<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/NCT04451603</url>
  </required_header>
  <id_info>
    <org_study_id>LMS Liver</org_study_id>
    <nct_id>NCT04451603</nct_id>
  </id_info>
  <brief_title>LiverMultiScan Analysis of MRI Scans in HCC</brief_title>
  <official_title>The Utility of LiverMultiScan Analysis of MRI Scans in Patients With Hepatocellular Carcinoma for Improved Prognostication and as an Aid to the Selection of Therapy</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National Cancer Centre, Singapore</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Perspectum</agency>
      <agency_class>Industry</agency_class>
    </collaborator>
  </sponsors>
  <source>National Cancer Centre, Singapore</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>
      This project is a pilot study to interrogate the potential of LMS as a predictive tool for&#xD;
      the selection of therapy for HCC patients. The reliability of LMS to predict patients'&#xD;
      response following HCC therapy will leverage on an algorithm that is built from the pool of&#xD;
      MRI scans from HCC patients pre- and post-treatment. In the study, MRI scans of 30 HCC and&#xD;
      metastatic colorectal cancer (CM) patients (ratio of 4:1) will be analysed. CM cancer&#xD;
      patients include patients whose cancers metastasized from colorectal cancer or primary liver&#xD;
      cancer. These patients will either receive one of the treatment, surgical resection, Y90 or&#xD;
      systemic therapy. A total of 4 MRI scans will be taken for each patient; the first MRI scan&#xD;
      will be taken within a month before treatment initiation and the remaining MRI scans will be&#xD;
      taken at the 1st, 3rd and 9th month post-initiation of treatment.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Liver cancer is currently the second most common cause of cancer-related death worldwide, and&#xD;
      hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers. There has been a&#xD;
      marked increase in HCC-related annual death rates over the past two decades, with the&#xD;
      majority of all cases of HCC worldwide found in the Asia-Pacific region. As such, HCC&#xD;
      represents a major public health problem in the Asia-Pacific region and globally. There are a&#xD;
      significant number of variables known to influence the prognosis of HCC, with the stage of&#xD;
      underlying liver disease being a dominant factor. Elucidating these variables remains a&#xD;
      challenge and the poor understanding of these variables has translated to poor&#xD;
      prognostication of treatment outcomes. To date, no ideal predictive modality has been&#xD;
      developed. One of the treatment therapies in HCC is surgical resection. Despite advances in&#xD;
      the surgical and perioperative fields, potential posthepatectomy liver failure (PHLF)&#xD;
      persists as a life-threatening complication, and is reported in up to 15% of patients (Aliza&#xD;
      et al., 2017). This accentuates the need to develop accurate methods to quantitatively&#xD;
      characterize future liver remnants (FLR) prior to surgery, as postoperative outcomes mainly&#xD;
      depend on the size and quality of FLR (Cieslak et al., 2014).&#xD;
&#xD;
      This study seeks to address this unmet need as we will employ the LMS platform to assess and&#xD;
      monitor changes in the liver health of HCC patients following treatment. In addition, this&#xD;
      platform will be leveraged upon to monitor patients' response to the different treatment&#xD;
      modalities such as surgical resection, Y90 and systemic therapy. These aims will be achieved&#xD;
      through the quantitative characterization of liver tissues, which forms the basis of LMS&#xD;
      technology. LMS is an MRI-based non-invasive tool that has attained CE marking and FDA&#xD;
      clearance to aid the diagnosis of patients with liver disease. This technology is highly&#xD;
      sensitive to subtle differences in liver tissue composition and samples the entire liver&#xD;
