<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/NCT03798795</url>
  </required_header>
  <id_info>
    <org_study_id>Sarcoma_Grading_Radiomics</org_study_id>
    <nct_id>NCT03798795</nct_id>
  </id_info>
  <brief_title>Radiomics for Tumor Grading of Soft Tissue Sarcomas.</brief_title>
  <official_title>Development of an MRI-based Radiomic Model for Non-invasive Tumor Grading of Soft Tissue Sarcomas.</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Technical University of Munich</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Washington</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Technical University of Munich</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>
      Radiomics is defined as a quantitative high-throughput analysis of imaging data combined with&#xD;
      model development aiming to predict biological correlates or clinical endpoints. The&#xD;
      investigators of this study hypothesize that radiomic features may correlate with&#xD;
      pathology-defined tumor grading in soft tissue sarcoma patients. The aim of this study is to&#xD;
      develop a predictive radiomics model for tumor grading determination.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Soft tissue sarcomas (STS) constitute an overall rare malignant entity comprising 1% of all&#xD;
      cancers with a yearly incidence rate of 3.8 per 100.000 inhabitants. Therapy decisions are&#xD;
      made using clinical and pathological determinants defined by the American Joint Committee on&#xD;
      Cancer (AJCC). It involves the TNM staging system that classifies STS by their tumor size&#xD;
      (measured as maximal diameter), pathological tumor grading defined by the French Fédération&#xD;
      Nationale des Centres de Lutte Contre le Cancer (FNCLCC) and the occurrence of nodal or&#xD;
      distant metastases.&#xD;
&#xD;
      For the guidance of therapy, the most important factor constitutes tumor grading. In&#xD;
      &quot;low-grade&quot; sarcomas (G1), surgical resection is often sufficient for durable tumor control.&#xD;
      In &quot;high risk&quot; STS, however, resection of the tumor is combined with radiotherapy improving&#xD;
      locoregional control and eventually survival.&#xD;
&#xD;
      Currently, invasive biopsies followed by pathological work-up are necessary to determine&#xD;
      tumor grading. However, bioptic specimens are always restricted to small tumor subvolume.&#xD;
&#xD;
      Medical imaging-based analyses constitutes an alternative tool to characterize tissue. Recent&#xD;
      developments in quantitative image analysis and data science have led to the evolvement of&#xD;
      &quot;Radiomics&quot;. It is defined as an algorithm-based large-scale quantitative analysis of imaging&#xD;
      features. It should be considered as a two-step process with (1) extraction of relevant&#xD;
      imaging features, and (2) incorporating these features into a mathematical model to&#xD;
      ultimately predict patient or tumor-specific outcomes. In previous scientific studies,&#xD;
      radiomic models have been associated with survival, tumor progression, and molecular changes&#xD;
      including genetic mutations or expression profiles as shown in multiple malignant entities.&#xD;
      In addition, radiomic models were able to predict tumor grading e.g. for gliomas,&#xD;
      meningiomas, hepatocellular carcinoma or pancreatic neuroendocrine tumors. In contrast to&#xD;
      pathology, quantitative image analysis (radiomics) has the principal advantage of analyzing&#xD;
      the whole tumor.&#xD;
&#xD;
      In this study, the investigators are aiming to correlate radiomic features with tumor grading&#xD;
      of STS. The ultimate goal is to develop a prediction model to non-invasively classify tumor&#xD;
      grading. In a first step, the focus will be laid on differentiating &quot;low-grade&quot; and&#xD;
      &quot;high-grade&quot; STS. In a second step, &quot;high-grade&quot; STS will be divided into G2 and G3 tumors.&#xD;
&#xD;
      To this end, the investigators will retrospectively analyze a patient cohort of 138 patients&#xD;
      (139 tumors) with known tumor grading and available pre-therapeutic MRI-scans. As secondary&#xD;
      endpoint overall survival will be determined for all patients. An independent patient cohort&#xD;
      from the University of Washington (139 patients) will be used for external validation of the&#xD;
      developed models.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 1, 2017</start_date>
  <completion_date type="Actual">March 1, 2019</completion_date>
  <primary_completion_date type="Actual">March 31, 2018</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Pathological tumor grading</measure>
    <time_frame>Baseline</time_frame>
    <description>Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Overall Survival</measure>
    <time_frame>From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.</time_frame>
    <description>Overall Survival</description>
  </secondary_outcome>
  <enrollment type="Actual">285</enrollment>
  <condition>Sarcoma, Soft Tissue</condition>
  <eligibility>
    <study_pop>
      <textblock>
        Patients with histologically proven soft tissue sarcomas with known FNCLCC tumor grading&#xD;
        determined by biopsy prior to therapy.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Histologically proven soft tissue sarcoma&#xD;
&#xD;
          -  Available pre-therapeutic MRI with a contrast-enhanced T1 weight fat saturated&#xD;
             sequence +/- fat saturated T2 sequences (e.g. STIR)&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Indeterminate tumor grading&#xD;
&#xD;
          -  Osteosarcoma&#xD;
&#xD;
          -  Ewing Sarcoma&#xD;
&#xD;
          -  Endoprothesis-dependent MRI artifacts&#xD;
&#xD;
          -  Previous radiotherapy or chemotherapy&#xD;
&#xD;
          -  Lack of a contrast-enhanced T1 weight fat saturated MRI sequence&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Stephanie E Combs, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Technical University of Munich</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Klinik für RadioOnkologie Strahlentherapie</name>
      <address>
        <city>Munich</city>
        <state>Bavaria</state>
        <zip>81675</zip>
        <country>Germany</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Germany</country>
  </location_countries>
  <reference>
    <citation>Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, Zhang L, Wan D, Huang Q, Lu Y, Kuang Y, Niu T. A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res. 2019 Jan 15;25(2):584-594. doi: 10.1158/1078-0432.CCR-18-1305. Epub 2018 Nov 5.</citation>
    <PMID>30397175</PMID>
  </reference>
  <reference>
    <citation>Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. Erratum In: Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara].</citation>
    <PMID>24892406</PMID>
  </reference>
  <verification_date>April 2019</verification_date>
  <study_first_submitted>January 7, 2019</study_first_submitted>
  <study_first_submitted_qc>January 8, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 10, 2019</study_first_posted>
  <last_update_submitted>April 8, 2019</last_update_submitted>
  <last_update_submitted_qc>April 8, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">April 10, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Sarcoma</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT03798795
org study id: Sarcoma_Grading_Radiomics
nct id: NCT03798795
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Radiomics is defined as a quantitative high-throughput analysis of imaging data combined with
      model development aiming to predict biological correlates or clinical endpoints. The
      investigators of this study hypothesize that radiomic features may correlate with
      pathology-defined tumor grading in soft tissue sarcoma patients. The aim of this study is to
      develop a predictive radiomics model for tumor grading determination.
Soft tissue sarcomas (STS) constitute an overall rare malignant entity comprising 1% of all
      cancers with a yearly incidence rate of 3.8 per 100.000 inhabitants. Therapy decisions are
      made using clinical and pathological determinants defined by the American Joint Committee on
      Cancer (AJCC). It involves the TNM staging system that classifies STS by their tumor size
      (measured as maximal diameter), pathological tumor grading defined by the French Fédération
      Nationale des Centres de Lutte Contre le Cancer (FNCLCC) and the occurrence of nodal or
      distant metastases.

