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Lee S, Arffman RK, Komsi EK, Lindgren O, Kemppainen J, Kask K, Saare M, Salumets A, Piltonen TT. Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium. J Pathol Inform 2024; 15:100364. [PMID: 38445292 PMCID: PMC10914580 DOI: 10.1016/j.jpi.2024.100364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/24/2024] [Accepted: 01/24/2024] [Indexed: 03/07/2024] Open
Abstract
Background The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists' assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women's samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.
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Affiliation(s)
- Seungbaek Lee
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
| | - Riikka K. Arffman
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
| | - Elina K. Komsi
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
| | - Outi Lindgren
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Janette Kemppainen
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Keiu Kask
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
| | - Merli Saare
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
| | - Andres Salumets
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm 14152, Sweden
| | - Terhi T. Piltonen
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
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Lee S, Arffman RK, Komsi EK, Lindgren O, Kemppainen JA, Metsola H, Rossi HR, Ahtikoski A, Kask K, Saare M, Salumets A, Piltonen TT. AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF). J Pathol Inform 2024; 15:100380. [PMID: 38827567 PMCID: PMC11140811 DOI: 10.1016/j.jpi.2024.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/20/2024] [Accepted: 04/26/2024] [Indexed: 06/04/2024] Open
Abstract
Background Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138- cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results The AI algorithm consistently and reliably distinguished CD138- and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36-0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders.
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Affiliation(s)
- Seungbaek Lee
- Department of Obstetrics and Gynaecology, Medical Research Center Oulu, Research Unit of Clinical Medicine, University of Oulu and Oulu University Hospital, Oulu 90220, Finland
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
| | - Riikka K. Arffman
- Department of Obstetrics and Gynaecology, Medical Research Center Oulu, Research Unit of Clinical Medicine, University of Oulu and Oulu University Hospital, Oulu 90220, Finland
| | - Elina K. Komsi
- Department of Obstetrics and Gynaecology, Medical Research Center Oulu, Research Unit of Clinical Medicine, University of Oulu and Oulu University Hospital, Oulu 90220, Finland
| | - Outi Lindgren
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Janette A. Kemppainen
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Hanna Metsola
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Henna-Riikka Rossi
- Department of Obstetrics and Gynaecology, Medical Research Center Oulu, Research Unit of Clinical Medicine, University of Oulu and Oulu University Hospital, Oulu 90220, Finland
| | - Anne Ahtikoski
- Department of Pathology, Turku University Hospital, Turku 20521, Finland
| | - Keiu Kask
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
| | - Merli Saare
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
| | - Andres Salumets
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm 14152, Sweden
| | - Terhi T. Piltonen
- Department of Obstetrics and Gynaecology, Medical Research Center Oulu, Research Unit of Clinical Medicine, University of Oulu and Oulu University Hospital, Oulu 90220, Finland
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Luikku AJ, Nerg O, Koivisto AM, Hänninen T, Junkkari A, Kemppainen S, Juopperi SP, Sinisalo R, Pesola A, Soininen H, Hiltunen M, Leinonen V, Rauramaa T, Martiskainen H. Deep learning assisted quantitative analysis of Aβ and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome. J Neuropathol Exp Neurol 2024:nlae083. [PMID: 39101555 DOI: 10.1093/jnen/nlae083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024] Open
Abstract
Neuropathologic changes of Alzheimer disease (AD) including Aβ accumulation and neuroinflammation are frequently observed in the cerebral cortex of patients with idiopathic normal pressure hydrocephalus (iNPH). We created an automated analysis platform to quantify Aβ load and reactive microglia in the vicinity of Aβ plaques and to evaluate their association with cognitive outcome in cortical biopsies of patients with iNPH obtained at the time of shunting. Aiforia Create deep learning software was used on whole slide images of Iba1/4G8 double immunostained frontal cortical biopsies of 120 shunted iNPH patients to identify Iba1-positive microglia somas and Aβ areas, respectively. Dementia, AD clinical syndrome (ACS), and Clinical Dementia Rating Global score (CDR-GS) were evaluated retrospectively after a median follow-up of 4.4 years. Deep learning artificial intelligence yielded excellent (>95%) precision for tissue, Aβ, and microglia somas. Using an age-adjusted model, higher Aβ coverage predicted the development of dementia, the diagnosis of ACS, and more severe memory impairment by CDR-GS whereas measured microglial densities and Aβ-related microglia did not correlate with cognitive outcome in these patients. Therefore, cognitive outcome seems to be hampered by higher Aβ coverage in cortical biopsies in shunted iNPH patients but is not correlated with densities of surrounding microglia.
