xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06019208</url>
    </required_header>
    <id_info>
      <org_study_id>928338</org_study_id>
      <nct_id>NCT06019208</nct_id>
    </id_info>
    <brief_title>GenoMed4ALL: Improving SCD Classification and Prognosis by AI</brief_title>
    <acronym>GenoMed4ALL</acronym>
    <official_title>Genomics and Personalized Medicine for All Though Artificial Intelligence in Haematological Diseases</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Hospital Universitari Vall d'Hebron Research Institute</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Hospital Universitari Vall d'Hebron Research Institute</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in&#xD;
        Haematological Diseases' aims to advance on individual SCD patients' disease characterisation&#xD;
        and to improve the monitoring of patients' health status, optimise clinical therapy guidance&#xD;
        and ultimately improved health outcomes by the identification of biomarkers and the&#xD;
        development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic,&#xD;
        clinical data and other &quot;-omics&quot; health through a secure and privacy respectful data sharing&#xD;
        platform based on the novel Federated Learning scheme, to advance research in personalised&#xD;
        medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models&#xD;
        and standardised interoperable sharing of cross-border data, without needing to directly&#xD;
        share any sensitive clinical patients' data. The SCD Use case will gather multi-modal&#xD;
        clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The&#xD;
        Netherlands.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        SCD is a chronic life-threatening multisystem disorder, autosomal recessively inherited,&#xD;
        caused by the presence of abnormal hemoglobin S (HbS) resulting from the sickle mutation in&#xD;
        the HBB gene. In spite of being a single gene mutation disorder, SCD presents extreme&#xD;
        phenotypic variability that is incompletely understood. Several genetic and environmental&#xD;
        factors are supposed to have an impact on disease phenotype, clinical manifestations,&#xD;
        progression of organ damage and quality of life throughout the lifespan.&#xD;
  &#xD;
        Although significant progress has been made over the past few decades in the highly complex&#xD;
        pathophysiology of SCD, the possibility of personalised medicine is still in its infancy.&#xD;
        There is a lack of markers of disease severity, prognosis, and response to treatment. In&#xD;
        particular, the heterogeneity of clinical expression of the disease along with long-term&#xD;
        chronic complications due to the increased lifespan of patients should be addressed by&#xD;
        innovative and personalised treatments. Furthermore, assessing the role of the novel&#xD;
        treatments both in regards of long-term efficacy and safety but also of cost/efficacy ratio&#xD;
        are required. The scarcity and fragmentation of SCD data prevent researchers from reaching&#xD;
        the critical numbers needed for basic and clinical research. Research and data-driven&#xD;
        solutions are therefore essential to improve the care of SCD patients and their quality of&#xD;
        life.&#xD;
  &#xD;
        The availability of numerous treatment options as well as the high disease heterogeneity&#xD;
        highlight the need to address patients' severity profiles and offer the best care for each&#xD;
        affected individual. Developing the GENOMED4ALL AI algorithms for SCD will be of great&#xD;
        importance for the in-depth characterization and prediction of the diverse complications of&#xD;
        SCD. The primary endpoints of interest include:&#xD;
  &#xD;
          -  Improving SCD classification&#xD;
  &#xD;
          -  Develop a probability score to predict various patterns recognized by Artificial&#xD;
             Intelligence (AI) based analyzing brain magnetic resonance imaging (Radiomics)&#xD;
  &#xD;
          -  To develop predictive risk scores for the occurrence of most prevalent and severe&#xD;
             clinical outcomes&#xD;
  &#xD;
          -  To develop predictive risk scores over time for the appearance of most prevalent and&#xD;
             severe clinical outcomes.&#xD;
  &#xD;
        RADeep will be used for standardization of existing clinical and laboratory data. A CRF was&#xD;
        developed, including just over 250 data elements. The GenoMed4All CRF builds on previous work&#xD;
        performed by RADeep and includes the &quot;set of common data elements for rare disease&#xD;
        registration&quot;, which was released in December 2017 as result of a dedicated working group&#xD;
