) and molecular pathology (MP), where its potential to enhance diagnostic accuracy, efficiency, and reproducibility is becoming increasingly evident. In IHC, AI tools offer solutions to limitations such as subjective biomarker scoring, interobserver variability, and growing workloads by enabling automated and consistent analysis of diagnostic and predictive markers. Similarly, in MP, AI addresses challenges in tumor annotation, genetic mutation interpretation and prediction, and integration of multidimensional data to streamline workflows and enhance precision medicine. By combining computational power with pathologists' expertise, AI holds the promise of reshaping pathology into a more efficient, reliable, and scalable discipline. However, continued efforts in validation, transparency, and cost optimization will be crucial to fully realize AI's transformative potential in clinical pathology.
{"title":"Pathology in the artificial intelligence era: practical insights for immunohistochemistry and molecular pathology","authors":"Gabriel Wasinger, Maximilian C Koeller, Eva Compérat","doi":"10.1016/j.mpdhp.2025.04.003","DOIUrl":"10.1016/j.mpdhp.2025.04.003","url":null,"abstract":"<div><div>Artificial intelligence (AI) is driving a revolution in pathology, transforming traditional workflows and addressing critical challenges in the field. This review highlights the integration of AI into immunohistochemistry (IHC) and molecular pathology (MP), where its potential to enhance diagnostic accuracy, efficiency, and reproducibility is becoming increasingly evident. In IHC, AI tools offer solutions to limitations such as subjective biomarker scoring, interobserver variability, and growing workloads by enabling automated and consistent analysis of diagnostic and predictive markers. Similarly, in MP, AI addresses challenges in tumor annotation, genetic mutation interpretation and prediction, and integration of multidimensional data to streamline workflows and enhance precision medicine. By combining computational power with pathologists' expertise, AI holds the promise of reshaping pathology into a more efficient, reliable, and scalable discipline. However, continued efforts in validation, transparency, and cost optimization will be crucial to fully realize AI's transformative potential in clinical pathology.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 416-423"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489540","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}
Pub Date : 2025-07-01Epub Date: 2025-05-10DOI: 10.1016/j.mpdhp.2025.04.005
Johannes Kläger, Maximilian C Koeller, Eva Compérat
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.
{"title":"Application of artificial intelligence in kidney neoplasms: usability of pathological data in enhancing classification, grading and prognostic and predictive models","authors":"Johannes Kläger, Maximilian C Koeller, 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":"2025-07-01","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}
Pub Date : 2025-07-01Epub Date: 2025-04-28DOI: 10.1016/j.mpdhp.2025.04.008
Anastasiya Kret, Ali Al-Omari, Bart Wagner
A female in her 30s presented with worsening lower limb swelling. Her past medical history included primary hypothyroidism, learning difficulties and an atrial septal defect. She was found to have a nephrotic syndrome and was referred to Nephrology with worsening oedema and proteinuria. The initial blood workup showed a mildly elevated serum C3 level and a polyclonal increase in serum IgM level. Renal biopsy was performed which on H&E demonstrated glomeruli with mild mesangial hypercellularity and prominent capillary walls. Electron microscopy showed severe podocyte foot processes effacement and unusual podocyte inclusions which were protruding into the glomerular basement membrane. She was diagnosed with minimal change disease. The exact nature of these peculiar podocyte inclusions remained unknown until the entity of podocyte infolding glomerulopathy (PIG) was published in the English language literature in 2008. In retrospect, we believe the changes observed in our case were due to PIG.