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Affiliation(s)
- Antti J Luikku
- Institute of Clinical Medicine-Neurosurgery, University of Eastern Finland, Kuopio, Finland
- Neurosurgery of NeuroCenter, Kuopio University Hospital, Kuopio, Finland
| | - Ossi Nerg
- Neurology of NeuroCenter, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine-Neurology, University of Eastern Finland, Kuopio, Finland
| | - Anne M Koivisto
- Neurology of NeuroCenter, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine-Neurology, University of Eastern Finland, Kuopio, Finland
- Department of Neurosciences, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
- Department of Geriatrics/Rehabilitation and Internal Medicine, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
| | - Tuomo Hänninen
- Neurology of NeuroCenter, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine-Neurology, University of Eastern Finland, Kuopio, Finland
| | - Antti Junkkari
- Neurosurgery of NeuroCenter, Kuopio University Hospital, Kuopio, Finland
| | - Susanna Kemppainen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | | | - Rosa Sinisalo
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Alli Pesola
- Institute of Clinical Medicine-Neurosurgery, University of Eastern Finland, Kuopio, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine-Neurology, University of Eastern Finland, Kuopio, Finland
| | - Mikko Hiltunen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Ville Leinonen
- Institute of Clinical Medicine-Neurosurgery, University of Eastern Finland, Kuopio, Finland
- Neurosurgery of NeuroCenter, Kuopio University Hospital, Kuopio, Finland
| | - Tuomas Rauramaa
- Department of Pathology, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine-Pathology, University of Eastern Finland, Kuopio, Finland
| | - Henna Martiskainen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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Nyholm I, Sjöblom N, Pihlajoki M, Hukkinen M, Lohi J, Heikkilä P, Mutka A, Jahnukainen T, Davenport M, Heikinheimo M, Arola J, Pakarinen MP. Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia. Hepatol Commun 2023; 7:e0333. [PMID: 38051554 PMCID: PMC10697619 DOI: 10.1097/hc9.0000000000000333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/17/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND We aimed to quantify ductular reaction (DR) in biliary atresia using a neural network in relation to underlying pathophysiology and prognosis. METHODS Image-processing neural network model was applied to 259 cytokeratin-7-stained native liver biopsies of patients with biliary atresia and 43 controls. The model quantified total proportional DR (DR%) composed of portal biliary epithelium (BE%) and parenchymal intermediate hepatocytes (PIH%). The results were related to clinical data, Sirius Red-quantified liver fibrosis, serum biomarkers, and bile acids. RESULTS In total, 2 biliary atresia biopsies were obtained preoperatively, 116 at Kasai portoenterostomy (KPE) and 141 during post-KPE follow-up. DR% (8.3% vs. 5.9%, p=0.045) and PIH% (1.3% vs. 0.6%, p=0.004) were increased at KPE in patients remaining cholestatic postoperatively. After KPE, patients with subsequent liver transplantation or death showed an increase in DR% (7.9%-9.9%, p = 0.04) and PIH% (1.6%-2.4%, p = 0.009), whereas patients with native liver survival (NLS) showed decreasing BE% (5.5%-3.0%, p = 0.03) and persistently low PIH% (0.9% vs. 1.3%, p = 0.11). In Cox regression, high DR predicted inferior NLS both at KPE [DR% (HR = 1.05, p = 0.01), BE% (HR = 1.05, p = 0.03), and PIH% (HR = 1.13, p = 0.005)] and during follow-up [DR% (HR = 1.08, p<0.0001), BE% (HR = 1.58, p = 0.001), and PIH% (HR = 1.04, p = 0.008)]. DR% correlated with Sirius red-quantified liver fibrosis at KPE (R = 0.47, p<0.0001) and follow-up (R = 0.27, p = 0.004). A close association between DR% and serum bile acids was observed at follow-up (R = 0.61, p<0.001). Liver fibrosis was not prognostic for NLS at KPE (HR = 1.00, p = 0.96) or follow-up (HR = 1.01, p = 0.29). CONCLUSIONS DR predicted NLS in different disease stages before transplantation while associating with serum bile acids after KPE.
