11 research outputs found

    Novel histological scoring for predicting disease outcome in primary sclerosing cholangitis

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    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.Peer reviewe

    Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis

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    Background and AimsPrimary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that obstructs the bile ducts and causes liver cirrhosis and cholangiocarcinoma. Efficient surrogate markers are required to measure disease progression. The cytokeratin 7 (K7) load in a liver specimen is an independent prognostic indicator that can be measured from digitalized slides using artificial intelligence (AI)-based models. MethodsA K7-AI model 2.0 was built to measure the hepatocellular K7 load area of the parenchyma, portal tracts, and biliary epithelium. K7-stained PSC liver biopsy specimens (n = 295) were analyzed. A compound endpoint (liver transplantation, liver-related death, and cholangiocarcinoma) was applied in Kaplan-Meier survival analysis to measure AUC values and positive likelihood ratios for each histological variable detected by the model. ResultsThe K7-AI model 2.0 was a better prognostic tool than plasma alkaline phosphatase, the fibrosis stage evaluated by Nakanuma classification, or K7 score evaluated by a pathologist based on the AUC values of measured variables. A combination of parameters, such as portal tract volume and area of K7-positive hepatocytes analyzed by the model, produced an AUC of 0.81 for predicting the compound endpoint. Portal tract volume measured by the model correlated with the histological fibrosis stage. ConclusionsThe K7 staining of histological liver specimens in PSC provides significant information on disease outcomes through objective and reproducible data, including variables that cannot be measured by a human pathologist. The K7-AI model 2.0 could serve as a prognostic tool for clinical endpoints and as a surrogate marker in drug trials.Peer reviewe

    Chronic cholestasis detection by a novel tool : automated analysis of cytokeratin 7-stained liver specimens

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    Background The objective was to build a novel method for automated image analysis to locate and quantify the number of cytokeratin 7 (K7)-positive hepatocytes reflecting cholestasis by applying deep learning neural networks (AI model) in a cohort of 210 liver specimens. We aimed to study the correlation between the AI model's results and disease progression. The cohort of liver biopsies which served as a model of chronic cholestatic liver disease comprised of patients diagnosed with primary sclerosing cholangitis (PSC). Methods In a cohort of patients with PSC identified from the PSC registry of the University Hospital of Helsinki, their K7-stained liver biopsy specimens were scored by a pathologist (human K7 score) and then digitally analyzed for K7-positive hepatocytes (K7%area). The digital analysis was by a K7-AI model created in an Aiforia Technologies cloud platform. For validation, values were human K7 score, stage of disease (Metavir and Nakunuma fibrosis score), and plasma liver enzymes indicating clinical cholestasis, all subjected to correlation analysis. Results The K7-AI model results (K7%area) correlated with the human K7 score (0.896; p < 2.2e(- 16)). In addition, K7%area correlated with stage of PSC (Metavir 0.446; p < 1.849e(- 10) and Nakanuma 0.424; p < 4.23e(- 10)) and with plasma alkaline phosphatase (P-ALP) levels (0.369, p < 5.749e(- 5)). Conclusions The accuracy of the AI-based analysis was comparable to that of the human K7 score. Automated quantitative image analysis correlated with stage of PSC and with P-ALP. Based on the results of the K7-AI model, we recommend K7 staining in the assessment of cholestasis by means of automated methods that provide fast (9.75 s/specimen) quantitative analysis.Peer reviewe

    Vammaisten henkilöiden henkilökohtaisen budjetoinnin kokeiluhankkeen tuloksia : Esitys Suomen malliksi

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    Vammaisten henkilöiden henkilökohtaisen budjetoinnin kokeiluhankkeen tuloksia -julkaisu on tulos Terveyden ja hyvinvoinnin laitoksen tekemästä työstä vammaisten henkilöiden henkilökohtaisen budjetoinnin kokeiluhankkeessa (HB-hanke) 2020 2021. HB-hankkeen tavoitteiden mukaisesti julkaisu noudattaa soveltuvin osin hallituksen esityksen muotoa. Julkaisu sisältää henkilökohtaisen budjetoinnin mallin, jolla tarkoitetaan ehdotusta henkilökohtaista budjetointia koskevaksi lainsäädännöksi. HB-hankkeen lähtökohtana oli vahvistaa vammaisen henkilön itsemääräämisoikeutta, osallisuutta ja valinnanmahdollisuuksia palveluiden suunnittelu- ja toteuttamisprosessissa. Henkilökohtaista budjetointia arvioitiin suhteessa siihen, mahdollistaisiko se avun ja tuen saamisen nykyistä joustavammin elämän eri tilanteissa ja edistäisikö se yksilöllisiin tarpeisiin vastaamista nykyistä paremmin. Hankkeessa pyrittiin ratkaisemaan, millaista apua ja tukea henkilökohtaisella budjetoinnilla voidaan järjestää, miten budjetin arvo määritellään ja miten budjetin käyttöä seurataan sekä millaista tukea budjetin käyttöön annetaan. Hankkeessa arvioitiin myös henkilökohtaisen budjetoinnin vahvuuksia, heikkouksia, mahdollisuuksia ja haasteita verrattuna muihin palvelujen järjestämistapoihin sekä luotiin yhteisesti hyväksytty henkilökohtaisen budjetoinnin määritelmä. Hankkeen lopuksi tehtiin esitys henkilökohtaisen budjetoinnin Suomen mallista. Julkaisu on THL:n esitys siitä, mitä tulee ottaa huomioon, jos henkilökohtainen budjetointi otettaisiin maassamme käyttöön yhtenä vammaisten henkilöiden sosiaalipalveluiden järjestämistapana