      quickly, rendering it an ideal platform for liver tissue characterisation. LMS uses MRI&#xD;
      mapping techniques to characterize liver tissue at the cellular level, delivering the&#xD;
      quantification of liver fat and correlates of fibroinflammation and iron load using proton&#xD;
      density fat fraction (PDFF), T1 and T2* maps, respectively. The modelling algorithms of this&#xD;
      device correct the T1 map (cT1) for the confounding effect of iron overload. In addition,&#xD;
      this platform has further refined the measurements for PDFF to be more accurate in liver&#xD;
      tissue characterisation. Previously, the use of LMS to predict clinical outcomes through&#xD;
      tissue characterisation has been validated in a few clinical studies on patients with liver&#xD;
      disease. In a prospective general hepatology clinical cohort following 112 patients for a&#xD;
      median of 27 months, patients with higher cT1 values tended to experience clinical events,&#xD;
      whereas patients with normal or low cT1 values had no clinical events (Pavlides et al.,&#xD;
      2016). Additionally, from a prospective study on 71 patients with suspected non-alcoholic&#xD;
      fatty liver disease (NAFLD), cT1 values have been shown to correlated with cirrhosis,&#xD;
      ballooning and significant NAFLD (Pavlides et al., 2016). Finally, an independent study has&#xD;
      also demonstrated the utility of T1 mapping in predicting clinical outcomes and for&#xD;
      distinguishing decompensated cirrhotics from compensated cirrhotics (Bradley, 2018).&#xD;
&#xD;
      In-depth characterisation of liver tissues will be done following Couinaud segmentation of&#xD;
      the liver. Specific to the surgical resection cohort, estimated FLR volume and cT1 will be&#xD;
      combined to refine the LMS platform in the assessment of liver health. The significance of&#xD;
      this in the long term resides in more accurate assessment of surgical risks, which is&#xD;
      paramount to improve the care of HCC patients considered for surgical resection. By adopting&#xD;
      the LMS platform to monitor patients' response to treatment longitudinally through the&#xD;
      characterisation of their liver tissues, our study also seeks to discern features present in&#xD;
      the LMS platform that are predictive of patients' outcomes. Additionally, this study will&#xD;
      compare anonymized blood test results and anonymized histological reports (histological&#xD;
      reports only for the surgical resection cohort) against features observed in the LMS platform&#xD;
      to better discern features indicative of patients' outcomes.&#xD;
&#xD;
      This study will monitor patients closely before treatment and after their treatment with&#xD;
      follow-up visits, and the MRI scans of these patients across these visits will be analysed&#xD;
      using the LMS. A total of 4 MRI scans will be taken for each patient with 1st MRI scan taken&#xD;
      within 6 weeks before treatment and the remaining MRI scans taken at the 1st, 3rd and 9th&#xD;
      month post-initiation of treatment.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">October 19, 2018</start_date>
  <completion_date type="Anticipated">December 31, 2022</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2021</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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Correlation of cT1 with the development of liver-related morbidity such as decompression</measure>
    <time_frame>From 6 weeks before start of treatment to the 9th month after start of treatment</time_frame>
    <description>The objective is to predict the clinical outcomes of patients undergoing treatment</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Use of prediction software or tools to estimate volume and health of the future liver remnant (FLR), monitor changes in liver volume and health post treatment</measure>
    <time_frame>From 6 weeks before start of treatment to the 9th month after start of treatment</time_frame>