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Affiliation(s)
- Iiris Nyholm
- Section of Pediatric Surgery, Pediatric Liver and Gut Research Group, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Pediatric Research Center, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nelli Sjöblom
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Marjut Pihlajoki
- Pediatric Research Center, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Maria Hukkinen
- Section of Pediatric Surgery, Pediatric Liver and Gut Research Group, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jouko Lohi
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Päivi Heikkilä
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Aino Mutka
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Timo Jahnukainen
- Department of Pediatric Nephrology and Transplantation, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mark Davenport
- Department of Pediatric Surgery, King’s College Hospital, London, UK
| | - Markku Heikinheimo
- Pediatric Research Center, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Pediatrics, Washington University School of Medicine, St. Louis Children’s Hospital, St. Louis, Missouri, USA
- Department of Pediatrics, Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Johanna Arola
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mikko P. Pakarinen
- Section of Pediatric Surgery, Pediatric Liver and Gut Research Group, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Pediatric Research Center, Children and Adolescent Department, New Children’s Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Women’s and Children’s Health, Karolinska Institute, Stockholm, Sweden
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Mairinoja L, Heikelä H, Blom S, Kumar D, Knuuttila A, Boyd S, Sjöblom N, Birkman EM, Rinne P, Ruusuvuori P, Strauss L, Poutanen M. Deep learning based image analysis of liver steatosis in mouse models. THE AMERICAN JOURNAL OF PATHOLOGY 2023:S0002-9440(23)00171-2. [PMID: 37236505 DOI: 10.1016/j.ajpath.2023.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 02/28/2023] [Accepted: 04/13/2023] [Indexed: 05/28/2023]
Abstract
The incidence of non-alcoholic fatty liver disease (NAFLD) is a continuously growing health problem worldwide, along with obesity. Therefore, both novel methods to efficiently study the manifestation of NAFLD and to analyze drug efficacy in pre-clinical models are needed. In the present study, we developed a deep neural network -based model to quantify micro- and macrovesicular steatosis in the liver on hematoxylin-eosin stained whole slide images (WSIs), using the cloud-based platform, Aiforia Create (Aiforia Technologies, Helsinki, Finland). The training data included a total of 101 WSIs from dietary interventions of wild-type mice and from two genetically modified (GM) mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of micro- and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists, and correlated well with the liver fat content measured by EcoMRI ex vivo, and the correlation with total liver triglycerides were notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections, and thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts.
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Affiliation(s)
- Laura Mairinoja
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland.