{"title":"Podocyte infolding glomerulopathy: a rare entity","authors":"Anastasiya Kret, Ali Al-Omari, Bart Wagner","doi":"10.1016/j.mpdhp.2025.04.008","DOIUrl":"10.1016/j.mpdhp.2025.04.008","url":null,"abstract":"<div><div>A female in her 30s presented with worsening lower limb swelling. Her past medical history included primary hypothyroidism, learning difficulties and an atrial septal defect. She was found to have a nephrotic syndrome and was referred to Nephrology with worsening oedema and proteinuria. The initial blood workup showed a mildly elevated serum C3 level and a polyclonal increase in serum IgM level. Renal biopsy was performed which on H&E demonstrated glomeruli with mild mesangial hypercellularity and prominent capillary walls. Electron microscopy showed severe podocyte foot processes effacement and unusual podocyte inclusions which were protruding into the glomerular basement membrane. She was diagnosed with minimal change disease. The exact nature of these peculiar podocyte inclusions remained unknown until the entity of podocyte infolding glomerulopathy (PIG) was published in the English language literature in 2008. In retrospect, we believe the changes observed in our case were due to PIG.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 444-446"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489645","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}
Pub Date : 2025-07-01Epub Date: 2025-04-30DOI: 10.1016/j.mpdhp.2025.04.002
Rainer Grobholz, Andrew Janowczyk, Inti Zlobec
Digital Pathology (DP) is revolutionizing diagnostic surgical pathology, transitioning from traditional microscopy to digital workflows that enhance diagnostic accuracy, streamline processes, and enable cost efficiency. While fully digitized laboratories demonstrate improved efficiency and engagement, adoption remains uneven globally due to infrastructure, cost, and organizational barriers. As a result, European and Asian institutions demonstrate adoption of DP with varying strategies tailored to resource availability and goals. Here we highlight important issues when planning and implementing DP systems. Successful implementation requires robust IT infrastructure (server, random access memory, network speed), including integrated image management and laboratory information systems, and scalable storage solutions. Selecting the appropriate scanners and optimizing workflows are critical, guided by specific institutional needs such as slide volume, turnaround times, and digitization scope. Financially, DP demands significant initial investment but offers long-term benefits in operational efficiency, cost savings, and workforce optimization. Image analysis integration and national initiatives are key drivers for DP adoption, addressing diagnostic challenges and fostering collaboration. Overcoming obstacles such as high costs, technical complexity, and resistance from pathologists is essential. As technology advances and costs decrease, DP is poised to transform pathology with improved diagnostic workflows, quality control, and accessibility.
{"title":"Transforming pathology into digital pathology: highway to hell or stairway to heaven?","authors":"Rainer Grobholz, Andrew Janowczyk, Inti Zlobec","doi":"10.1016/j.mpdhp.2025.04.002","DOIUrl":"10.1016/j.mpdhp.2025.04.002","url":null,"abstract":"<div><div>Digital Pathology (DP) is revolutionizing diagnostic surgical pathology, transitioning from traditional microscopy to digital workflows that enhance diagnostic accuracy, streamline processes, and enable cost efficiency. While fully digitized laboratories demonstrate improved efficiency and engagement, adoption remains uneven globally due to infrastructure, cost, and organizational barriers. As a result, European and Asian institutions demonstrate adoption of DP with varying strategies tailored to resource availability and goals. Here we highlight important issues when planning and implementing DP systems. Successful implementation requires robust IT infrastructure (server, random access memory, network speed), including integrated image management and laboratory information systems, and scalable storage solutions. Selecting the appropriate scanners and optimizing workflows are critical, guided by specific institutional needs such as slide volume, turnaround times, and digitization scope. Financially, DP demands significant initial investment but offers long-term benefits in operational efficiency, cost savings, and workforce optimization. Image analysis integration and national initiatives are key drivers for DP adoption, addressing diagnostic challenges and fostering collaboration. Overcoming obstacles such as high costs, technical complexity, and resistance from pathologists is essential. As technology advances and costs decrease, DP is poised to transform pathology with improved diagnostic workflows, quality control, and accessibility.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 410-415"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489741","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}
Pub Date : 2025-07-01Epub Date: 2025-04-25DOI: 10.1016/j.mpdhp.2025.04.006
Caroline Cartlidge, Selina Bhattarai
We present a case of a teenage boy with haematuria who underwent a trans urethral removal of bladder tumour (TURBT) for multiple solid bladder lesions with sandy patches. Investigations led to a diagnosis of schistosomiasis. The clinical, radiological, macroscopic, and microscopic histological findings are highlighted. We discuss the complex parasitic life cycle of Schistosoma and the well evidenced link between schistosomiasis and bladder cancer, specifically high-grade squamous cell carcinoma.
{"title":"Schistosomiasis: extensive urinary bladder infiltration in an unusual case of suspected cancer","authors":"Caroline Cartlidge, Selina Bhattarai","doi":"10.1016/j.mpdhp.2025.04.006","DOIUrl":"10.1016/j.mpdhp.2025.04.006","url":null,"abstract":"<div><div>We present a case of a teenage boy with haematuria who underwent a trans urethral removal of bladder tumour (TURBT) for multiple solid bladder lesions with sandy patches. Investigations led to a diagnosis of schistosomiasis. The clinical, radiological, macroscopic, and microscopic histological findings are highlighted. We discuss the complex parasitic life cycle of Schistosoma and the well evidenced link between schistosomiasis and bladder cancer, specifically high-grade squamous cell carcinoma.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 7","pages":"Pages 438-440"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489543","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}
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