    <description>The objective is to refine the prediction capabilities of these software or tools. The software used in this study is the LiverMultiScan (LMS) by Perspectum which will do post-analysis of patients' MRI scans.</description>
  </primary_outcome>
  <number_of_groups>3</number_of_groups>
  <enrollment type="Anticipated">30</enrollment>
  <condition>Hepatocellular Carcinoma</condition>
  <arm_group>
    <arm_group_label>Surgical resection</arm_group_label>
  </arm_group>
  <arm_group>
    <arm_group_label>Y-90 therapy</arm_group_label>
  </arm_group>
  <arm_group>
    <arm_group_label>Systemic Therapy</arm_group_label>
  </arm_group>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>Surgery</intervention_name>
    <description>Surgical resection of the liver</description>
    <arm_group_label>Surgical resection</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Radiation</intervention_type>
    <intervention_name>Radiation therapy</intervention_name>
    <description>Y-90 radioembolization therapy is the main treatment</description>
    <arm_group_label>Y-90 therapy</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Drug</intervention_type>
    <intervention_name>Drug treatment</intervention_name>
    <description>Examples include tyrosine kinase inhibitors and immune checkpoint inhibitors</description>
    <arm_group_label>Systemic Therapy</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Participants of the study are patients in National Cancer Centre who have HCC or CM. They&#xD;
        are recruited because they will receive either one of the three treatments (surgical&#xD;
        resection, Y90 treatment or systemic therapy). The patient will be approached to join the&#xD;
        study during their consultation visit in NCC.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion criteria:&#xD;
&#xD;
        Common Inclusion Criteria (Surgical Resection, Y90 Treatment and Systemic Therapy):&#xD;
&#xD;
          -  Male and female patients, 21 to 90 years of age at the time of signing of the informed&#xD;
             consent form.&#xD;
&#xD;
          -  For HCC patients: Diagnosis of HCC confirmed by either 1 of the following 3 criteria:&#xD;
&#xD;
               -  AASLD imaging criteria&#xD;
&#xD;
               -  Histology/cytology of biopsy or surgically resected tumor tissue samples&#xD;
&#xD;
               -  Space occupying lesion in the liver and serum alpha-fetoprotein (AFP) of more&#xD;
                  than 400 in patients with chronic viral hepatitis or cirrhosis of any cause&#xD;
&#xD;
          -  For CM patients: Diagnosis of colorectal cancer with metastasis to the liver confirmed&#xD;
             by histology/cytology.&#xD;
&#xD;
          -  Has ECOG performance status 0-1 before treatment intervention.&#xD;
&#xD;
        Inclusion Criteria Specific to Treatment Arm:&#xD;
&#xD;
          -  Surgical Resection&#xD;
&#xD;
               -  Has received no anti-cancer specific treatment for HCC i.e. previous liver&#xD;
                  resection, loco-regional therapy (e.g. RFA, TACE, SIRT), radiotherapy,&#xD;
                  immunotherapy, chemotherapy or neo-adjuvant chemotherapy within 4 weeks prior to&#xD;
                  the date of the planned surgery.&#xD;
&#xD;
               -  Has adequate bone-marrow reserve, renal function and hepatic function as assessed&#xD;
                  by standard laboratory criteria:&#xD;
&#xD;
                    -  Absolute neutrophil count ≥ 1.0 x 10^9/L&#xD;
&#xD;
                    -  Platelet count ≥ 50 x 10^9/L&#xD;
&#xD;
                    -  Haemoglobin ≥ 9.0 g/dL&#xD;
&#xD;
                    -  INR ≤ 2.0&#xD;
&#xD;
                    -  Serum creatinine ≤ 1.5 times the Upper Limit of Normal (ULN)&#xD;
&#xD;
                    -  Albumin ≥ 2.5 g/dL&#xD;
&#xD;
                    -  Total bilirubin ≤ 1.5 times the ULN&#xD;
&#xD;
                    -  Alanine transaminase (ALT) ≤ 2.5 times the ULN&#xD;
&#xD;
                    -  Aspartate Transaminase (AST) ≤ 2.5 times the ULN&#xD;
&#xD;
               -  Has Child-Pugh score ≤ 7.&#xD;
&#xD;
          -  Y90 treatment&#xD;
&#xD;
               -  Y-90 therapy as the main treatment.&#xD;
&#xD;