        facilitated by the Joint Research Centre (JRC). This approach will ensure interoperability&#xD;
        with other similar initiatives in Europe and will also enable the collected data to be reused&#xD;
        for future research studies.&#xD;
  &#xD;
        Genome-wide Association Studies (GWAS) extends the concept of association studies to assay&#xD;
        hundreds of thousands of single-nucleotide polymorphisms (SNPs) simultaneously and provide a&#xD;
        cost-effective way to explore genetic variants across the whole genome. But despite&#xD;
        considerable interest in identifying genetic modifiers in SCD, the majority of previous GWAS&#xD;
        searched for genetic linkage and association with HbF levels, an established ameliorating&#xD;
        factor of disease severity. Addintionally, the utilization of data science and artificial&#xD;
        intelligence (AI) has been limited in SCD research. Therefore, the generation of GWAS data&#xD;
        combined with the use of the most recent imputation panel for imputation offers an&#xD;
        opportunity for the development of novel AI techniques and for novel discoveries in SCD.&#xD;
  &#xD;
        Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25%&#xD;
        of children by the age of 6 and 40% by the age of 18 with consequences on cognition,&#xD;
        schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical&#xD;
        case in GENOMED4ALL is the use of radiomics - quantitative method for the evaluation and&#xD;
        interpretation of medical images- and AI firstly to develop an automatic and uniform&#xD;
        identification and characterization of SCI on MRIs, secondly, to correlate imaging data with&#xD;
        other types of OMICS data in order to predict risk of occurrence and recurrence.&#xD;
  &#xD;
        The deformability of red blood cells (RBC) from individuals with SCD is markedly abnormal,&#xD;
        regardless of genotype. Several studies reported some associations between the degree of&#xD;
        impairment of RBC deformability measured at steady state in SCD patients and the presence of&#xD;
        chronic complications, such as priapism, leg ulcers, glomerulopathy, etc. The recently&#xD;
        developed technique of oxygen gradient ektacytometry allows for a more comprehensive&#xD;
        functional characterization and rheological behavior of SCD RBCs over a range of oxygen&#xD;
        tensions to test whether the rheological changes could reflect clinical&#xD;
        severity/complications.Data on rheological characteristics of RBC on all patients in steady&#xD;
        state is going to be obtained through Laser Optical Rotational Red Cell Analyzer (Lorrca)&#xD;
        ektacytometer (RR Mechatronics).&#xD;
  &#xD;
        By combining a large amount standardized multimodal (clinical, multi-omics, and imaging)&#xD;
        datasets, the investigators hypothesize that AI will allow to understand better SCD biology&#xD;
        and classification, enhance prognostic/predictive capacity of currently available tools and&#xD;
        apply treatments in a more targeted way, thus facilitating the implementation of personalized&#xD;
        medicine program across EU.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">January 1, 2021</start_date>
    <completion_date type="Anticipated">December 31, 2024</completion_date>
    <primary_completion_date type="Anticipated">September 30, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Cross-Sectional</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Improving SCD classification</measure>
      <time_frame>through study completion, an average of 2 years</time_frame>
      <description>To improve classification of SCD by integrating clinical and hematological information with genomic features. To address this issue, different methods of statistical learning (Dirichlet processes (DP), Bayesian networks (BN)) and machine learning (deep learning physics informed neural network, constrained regression and deep models) will be compared in order to define specific genotype-phenotype correlations and to develop a new disease classification.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Improve diagnosis of cerebrovascular complications.</measure>
      <time_frame>through study completion, an average of 2 years</time_frame>
      <description>Develop an artificial intelligence algorithm for early diagnosis of silent infarcts by analyzing brain magnetic resonance imaging (Radiomics).</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">1000</enrollment>
    <condition>Sickle Cell Disorders</condition>
    <arm_group>
      <arm_group_label>GENOMED4ALL - SCD patients</arm_group_label>