| | - Hanna Heikelä
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Sami Blom
- Aiforia Technologies Oyj, Pursimiehenkatu 29-31 D, 00150 Helsinki, Finland
| | - Darshan Kumar
- Aiforia Technologies Oyj, Pursimiehenkatu 29-31 D, 00150 Helsinki, Finland
| | - Anna Knuuttila
- Aiforia Technologies Oyj, Pursimiehenkatu 29-31 D, 00150 Helsinki, Finland
| | - Sonja Boyd
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290 Helsinki, Finland
| | - Nelli Sjöblom
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290 Helsinki, Finland
| | - Eva-Maria Birkman
- Department of Pathology, Turku University Hospital and University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Petteri Rinne
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Pekka Ruusuvuori
- Cancer Research Unit, Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Leena Strauss
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Matti Poutanen
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland; Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 3, 413 90 Gothenburg, Sweden
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Allaume P, Rabilloud N, Turlin B, Bardou-Jacquet E, Loréal O, Calderaro J, Khene ZE, Acosta O, De Crevoisier R, Rioux-Leclercq N, Pecot T, Kammerer-Jacquet SF. Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13101799. [PMID: 37238283 DOI: 10.3390/diagnostics13101799] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/04/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
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Affiliation(s)
- Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Edouard Bardou-Jacquet
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Department of Liver Diseases CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Olivier Loréal
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Department of Pathology Henri Mondor, 94000 Créteil, France
- INSERM U955, Team Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers, 94000 Créteil, France
| | - Zine-Eddine Khene
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Urology, CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Oscar Acosta
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Renaud De Crevoisier
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Radiotherapy, Centre Eugène Marquis, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Thierry Pecot
- Biosit Platform UAR 3480 CNRS US18 INSERM U955, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
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Saffioti F, Vieira Motta R, Quaglia A. Histological evaluation in biliary diseases. Curr Opin Gastroenterol 2023; 39:75-82. [PMID: 36821454 DOI: 10.1097/mog.0000000000000908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
PURPOSE OF REVIEW This review focuses on recent developments of histopathology in the most common biliary disorders affecting adults. The reader is referred to other sources for the specialized topics on paediatric populations and post liver transplantation. RECENT FINDINGS Fibrosis stage at diagnosis is an independent predictor of liver transplant-free survival in patients with primary biliary cholangitis. Immunohistochemistry might have an important role in predicting response to treatment. New histological scoring systems with excellent correlation with long-term clinical outcomes are being developed in primary sclerosing cholangitis (PSC). Quantification of fibrosis with collagen proportionate area can improve risk stratification and could be particularly useful to assess treatment response in PSC.Gene sequencing on cytology and intrabiliary biopsy may improve risk stratification for cholangiocarcinoma. Genetic variants of ATP8B1, ABCB11 and ABCB4 are relatively common in adults with cholestatic liver disease. New causes of cholestatic liver injury have recently been described. SUMMARY Histology is often not necessary for the diagnosis of biliary disease, but can provide important information that may assist the clinician in patients' management. Histopathology remains crucial to confirm a diagnosis of cholangiocarcinoma, and to identify the pattern of biliary injury in immune-mediated cholangiopathies and rarer pathological entities.
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Affiliation(s)
- Francesca Saffioti
- Oxford University Hospitals NHS Foundation Trust, Oxford.,UCL Institute for Liver and Digestive Health, Royal Free Hospital and UCL, London
| | - Rodrigo Vieira Motta
- Nuffield Department of Medicine, Investigative Medicine, University of Oxford, Oxford
| | - Alberto Quaglia
- Royal Free London NHS Foundation Trust and UCL Cancer Institute, London, UK
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Kim M, Rizvi F, Shin D, Gouon-Evans V. Update on Hepatobiliary Plasticity. Semin Liver Dis 2023; 43:13-23. [PMID: 36764306 PMCID: PMC10005859 DOI: 10.1055/s-0042-1760306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
The liver field has been debating for decades the contribution of the plasticity of the two epithelial compartments in the liver, hepatocytes and biliary epithelial cells (BECs), to derive each other as a repair mechanism. The hepatobiliary plasticity has been first observed in diseased human livers by the presence of biphenotypic cells expressing hepatocyte and BEC markers within bile ducts and regenerative nodules or budding from strings of proliferative BECs in septa. These observations are not surprising as hepatocytes and BECs derive from a common fetal progenitor, the hepatoblast, and, as such, they are expected to compensate for each other's loss in adults. To investigate the cell origin of regenerated cell compartments and associated molecular mechanisms, numerous murine and zebrafish models with ability to trace cell fates have been extensively developed. This short review summarizes the clinical and preclinical studies illustrating the hepatobiliary plasticity and its potential therapeutic application.