               -  Has Child-Pugh score ≤ 7.&#xD;
&#xD;
          -  Systemic therapy&#xD;
&#xD;
               -  Systemic therapy as the only treatment.&#xD;
&#xD;
               -  Has Child-Pugh score ≤ 8&#xD;
&#xD;
        Exclusion criteria:&#xD;
&#xD;
        Common Exclusion Criteria (Surgical Resection, Y90 Treatment and Systemic Therapy):&#xD;
&#xD;
          -  Has low creatinine clearance (estimated glomerular filtration rates &lt; 60)&#xD;
&#xD;
          -  Is unable to return for follow-up visits to monitor for disease response/progression&#xD;
&#xD;
          -  Is unable to have a MRI scan, including but not limited to claustrophobia, implanted&#xD;
             metallic devices and metal foreign bodies&#xD;
&#xD;
          -  Non-provision of informed consent&#xD;
&#xD;
          -  For female patients: pregnant or lactating&#xD;
&#xD;
        Exclusion Criteria Specific to Treatment Arm:&#xD;
&#xD;
          -  Surgical resection&#xD;
&#xD;
               -  Has encephalopathy.&#xD;
&#xD;
               -  Has received a major organ allograft.&#xD;
&#xD;
               -  Has an uncontrolled bleeding disorder.&#xD;
&#xD;
               -  Has uncontrolled congestive heart failure or hypertension, unstable heart disease&#xD;
                  (coronary artery disease or myocardial infarction) or uncontrolled arrhythmia at&#xD;
                  the time of enrolment.&#xD;
&#xD;
               -  Has psychiatric or addictive disorders that may compromise his/her ability to&#xD;
                  give informed consent, or to comply with the study procedures.&#xD;
&#xD;
               -  Has other concurrent severe medical problems, unrelated to the malignancy, that&#xD;
                  would significantly limit full compliance with the study or expose the patient to&#xD;
                  unacceptable risk.&#xD;
&#xD;
          -  Y90 treatment&#xD;
&#xD;
             o Nil&#xD;
&#xD;
          -  Systemic therapy&#xD;
&#xD;
               -  Receiving concurrent therapy alongside systemic therapy.&#xD;
&#xD;
               -  Has no anti-cancer specific treatment within 4 weeks prior to the initiation of&#xD;
                  systemic therapy.&#xD;
&#xD;
               -  Has encephalopathy.&#xD;
&#xD;
               -  Has history of allergic disease or reactions likely to be exacerbated by any&#xD;
                  component of treatment.&#xD;
&#xD;
               -  Has had a solid organ or hematologic transplant. Has clinically apparent ascites&#xD;
                  on physical examination. Note: Ascites detectable on imaging studies only are&#xD;
                  allowed.&#xD;
&#xD;
               -  Has known active central nervous system (CNS) metastases and/or carcinomatous&#xD;
                  meningitis.&#xD;
&#xD;
               -  Has active autoimmune disease that has required systemic treatment in the past 2&#xD;
                  years (i.e. with use of disease modifying agents, corticosteroids or&#xD;
                  immunosuppressive drugs). Note: Replacement therapy (e.g. thyroxine, insulin, or&#xD;
                  physiologic corticosteroid replacement therapy for adrenal or pituitary&#xD;
                  insufficiency, etc.) is not considered a form of systemic treatment.&#xD;
&#xD;
               -  Has current pneumonitis or known history of, or any evidence of active,&#xD;
                  non-infectious pneumonitis that requires steroids.&#xD;
&#xD;
               -  Has uncontrolled congestive heart failure or hypertension, unstable heart disease&#xD;
                  (coronary artery disease or myocardial infarction) or any uncontrolled arrhythmia&#xD;
                  at the time of enrolment into study.&#xD;
&#xD;
               -  Has known psychiatric or addictive disorders that may compromise the patient's&#xD;
                  ability to give informed consent, or to comply with trial procedures.&#xD;
&#xD;
               -  Is pregnant or breastfeeding, or expecting to conceive or father children within&#xD;
                  the projected duration of the trial.&#xD;
&#xD;