      <description>Non transplanted SCD patients aged over 1 year.</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          Patients affected by SCD&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients older than 1 year, diagnosed with SCD, all genotypes.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients treated with stem cell transplant or gene therapy.&#xD;
  &#xD;
            -  Patients younger than 1 year old.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>1 Year</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Federico Alvarez</last_name>
      <role>Principal Investigator</role>
      <affiliation>Universidad Politécnica de Madrid.</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Gastone Castellani</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Bologna</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Raffaella Colombatti</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Padova</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Eduard van Beers</last_name>
      <role>Principal Investigator</role>
      <affiliation>UMC Utrecht</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Marianne de Montalembert</last_name>
      <role>Principal Investigator</role>
      <affiliation>APHP Necker</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Pablo Bartolucci</last_name>
      <role>Principal Investigator</role>
      <affiliation>APHP Henri Mondor</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Tiziana Sanavia</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Torino</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Petros Kountouris</last_name>
      <role>Principal Investigator</role>
      <affiliation>Cyprus Institute of Neurology and Genetics</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Matteo Della Porta</last_name>
      <role>Principal Investigator</role>
      <affiliation>Istituto Clinico Humanitas</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Maria del Mar Mañú Pereira</last_name>
      <role>Principal Investigator</role>
      <affiliation>Hospital Universitari Vall d'Hebron Research Institute</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Maria del Mar Mañú Pereira, PhD</last_name>
      <phone>93 489 3000</phone>
      <phone_ext>4173</phone_ext>
      <email>mar.manu@vhir.org</email>
    </overall_contact>
    <location>
      <facility>
        <name>APHP Henri Mondor</name>
        <address>
          <city>Créteil</city>
          <zip>94000</zip>
          <country>France</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Pablo Bartolucci</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>APHP Necker</name>
        <address>
          <city>Paris</city>
          <zip>75015</zip>
          <country>France</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Marianne de Montalembert</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Azienda Ospedale Università Padova</name>
        <address>
          <city>Padova</city>
          <zip>35121</zip>
          <country>Italy</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Raffaella Colombatti</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>UMC Utrecht</name>
        <address>
          <city>Utrecht</city>
          <zip>3584</zip>
          <country>Netherlands</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Eduard Van Beers</last_name>
      </contact>
    </location>
    <location>
      <facility>
        <name>Hospital Universitari Vall d'Hebron Research Institute</name>
        <address>
          <city>Barcelona</city>
          <zip>08035</zip>
          <country>Spain</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Maria del Mar Mañú Pereira</last_name>
      </contact>
    </location>
    <location_countries>
      <country>France</country>
      <country>Italy</country>
      <country>Netherlands</country>
      <country>Spain</country>
    </location_countries>
    <reference>
      <citation>Aguilar Martinez P, Angastiniotis M, Eleftheriou A, Gulbis B, Manu Pereira Mdel M, Petrova-Benedict R, Corrons JL. Haemoglobinopathies in Europe: health &amp; migration policy perspectives. Orphanet J Rare Dis. 2014 Jul 1;9:97. doi: 10.1186/1750-1172-9-97.</citation>
      <PMID>24980780</PMID>
    </reference>
    <reference>
      <citation>INGRAM VM. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins. Biochim Biophys Acta. 1959 Dec;36:402-11. doi: 10.1016/0006-3002(59)90183-0. No abstract available.</citation>
      <PMID>13852872</PMID>
    </reference>
    <reference>
      <citation>Bunn HF. Pathogenesis and treatment of sickle cell disease. N Engl J Med. 1997 Sep 11;337(11):762-9. doi: 10.1056/NEJM199709113371107. No abstract available.</citation>
      <PMID>9287233</PMID>
    </reference>
    <reference>
      <citation>Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP. Sickle cell disease. Nat Rev Dis Primers. 2018 Mar 15;4:18010. doi: 10.1038/nrdp.2018.10.</citation>
      <PMID>29542687</PMID>