    Programmed death-ligand 1 and tumor-infiltrating lymphocytes (TILs) – low TIL density may predict poorer long-term prognosis in T1 laryngeal cancer

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    We evaluated the prognostic role of programmed death-ligand 1 (PD-L1) and tumor-infiltrating lymphocytes (TILs) in T1 glottic laryngeal squamous cell carcinoma (LSCC). T1 glottic LSCC patients (n = 174) treated at five Finnish university hospitals between 2003 and 2013 were included. Tissue microarray (TMA) blocks were used for PD-L1 immunohistochemistry. TILs were scored from intratumoral and stromal regions in whole tissue sections. Of 174 patients, 92 (53%) had negative, 66 (38%) intermediate, and 16 (9%) high PD-L1 levels. Of 80 patients whose TILs were analyzed, 50 (63%) had low and 30 (38%) high stromal TIL density. Patients with a local recurrence or a new primary tumor of the larynx had lower TIL density than had other patients (p = 0.047). High PD-L1 expression with low stromal TIL density was associated with inferior 5-year disease-specific survival (85% vs. 100%, p = 0.02). In conclusion, in patients treated for T1 glottic LSCC, low stromal TIL density was associated with local recurrences and new primary tumors of the larynx. High PD-L1 expression with low stromal TIL density may be associated with worse survival in T1 glottic LSCC.Peer reviewe

    Role of Histology in Primary Schlerosing Cholangitis : Diagnostic and Prognostic Value Assessed by Novel Tools