      For the guidance of therapy, the most important factor constitutes tumor grading. In
      "low-grade" sarcomas (G1), surgical resection is often sufficient for durable tumor control.
      In "high risk" STS, however, resection of the tumor is combined with radiotherapy improving
      locoregional control and eventually survival.

      Currently, invasive biopsies followed by pathological work-up are necessary to determine
      tumor grading. However, bioptic specimens are always restricted to small tumor subvolume.

      Medical imaging-based analyses constitutes an alternative tool to characterize tissue. Recent
      developments in quantitative image analysis and data science have led to the evolvement of
      "Radiomics". It is defined as an algorithm-based large-scale quantitative analysis of imaging
      features. It should be considered as a two-step process with (1) extraction of relevant
      imaging features, and (2) incorporating these features into a mathematical model to
      ultimately predict patient or tumor-specific outcomes. In previous scientific studies,
      radiomic models have been associated with survival, tumor progression, and molecular changes
      including genetic mutations or expression profiles as shown in multiple malignant entities.
      In addition, radiomic models were able to predict tumor grading e.g. for gliomas,
      meningiomas, hepatocellular carcinoma or pancreatic neuroendocrine tumors. In contrast to
      pathology, quantitative image analysis (radiomics) has the principal advantage of analyzing
      the whole tumor.

      In this study, the investigators are aiming to correlate radiomic features with tumor grading
      of STS. The ultimate goal is to develop a prediction model to non-invasively classify tumor
      grading. In a first step, the focus will be laid on differentiating "low-grade" and
      "high-grade" STS. In a second step, "high-grade" STS will be divided into G2 and G3 tumors.

      To this end, the investigators will retrospectively analyze a patient cohort of 138 patients
      (139 tumors) with known tumor grading and available pre-therapeutic MRI-scans. As secondary
      endpoint overall survival will be determined for all patients. An independent patient cohort
      from the University of Washington (139 patients) will be used for external validation of the
      developed models.
observational model: Cohort
time perspective: Retrospective
measure: Pathological tumor grading
time frame: Baseline
description: Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)
measure: Overall Survival
time frame: From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.
description: Overall Survival
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Stephanie E Combs, MD
role: Principal Investigator
affiliation: Technical University of Munich
facility: 
country: Germany
citation: Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, Zhang L, Wan D, Huang Q, Lu Y, Kuang Y, Niu T. A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res. 2019 Jan 15;25(2):584-594. doi: 10.1158/1078-0432.CCR-18-1305. Epub 2018 Nov 5.
PMID: 30397175
citation: Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. Erratum In: Nat Commun. 2014;5:4644. Cavalho, Sara [corrected to Carvalho, Sara].
PMID: 24892406
responsible party type: Sponsor
mesh term: Sarcoma
sharing ipd: Undecided