    </reference>
    <reference>
      <citation>Steinberg MH, Sebastiani P. Genetic modifiers of sickle cell disease. Am J Hematol. 2012 Aug;87(8):795-803. doi: 10.1002/ajh.23232. Epub 2012 May 28.</citation>
      <PMID>22641398</PMID>
    </reference>
    <reference>
      <citation>Alapan Y, Fraiwan A, Kucukal E, Hasan MN, Ung R, Kim M, Odame I, Little JA, Gurkan UA. Emerging point-of-care technologies for sickle cell disease screening and monitoring. Expert Rev Med Devices. 2016 Dec;13(12):1073-1093. doi: 10.1080/17434440.2016.1254038. Epub 2016 Nov 22.</citation>
      <PMID>27785945</PMID>
    </reference>
    <reference>
      <citation>Bao EL, Lareau CA, Brugnara C, Fulcher IR, Barau C, Moutereau S, Habibi A, Badaoui B, Berkenou J, Bartolucci P, Galacteros F, Platt OS, Mahaney M, Sankaran VG. Heritability of fetal hemoglobin, white cell count, and other clinical traits from a sickle cell disease family cohort. Am J Hematol. 2019 May;94(5):522-527. doi: 10.1002/ajh.25421. Epub 2019 Feb 6.</citation>
      <PMID>30680775</PMID>
    </reference>
    <reference>
      <citation>Thein SL, Menzel S, Peng X, Best S, Jiang J, Close J, Silver N, Gerovasilli A, Ping C, Yamaguchi M, Wahlberg K, Ulug P, Spector TD, Garner C, Matsuda F, Farrall M, Lathrop M. Intergenic variants of HBS1L-MYB are responsible for a major quantitative trait locus on chromosome 6q23 influencing fetal hemoglobin levels in adults. Proc Natl Acad Sci U S A. 2007 Jul 3;104(27):11346-51. doi: 10.1073/pnas.0611393104. Epub 2007 Jun 25.</citation>
      <PMID>17592125</PMID>
    </reference>
    <reference>
      <citation>Thein SL. Genetic modifiers of the beta-haemoglobinopathies. Br J Haematol. 2008 May;141(3):357-66. doi: 10.1111/j.1365-2141.2008.07084.x.</citation>
      <PMID>18410570</PMID>
    </reference>
    <reference>
      <citation>Rab MAE, van Oirschot BA, Bos J, Merkx TH, van Wesel ACW, Abdulmalik O, Safo MK, Versluijs BA, Houwing ME, Cnossen MH, Riedl J, Schutgens REG, Pasterkamp G, Bartels M, van Beers EJ, van Wijk R. Rapid and reproducible characterization of sickling during automated deoxygenation in sickle cell disease patients. Am J Hematol. 2019 May;94(5):575-584. doi: 10.1002/ajh.25443. Epub 2019 Mar 8.</citation>
      <PMID>30784099</PMID>
    </reference>
    <reference>
      <citation>Collado A, Boaro MP, van der Veen S, Idrizovic A, Biemond BJ, Beneitez Pastor D, Ortuno A, Cela E, Ruiz-Llobet A, Bartolucci P, de Montalembert M, Castellani G, Biondi R, Manara R, Sanavia T, Fariselli P, Kountouris P, Kleanthous M, Alvarez F, Zazo S, Colombatti R, van Beers EJ, Manu-Pereira MDM. Challenges and Opportunities of Precision Medicine in Sickle Cell Disease: Novel European Approach by GenoMed4All Consortium and ERN-EuroBloodNet. Hemasphere. 2023 Feb 22;7(3):e844. doi: 10.1097/HS9.0000000000000844. eCollection 2023 Mar. No abstract available.</citation>
      <PMID>36844183</PMID>
    </reference>
    <verification_date>June 2023</verification_date>
    <study_first_submitted>June 15, 2023</study_first_submitted>
    <study_first_submitted_qc>August 24, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 31, 2023</study_first_posted>
    <last_update_submitted>August 24, 2023</last_update_submitted>
    <last_update_submitted_qc>August 24, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 31, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Hematological diseases</keyword>
    <keyword>GWAS</keyword>
    <keyword>Personalized medicine</keyword>
    <keyword>Prognosis</keyword>
    <keyword>Disease classification</keyword>
    <keyword>Sickle cell disease</keyword>
    <keyword>Radiomics</keyword>
    <keyword>Metabolomics</keyword>
    <keyword>Clinical unmet needs</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Anemia, Sickle Cell</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06019208
  org study id: 928338
  nct id: NCT06019208
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in
        Haematological Diseases' aims to advance on individual SCD patients' disease characterisation
        and to improve the monitoring of patients' health status, optimise clinical therapy guidance
        and ultimately improved health outcomes by the identification of biomarkers and the
        development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic,
        clinical data and other "-omics" health through a secure and privacy respectful data sharing
        platform based on the novel Federated Learning scheme, to advance research in personalised
        medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models
        and standardised interoperable sharing of cross-border data, without needing to directly
        share any sensitive clinical patients' data. The SCD Use case will gather multi-modal
        clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The
        Netherlands.