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Affiliation(s)
- Minwook Kim
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Fatima Rizvi
- Department of Medicine, Gastroenterology Section, Center for Regenerative Medicine, Boston University and Boston Medical Center, Boston, Massachusetts
| | - Donghun Shin
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Valerie Gouon-Evans
- Department of Medicine, Gastroenterology Section, Center for Regenerative Medicine, Boston University and Boston Medical Center, Boston, Massachusetts
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Prominent Pseudoacini in Focal Nodular Hyperplasia: A Potential Diagnostic Pitfall. Am J Surg Pathol 2022; 46:1380-1385. [PMID: 35749760 DOI: 10.1097/pas.0000000000001931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Pseudoacini are generally a morphologic feature of hepatocellular carcinoma (HCC), being absent or rare in benign hepatocytic tumors, such as hepatocellular adenoma. However, rarely these can be seen in focal nodular hyperplasia (FNH) and may pose diagnostic challenges, especially when prominent. The study was aimed to evaluate the occurrence of pseudoacini in FNH and their clinicopathologic correlations. A total of 95 FNH cases diagnosed from 2005 to 2020 were included in the study. A pseudoacinus was defined as a circular arrangement of hepatocytes around a central dilated lumen present within the lobular parenchyma of the lesion with or without inspissated bile. Among the 95 FNH cases, 28 (29.5%) showed pseudoacini, which were prominent in 12 (12.6%) cases. Of these 3 occurred in patients above 50 years old. The pseudoacini were numerous in 3 cases, leading to an initial consideration of HCC in the differential diagnosis, and 1 case was diagnosed as well-differentiated hepatocellular neoplasm on initial biopsy. All 12 cases showed map-like staining pattern for glutamine synthetase. The hepatocytes forming the pseudoacini were positive for CK7 and HepPar1, while the inner lumina were highlighted by CD10 and bile salt export pump immunostains similar to adjacent canaliculi. The presence of prominent pseudoacini was not significantly associated with any clinical or pathologic features. The findings suggest that pseudoacini are likely manifestation of hepatocyte biliary transdifferentiation associated with chronic cholestasis in the lesion. This feature may pose a potential diagnostic pitfall especially on needle biopsies and awareness is needed to avoid misdiagnosing this as HCC.
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Sjöblom N, Boyd S, Kautiainen H, Arola J, Färkkilä M. Novel histological scoring for predicting disease outcome in primary sclerosing cholangitis. Histopathology 2022; 81:192-204. [PMID: 35510514 PMCID: PMC9544993 DOI: 10.1111/his.14677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 04/24/2022] [Accepted: 05/03/2022] [Indexed: 12/01/2022]
Abstract
Background Primary sclerosing cholangitis (PSC) is a progressive cholestatic liver disease that may lead to liver cirrhosis or cholangiocarcinoma. Liver histology and fibrosis stage are predictive markers of disease progression, and histological cirrhosis is defined as a significant endpoint. PSC‐specific histological scoring methods are lacking at present. We aimed to develop a tailored classification system for PSC, the PSC histoscore, based on histological features associated with disease progression. Methods In total, 300 PSC patients diagnosed between 1988 and 2018 were enrolled; their data were collected from the PSC registry (Helsinki University Hospital), and liver specimens were obtained from the Biobank of Helsinki. Five histological features included in the adapted Nakanuma scoring system and three additional parameters typical for PSC histology were evaluated and compared with the clinical and laboratory data. A compound endpoint consisting of liver transplantation, development of cholangiocarcinoma, or death was used as outcome measurement. Results Stage (fibrosis, bile duct loss, ductular reaction, and chronic cholestasis) and grade (portal inflammation, portal edema, hepatitis activity, and cholangitis activity) parameters were found to be independent predictive risk factors for the compound endpoint (P < 0.001). High disease grade (2–6) and stage (2–4) better correlated with clinical endpoints when evaluated with the PSC histoscore system compared to the adapted Nakanuma classification. The risk for disease progression in sequential endoscopic retrograde cholangiography (ERC) examinations was increased with elevated total PSC histoscores. Conclusion The PSC histoscore is a novel histological classification system for PSC. Our findings support the applicability of liver histology as a marker for disease progression.
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Affiliation(s)
- Nelli Sjöblom
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290, Helsinki, Finland
| | - Sonja Boyd
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290, Helsinki, Finland
| | - Hannu Kautiainen
- Folkhälsan Research Center, Helsinki, Finland.,Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Johanna Arola
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290, Helsinki, Finland
| | - Martti Färkkilä
- Department of Gastroenterology, University of Helsinki and Helsinki University Hospital, 00290, Helsinki, Finland
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