               -  Has had a minor surgery ≤7 days prior to the first dose of systemic therapy.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>21 Years</minimum_age>
    <maximum_age>90 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Pierce Chow</last_name>
    <role>Principal Investigator</role>
    <affiliation>National Cancer Centre, Singapore</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Pierce Chow, MBBS, PhD</last_name>
    <phone>+65 6436 8000</phone>
    <email>pierce.chow.k.h@singhealth.com.sg</email>
  </overall_contact>
  <location>
    <facility>
      <name>National Cancer Center Singapore</name>
      <address>
        <city>Singapore</city>
        <zip>169690</zip>
        <country>Singapore</country>
      </address>
    </facility>
    <status>Recruiting</status>
  </location>
  <location_countries>
    <country>Singapore</country>
  </location_countries>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>April 2, 2020</study_first_submitted>
  <study_first_submitted_qc>June 25, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 30, 2020</study_first_posted>
  <last_update_submitted>June 25, 2020</last_update_submitted>
  <last_update_submitted_qc>June 25, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 30, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Hepatocellular Carcinoma</keyword>
  <keyword>Magnetic Resonance Imaging (MRI)</keyword>
  <keyword>LiverMultiScan (LMS)</keyword>
  <keyword>Prognostication</keyword>
  <keyword>Predictive tool</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Carcinoma</mesh_term>
    <mesh_term>Carcinoma, Hepatocellular</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</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/NCT04451603
org study id: LMS Liver
nct id: NCT04451603
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This project is a pilot study to interrogate the potential of LMS as a predictive tool for
      the selection of therapy for HCC patients. The reliability of LMS to predict patients'
      response following HCC therapy will leverage on an algorithm that is built from the pool of
      MRI scans from HCC patients pre- and post-treatment. In the study, MRI scans of 30 HCC and
      metastatic colorectal cancer (CM) patients (ratio of 4:1) will be analysed. CM cancer
      patients include patients whose cancers metastasized from colorectal cancer or primary liver
      cancer. These patients will either receive one of the treatment, surgical resection, Y90 or
      systemic therapy. A total of 4 MRI scans will be taken for each patient; the first MRI scan
      will be taken within a month before treatment initiation and the remaining MRI scans will be
      taken at the 1st, 3rd and 9th month post-initiation of treatment.
Liver cancer is currently the second most common cause of cancer-related death worldwide, and
      hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers. There has been a
      marked increase in HCC-related annual death rates over the past two decades, with the
      majority of all cases of HCC worldwide found in the Asia-Pacific region. As such, HCC
      represents a major public health problem in the Asia-Pacific region and globally. There are a
      significant number of variables known to influence the prognosis of HCC, with the stage of
      underlying liver disease being a dominant factor. Elucidating these variables remains a
      challenge and the poor understanding of these variables has translated to poor
      prognostication of treatment outcomes. To date, no ideal predictive modality has been
      developed. One of the treatment therapies in HCC is surgical resection. Despite advances in
      the surgical and perioperative fields, potential posthepatectomy liver failure (PHLF)
      persists as a life-threatening complication, and is reported in up to 15% of patients (Aliza
      et al., 2017). This accentuates the need to develop accurate methods to quantitatively
      characterize future liver remnants (FLR) prior to surgery, as postoperative outcomes mainly
      depend on the size and quality of FLR (Cieslak et al., 2014).