  SCD is a chronic life-threatening multisystem disorder, autosomal recessively inherited,
        caused by the presence of abnormal hemoglobin S (HbS) resulting from the sickle mutation in
        the HBB gene. In spite of being a single gene mutation disorder, SCD presents extreme
        phenotypic variability that is incompletely understood. Several genetic and environmental
        factors are supposed to have an impact on disease phenotype, clinical manifestations,
        progression of organ damage and quality of life throughout the lifespan.
  
        Although significant progress has been made over the past few decades in the highly complex
        pathophysiology of SCD, the possibility of personalised medicine is still in its infancy.
        There is a lack of markers of disease severity, prognosis, and response to treatment. In
        particular, the heterogeneity of clinical expression of the disease along with long-term
        chronic complications due to the increased lifespan of patients should be addressed by
        innovative and personalised treatments. Furthermore, assessing the role of the novel
        treatments both in regards of long-term efficacy and safety but also of cost/efficacy ratio
        are required. The scarcity and fragmentation of SCD data prevent researchers from reaching
        the critical numbers needed for basic and clinical research. Research and data-driven
        solutions are therefore essential to improve the care of SCD patients and their quality of
        life.
  
        The availability of numerous treatment options as well as the high disease heterogeneity
        highlight the need to address patients' severity profiles and offer the best care for each
        affected individual. Developing the GENOMED4ALL AI algorithms for SCD will be of great
        importance for the in-depth characterization and prediction of the diverse complications of
        SCD. The primary endpoints of interest include:
  
          -  Improving SCD classification
  
          -  Develop a probability score to predict various patterns recognized by Artificial
             Intelligence (AI) based analyzing brain magnetic resonance imaging (Radiomics)
  
          -  To develop predictive risk scores for the occurrence of most prevalent and severe
             clinical outcomes
  
          -  To develop predictive risk scores over time for the appearance of most prevalent and
             severe clinical outcomes.
  
        RADeep will be used for standardization of existing clinical and laboratory data. A CRF was
        developed, including just over 250 data elements. The GenoMed4All CRF builds on previous work
        performed by RADeep and includes the "set of common data elements for rare disease
        registration", which was released in December 2017 as result of a dedicated working group
        facilitated by the Joint Research Centre (JRC). This approach will ensure interoperability
        with other similar initiatives in Europe and will also enable the collected data to be reused
        for future research studies.
  
        Genome-wide Association Studies (GWAS) extends the concept of association studies to assay
        hundreds of thousands of single-nucleotide polymorphisms (SNPs) simultaneously and provide a
        cost-effective way to explore genetic variants across the whole genome. But despite
        considerable interest in identifying genetic modifiers in SCD, the majority of previous GWAS
        searched for genetic linkage and association with HbF levels, an established ameliorating
        factor of disease severity. Addintionally, the utilization of data science and artificial
        intelligence (AI) has been limited in SCD research. Therefore, the generation of GWAS data
        combined with the use of the most recent imputation panel for imputation offers an
        opportunity for the development of novel AI techniques and for novel discoveries in SCD.