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    Primary sclerosing cholangitis (PSC) is a chronic inflammatory disease of the intra- and extrahepatic bile ducts. Over time, the chronic inflammation may lead to fibrosis and obstruction of the biliary tree, which prevents normal flow of bile and causes cholestasis. Obstructive fibrosis will often lead to liver cirrhosis, which can be treated with liver transplantation surgery. Liver transplantation is one of the only feasible treatment options for this group of patients. Aetiology of PSC remains elusive and therapeutic options are difficult to develop. Moreover, chronic inflammation of biliary epithelium predisposes to biliary neoplasia/ dysplasia and cholangiocarcinoma. The incidence of cholangiocarcinoma in this population is high, the prognosis is poor, and treatment options are sparse. Preemptive liver transplantation, before development of cholangiocarcinoma (CCA) or metastatic disease, may improve overall survival significantly. Liver biopsy is no longer considered a mandatory part of routine work-up to assign the diagnosis of PSC according to the international guidelines for disease management and diagnosis (EASL). It is, however, needed to exclude concomitant autoimmune hepatitis and small duct PSC, not visible in imaging. Additionally, inflammation activity in the liver tissue cannot be evaluated by any other modality. Concerns about applying liver biopsy specimens in evaluation of disease stage are related to the patchiness of the disease. We aimed to investigate the value of liver histology in diagnosis and in measuring disease progression in PSC patients (n=300), diagnosed at Helsinki University Hospital between 1988 and 2018. In addition, we examined whether we could develop automated analysis methods to evaluate liver histology in PSC. We created a non-invasive HelPSCreen measurement tool to screen and diagnose PSC based on only laboratory parameters. With this tool, we were able to reliably screen PSC patients from a larger cohort of patients with inflammatory bowel disease (IBD). Nevertheless, histology did not add value in the primary diagnosis of PSC. On the other hand, we created a novel histological scoring system – the PSC histoscore – a PSC-tailored system to evaluate liver samples of PSC patients. By using this scoring system, the risk for major clinical endpoints can be predicted better. In this study, we showed that inflammation activity, in addition to disease stage, evaluated from histological liver samples, had substantial prognostic value in estimating the course of disease. As clinical endpoints, we applied liver-related deaths, cholangiocarcinomas, and liver transplantation. Additionally, we created an artificial intelligence (AI)-based image analysis tool to quantify objectively and repeatably several histological features from cytokeratin 7 (K7)-stained liver biopsy specimens of patients with PSC. Using this technology, we showed that a single immunohistochemical staining method can provide substantial prognostic information compared with the traditional surrogate endpoint markers. We demonstrated that K7-staining, as a single immunohistochemical staining method, can be applied in evaluation of chronic cholestasis, as it correlates well with clinical indices of cholestasis. Additionally, the K7-load in a liver biopsy specimen of a patient with PSC predicts disease outcome measured by either a pathologist or an automated image analysis tool. In conclusion, we promote the use of liver histology, PSC histoscore, and K7- staining as part of the routine follow-up protocols because they provide prognostic information, even at an early stage of disease. Since newly developed treatment options are becoming available and several drug trials are ongoing, it might be feasible to test the performance of liver biopsy specimens as surrogate endpoint markers and to evaluate treatment response, also because inflammation activity, shown to have prognostic value, cannot be evaluated in any other way. The value of histology can be increased by applying automated objective methods in their evaluation when high quantities of objective numeric data are produced. International collaboration is imperative in order to develop better prognostic markers and treatment options for patients with PSC. As a future objective, we intend to validate our results in larger international cohorts.Primaari sklerosoiva kolangiitti (PSC) on krooninen, tulehduksellinen sappitiesairaus. Edetessään se aiheuttaa arpikudosmuodostusta sappiteiden ympärille, joka ahtauttaa sappiteitä ja estää sapen normaalia eritystä ja kulkua (eng. cholestasis). Lisääntyvä arpikudos voi johtaa maksakirroosiin, johon ainut hoitomuoto on maksan siirto. Lääkehoidon kehittäminen on haasteellista, koska taudin etiologisia tekijöitä ei täysin tunneta. Lisäksi krooninen tulehdus altistaa sappiteiden syövälle – kolangiokarsinoomalle. Kolangiokarsinooman insidenssi PSC-populaatiossa on korkea, ennuste on huono ja hoitomahdollisuudet ovat rajalliset. Oikein ajoitettu ennalta ehkäisevä maksan siirto voi parantaa potilaiden ennustetta huomattavasti. Kansainvälisten hoitosuositusten mukaisesti maksabiopsiaa ei pidetä tarpeellisena taudin diagnosoimiseksi. PSC:n aiheuttamat muutokset maksakudoksessa ovat usein läiskäiset, joten taudin edenneisyyden arvioimista maksabiopsiasta on pidetty epävarmana. Tulehdusaktiviteetin arvioiminen kuitenkin onnistuu ainoastaan biopsiasta. Tavoitteemme oli tutkia Helsingin Yliopistollisen keskussairaalan (HUH) alueella vuosien 1988 ja 2018 välillä PSC diagnoosin saaneiden potilaiden (n=300) maksabiopsioita ja niiden merkitystä taudin toteamisessa ja ennusteen arvioinnissa. Tavoitteena oli selvittää, mitä uutta tietoa näytteistä saadaan tulehdusaktiviteettia arvioimalla, ja voidaanko näitä näytteitä hyödyntämällä rakentaa automatisoitu arviointityökalu. Rakensimme kajoamattoman laboratorioparametreihin perustuvan työkalun nimeltään HelPSCreen, jonka avulla pystyimme seulomaan ja diagnosoimaan PSC potilaat kohortista, joka koostui muita maksasairauksia sairastavista potilaista. Histologia ei kuitenkaan tuonut tähän merkittävää lisäarvoa. Lisäksi loimme histologisen, PSC:lle räätälöidyn luokittelumenetelmän maksabiopsioiden tutkimiseen, (PSC-Histoscore). Tällä menetelmällä pystyimme ennustamaan taudin kulkua biopsiassa esiintyvien histologisten muuttujien avulla. Osoitimme, että tulehdusaktiviteetti sekä sidekudoksen määrä ennustavat merkittävällä tavalla päätetapahtumia. Rakensimme myös tekoälyyn pohjautuvan kuva-analyysityökalun, jolla pystyimme objektiivisesti sekä toistettavasti mittaamaan useita histologisia piirteitä PSC-potilaiden sytokeratiini 7-värjätyistä maksabiopsioista. Tämä yksittäinen värjäysmenetelmä tuotti merkittävästi ennusteen kannalta oleellista tietoa verrattuna perinteisiin ennustemittareihin. Johtopäätöksiimme pohjautuen kannustamme hyödyntämään PSC-potilaiden seurannassa maksabiopsioita, tekemään näistä K7-värjäyksiä, sekä käyttämään histologisessa arvioinnissa PSC-Histoscore-menetelmää

    Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia

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    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.Peer reviewe

    Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models

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    The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalco-holic fatty liver disease and to analyze drug efficacy in preclinical models are needed. The present study developed a deep neural network-based model to quantify microvesicular and macrovesicular steatosis in the liver on hematoxylin-eosin-stained whole slide images, using the cloud-based platform, Aiforia Create. The training data included a total of 101 whole slide images from dietary interventions of wild-type mice and from two genetically modified 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-vesicular 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 EchoMRI ex vivo, and the correlation with total liver triglycerides was 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. (Am J Pathol 2023, 193: 1072-1080; https://doi.org/ 10.1016/j.ajpath.2023.04.014)Peer reviewe
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