      This study seeks to address this unmet need as we will employ the LMS platform to assess and
      monitor changes in the liver health of HCC patients following treatment. In addition, this
      platform will be leveraged upon to monitor patients' response to the different treatment
      modalities such as surgical resection, Y90 and systemic therapy. These aims will be achieved
      through the quantitative characterization of liver tissues, which forms the basis of LMS
      technology. LMS is an MRI-based non-invasive tool that has attained CE marking and FDA
      clearance to aid the diagnosis of patients with liver disease. This technology is highly
      sensitive to subtle differences in liver tissue composition and samples the entire liver
      quickly, rendering it an ideal platform for liver tissue characterisation. LMS uses MRI
      mapping techniques to characterize liver tissue at the cellular level, delivering the
      quantification of liver fat and correlates of fibroinflammation and iron load using proton
      density fat fraction (PDFF), T1 and T2* maps, respectively. The modelling algorithms of this
      device correct the T1 map (cT1) for the confounding effect of iron overload. In addition,
      this platform has further refined the measurements for PDFF to be more accurate in liver
      tissue characterisation. Previously, the use of LMS to predict clinical outcomes through
      tissue characterisation has been validated in a few clinical studies on patients with liver
      disease. In a prospective general hepatology clinical cohort following 112 patients for a
      median of 27 months, patients with higher cT1 values tended to experience clinical events,
      whereas patients with normal or low cT1 values had no clinical events (Pavlides et al.,
      2016). Additionally, from a prospective study on 71 patients with suspected non-alcoholic
      fatty liver disease (NAFLD), cT1 values have been shown to correlated with cirrhosis,
      ballooning and significant NAFLD (Pavlides et al., 2016). Finally, an independent study has
      also demonstrated the utility of T1 mapping in predicting clinical outcomes and for
      distinguishing decompensated cirrhotics from compensated cirrhotics (Bradley, 2018).

      In-depth characterisation of liver tissues will be done following Couinaud segmentation of
      the liver. Specific to the surgical resection cohort, estimated FLR volume and cT1 will be
      combined to refine the LMS platform in the assessment of liver health. The significance of
      this in the long term resides in more accurate assessment of surgical risks, which is
      paramount to improve the care of HCC patients considered for surgical resection. By adopting
      the LMS platform to monitor patients' response to treatment longitudinally through the
      characterisation of their liver tissues, our study also seeks to discern features present in
      the LMS platform that are predictive of patients' outcomes. Additionally, this study will
      compare anonymized blood test results and anonymized histological reports (histological
      reports only for the surgical resection cohort) against features observed in the LMS platform
      to better discern features indicative of patients' outcomes.

      This study will monitor patients closely before treatment and after their treatment with
      follow-up visits, and the MRI scans of these patients across these visits will be analysed
      using the LMS. A total of 4 MRI scans will be taken for each patient with 1st MRI scan taken
      within 6 weeks before treatment and the remaining MRI scans taken at the 1st, 3rd and 9th
      month post-initiation of treatment.
observational model: Cohort
time perspective: Prospective
measure: Correlation of cT1 with the development of liver-related morbidity such as decompression
time frame: From 6 weeks before start of treatment to the 9th month after start of treatment
description: The objective is to predict the clinical outcomes of patients undergoing treatment
measure: Use of prediction software or tools to estimate volume and health of the future liver remnant (FLR), monitor changes in liver volume and health post treatment
time frame: From 6 weeks before start of treatment to the 9th month after start of treatment
description: The objective is to refine the prediction capabilities of these software or tools. The software used in this study is the LiverMultiScan (LMS) by Perspectum which will do post-analysis of patients' MRI scans.
arm group label: Surgical resection
arm group label: Y-90 therapy
arm group label: Systemic Therapy
intervention type: Procedure
intervention name: Surgery
description: Surgical resection of the liver
arm group label: Surgical resection
intervention type: Radiation
intervention name: Radiation therapy
description: Y-90 radioembolization therapy is the main treatment
arm group label: Y-90 therapy
intervention type: Drug
intervention name: Drug treatment
description: Examples include tyrosine kinase inhibitors and immune checkpoint inhibitors
arm group label: Systemic Therapy
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 21 Years
maximum age: 90 Years
healthy volunteers: No
last name: Pierce Chow
role: Principal Investigator
affiliation: National Cancer Centre, Singapore
last name: Pierce Chow, MBBS, PhD
phone: +65 6436 8000
email: pierce.chow.k.h@singhealth.com.sg
facility: 
status: Recruiting
country: Singapore
responsible party type: Sponsor
mesh term: Carcinoma
mesh term: Carcinoma, Hepatocellular
sharing ipd: No