  
        Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25%
        of children by the age of 6 and 40% by the age of 18 with consequences on cognition,
        schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical
        case in GENOMED4ALL is the use of radiomics - quantitative method for the evaluation and
        interpretation of medical images- and AI firstly to develop an automatic and uniform
        identification and characterization of SCI on MRIs, secondly, to correlate imaging data with
        other types of OMICS data in order to predict risk of occurrence and recurrence.
  
        The deformability of red blood cells (RBC) from individuals with SCD is markedly abnormal,
        regardless of genotype. Several studies reported some associations between the degree of
        impairment of RBC deformability measured at steady state in SCD patients and the presence of
        chronic complications, such as priapism, leg ulcers, glomerulopathy, etc. The recently
        developed technique of oxygen gradient ektacytometry allows for a more comprehensive
        functional characterization and rheological behavior of SCD RBCs over a range of oxygen
        tensions to test whether the rheological changes could reflect clinical
        severity/complications.Data on rheological characteristics of RBC on all patients in steady
        state is going to be obtained through Laser Optical Rotational Red Cell Analyzer (Lorrca)
        ektacytometer (RR Mechatronics).
  
        By combining a large amount standardized multimodal (clinical, multi-omics, and imaging)
        datasets, the investigators hypothesize that AI will allow to understand better SCD biology
        and classification, enhance prognostic/predictive capacity of currently available tools and
        apply treatments in a more targeted way, thus facilitating the implementation of personalized
        medicine program across EU.
  observational model: Cohort
  time perspective: Cross-Sectional
  measure: Improving SCD classification
  time frame: through study completion, an average of 2 years
  description: To improve classification of SCD by integrating clinical and hematological information with genomic features. To address this issue, different methods of statistical learning (Dirichlet processes (DP), Bayesian networks (BN)) and machine learning (deep learning physics informed neural network, constrained regression and deep models) will be compared in order to define specific genotype-phenotype correlations and to develop a new disease classification.
  measure: Improve diagnosis of cerebrovascular complications.
  time frame: through study completion, an average of 2 years
  description: Develop an artificial intelligence algorithm for early diagnosis of silent infarcts by analyzing brain magnetic resonance imaging (Radiomics).
  arm group label: GENOMED4ALL - SCD patients
  description: Non transplanted SCD patients aged over 1 year.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 1 Year
  maximum age: N/A
  healthy volunteers: No
  last name: Federico Alvarez
  role: Principal Investigator
  affiliation: Universidad Politécnica de Madrid.
  last name: Gastone Castellani
  role: Principal Investigator
  affiliation: University of Bologna
  last name: Raffaella Colombatti
  role: Principal Investigator
  affiliation: University of Padova
  last name: Eduard van Beers
  role: Principal Investigator
  affiliation: UMC Utrecht
  last name: Marianne de Montalembert
  role: Principal Investigator
  affiliation: APHP Necker
  last name: Pablo Bartolucci
  role: Principal Investigator
  affiliation: APHP Henri Mondor
  last name: Tiziana Sanavia
  role: Principal Investigator
  affiliation: University of Torino
  last name: Petros Kountouris
  role: Principal Investigator
  affiliation: Cyprus Institute of Neurology and Genetics
  last name: Matteo Della Porta
  role: Principal Investigator
  affiliation: Istituto Clinico Humanitas
  last name: Maria del Mar Mañú Pereira
  role: Principal Investigator
  affiliation: Hospital Universitari Vall d'Hebron Research Institute
  last name: Maria del Mar Mañú Pereira, PhD
  phone: 93 489 3000
  phone ext: 4173
  email: mar.manu@vhir.org
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  facility: 
  status: Recruiting
  contact: 
  country: France
  country: Italy
  country: Netherlands
  country: Spain
  citation: Aguilar Martinez P, Angastiniotis M, Eleftheriou A, Gulbis B, Manu Pereira Mdel M, Petrova-Benedict R, Corrons JL. Haemoglobinopathies in Europe: health & migration policy perspectives. Orphanet J Rare Dis. 2014 Jul 1;9:97. doi: 10.1186/1750-1172-9-97.
  PMID: 24980780
  citation: INGRAM VM. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins. Biochim Biophys Acta. 1959 Dec;36:402-11. doi: 10.1016/0006-3002(59)90183-0. No abstract available.
  PMID: 13852872
  citation: Bunn HF. Pathogenesis and treatment of sickle cell disease. N Engl J Med. 1997 Sep 11;337(11):762-9. doi: 10.1056/NEJM199709113371107. No abstract available.
  PMID: 9287233
  citation: Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP. Sickle cell disease. Nat Rev Dis Primers. 2018 Mar 15;4:18010. doi: 10.1038/nrdp.2018.10.
  PMID: 29542687
  citation: Steinberg MH, Sebastiani P. Genetic modifiers of sickle cell disease. Am J Hematol. 2012 Aug;87(8):795-803. doi: 10.1002/ajh.23232. Epub 2012 May 28.
  PMID: 22641398
  citation: Alapan Y, Fraiwan A, Kucukal E, Hasan MN, Ung R, Kim M, Odame I, Little JA, Gurkan UA. Emerging point-of-care technologies for sickle cell disease screening and monitoring. Expert Rev Med Devices. 2016 Dec;13(12):1073-1093. doi: 10.1080/17434440.2016.1254038. Epub 2016 Nov 22.
  PMID: 27785945
  citation: Bao EL, Lareau CA, Brugnara C, Fulcher IR, Barau C, Moutereau S, Habibi A, Badaoui B, Berkenou J, Bartolucci P, Galacteros F, Platt OS, Mahaney M, Sankaran VG. Heritability of fetal hemoglobin, white cell count, and other clinical traits from a sickle cell disease family cohort. Am J Hematol. 2019 May;94(5):522-527. doi: 10.1002/ajh.25421. Epub 2019 Feb 6.
  PMID: 30680775
  citation: Thein SL, Menzel S, Peng X, Best S, Jiang J, Close J, Silver N, Gerovasilli A, Ping C, Yamaguchi M, Wahlberg K, Ulug P, Spector TD, Garner C, Matsuda F, Farrall M, Lathrop M. Intergenic variants of HBS1L-MYB are responsible for a major quantitative trait locus on chromosome 6q23 influencing fetal hemoglobin levels in adults. Proc Natl Acad Sci U S A. 2007 Jul 3;104(27):11346-51. doi: 10.1073/pnas.0611393104. Epub 2007 Jun 25.
  PMID: 17592125
  citation: Thein SL. Genetic modifiers of the beta-haemoglobinopathies. Br J Haematol. 2008 May;141(3):357-66. doi: 10.1111/j.1365-2141.2008.07084.x.
  PMID: 18410570
  citation: Rab MAE, van Oirschot BA, Bos J, Merkx TH, van Wesel ACW, Abdulmalik O, Safo MK, Versluijs BA, Houwing ME, Cnossen MH, Riedl J, Schutgens REG, Pasterkamp G, Bartels M, van Beers EJ, van Wijk R. Rapid and reproducible characterization of sickling during automated deoxygenation in sickle cell disease patients. Am J Hematol. 2019 May;94(5):575-584. doi: 10.1002/ajh.25443. Epub 2019 Mar 8.
  PMID: 30784099
  citation: Collado A, Boaro MP, van der Veen S, Idrizovic A, Biemond BJ, Beneitez Pastor D, Ortuno A, Cela E, Ruiz-Llobet A, Bartolucci P, de Montalembert M, Castellani G, Biondi R, Manara R, Sanavia T, Fariselli P, Kountouris P, Kleanthous M, Alvarez F, Zazo S, Colombatti R, van Beers EJ, Manu-Pereira MDM. Challenges and Opportunities of Precision Medicine in Sickle Cell Disease: Novel European Approach by GenoMed4All Consortium and ERN-EuroBloodNet. Hemasphere. 2023 Feb 22;7(3):e844. doi: 10.1097/HS9.0000000000000844. eCollection 2023 Mar. No abstract available.
  PMID: 36844183
  responsible party type: Sponsor
  mesh term: Anemia, Sickle Cell