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/NCT06029777</url>
    </required_header>
    <id_info>
      <org_study_id>PNRR-MAD-2022-12376633</org_study_id>
      <nct_id>NCT06029777</nct_id>
    </id_info>
    <brief_title>Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients (CORO-CTAIOMICS)</brief_title>
    <acronym>CORO-CTAIOMICS</acronym>
    <official_title>Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>IRCCS San Raffaele</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Ministry of Health, Italy</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>IRCCS San Raffaele</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>
        CAD is a leading cause of mortality in Europe. cCTA is recommended to rule out obstructive&#xD;
        CAD, but, in most patients, it shows non-obstructive CAD. The management of these patients is&#xD;
        unclear due to lack of reproducible quantitative measurement, beyond stenosis severity,&#xD;
        capable to assess the risk of disease progression towards developing MACEs. To improve&#xD;
        identification and phenotypization of patients at high risk of disease progression, we&#xD;
        propose the application of artificial intelligence algorithms to cCTA images to automatically&#xD;
        extract periluminal radiomics features to characterize the atherosclerotic process. By&#xD;
        leveraging machine-learning empowered radiomics we aim to improve patients' risk&#xD;
        stratification in a robust, quantitative and reproducible fashion. By developing a novel&#xD;
        quantitative AI based cCTA measure, we expect to provide a risk score capable to identify&#xD;
        patients who can benefit of a more aggressive medical treatment and management, thus&#xD;
        improving outcome&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Background and rationale&#xD;
  &#xD;
        In the last years, cCTA has become a pivotal diagnostic tool in the setting of suspected CAD.&#xD;
        The latest ESC guidelines recommend cCTA as the first line diagnostic test for patients with&#xD;
        symptoms suspected to originate from CAD, especially in intermediate pre-test probability (15&#xD;
        to 85%). patients. The AHA and Italian guidelines essentially suggest the same approach. The&#xD;
        recommendations are based on the extremely high accuracy of cCTA in ruling-out obstructive&#xD;
        CAD. As result we assist to a tremendous increase in the number of patients undergoing CCTA&#xD;
        in the daily clinical routine. Most of these patients (80%) result to not have obstructive&#xD;
        CAD, but this result does not mean that all this patients are at low risk. The prognostic&#xD;
        stratification of these patients is still an urgent unmet need. Among patients with absence&#xD;
        of obstructive CAD we found patients with different degree of atherosclerotic burden and&#xD;
        patients with different kind of plaques (high or low risk plaques) and different degrees of&#xD;
        coronary wall and pericoronary inflammation. The prognostic stratification of these patients&#xD;
        is still an urgent unmet need. The cCTA images include a lot of information with a great&#xD;
        potential informative content about the atherosclerotic burden and the vulnerability of the&#xD;
        non-obstructive CAD of each single patients, but all these information are not currently&#xD;
        exploited in the clinical routine to change the patients management for a lack of robust and&#xD;
        reproducible tools to extract this data in quantitative way and o integrate them in a&#xD;
        prognostic risk score.&#xD;
  &#xD;
        In the last years, it has been shown that coronary artery plaque characteristics (e.g. lesion&#xD;
        length, volume, stenosis, attenuation, remodeling index, etc) (2) and pericoronary adipose&#xD;
        tissue (3) attenuation carry a significant prognostic value. However, it is known that many&#xD;
        of these biomarkers suffer from low reproducibility, mainly due to technical constrains in&#xD;
        automatic or semiautomatic separation of plaques from surrounding adipose tissue. This may&#xD;
        cause the incorrect inclusion of plaques into the segmentation of pericoronary adipose tissue&#xD;
        and vice-versa, leading to unreliable results in the assessment of plaque burden and plaque&#xD;
        attenuation (4). Furthermore, the qualitative or semiquantitative evaluation of the&#xD;
        aforementioned plaque characteristics and pericoronary fat density might reflect only part of&#xD;
        the information available. In this scenario, radiomics-based assessment may unveil&#xD;
        information hidden to the human eye, leading to better risk stratification of cardiovascular&#xD;
        risk (5, 6).&#xD;
  &#xD;
        Our study adds two major improvements in prognostic risk stratification based on cCTA plaque&#xD;
        and adipose tissue analysis. First, we propose a method to segment both the plaques and the&#xD;
        pericoronary adipose tissue easily and independently by semiautomatic or automatic&#xD;
        identification of the edge between plaque and fat (which is critical). In fact, our tool will&#xD;
        perform a segmentation of all the tissue included in a circular range outside the coronary&#xD;
        lumen with a diameter based on the lumen diameter itself. This technical solution will&#xD;
        greatly increase reproducibility of segmentation. Secondly, we propose the use radiomics to&#xD;
        analyze this circular pericoronary milieu, potentially individuating novel biomarkers&#xD;
        -invisible to human eye- of CAD instability capable of providing important prognostic value.&#xD;
  &#xD;
        In detail, the novelty of our research lies in the fact that 1) pericoronary adipose tissue&#xD;
        and plaques will be treated as a single milieu, thus significantly reducing the effort for&#xD;
        accurate tissue segmentation and reducing reproducibility concerns; 2) radiomics will be&#xD;
        applied to extract meaningful information invisible to human eye capable. Furthermore, this&#xD;
        radiomics approach will be supported by machine learning (ML) models including regularized&#xD;
        regression, genetic algorithms and deep learning, due to the capability of ML to directly&#xD;
        manage and assess the huge amount of data extracted from the radiological images. Thus, the&#xD;
        final outcome of our research will be an algorithm capable of predicting the risk of MACEs of&#xD;
        the single patient by automatically analyzing peri-luminal coronary tissue radiomics data&#xD;
        derived from cCTA performed in the routine clinical practice.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">July 31, 2023</start_date>
    <completion_date type="Anticipated">May 20, 2025</completion_date>
    <primary_completion_date type="Anticipated">May 20, 2024</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>Composite outcome</measure>
      <time_frame>48 months from CCTA</time_frame>
      <description>All-cause mortality, myocardial infarction, due to unstable angina or heart hospitalization failure, late coronary revascularization</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">2190</enrollment>
    <condition>Coronary Artery Disease</condition>
    <arm_group>
      <arm_group_label>Retrospective cohort</arm_group_label>
      <description>The retrospective cohort will include 2190 patients who underwent a clinically indicated cCTA between 2017 and 2019 at the Radiology Unit of San Raffaele Hospital.&#xD;
  No other interventions will be performed. Patients will be solely contacted via a telephone call to assess their clinical status.</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          Patients undergoing clinically indicated cCTA for suspected CAD.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Patients with CT performed for CAD assessment between 2017 and 2019.&#xD;
  &#xD;
            2. Follow-up duration of at least 4 years.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Refusal to participate in the study&#xD;
  &#xD;
            2. Age &lt;18 years old&#xD;
  &#xD;
            3. History of previous coronary revascularization&#xD;
  &#xD;
            4. Presence of other cardiovascular comorbidities (e.g. inflammatory cardiomyopathy,&#xD;
               valvular cardiomyopathy, idiopathic dilated cardiomyopathy, infiltrative&#xD;
               cardiomyopathy)&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Antonio Esposito</last_name>
      <phone>02 2643 6102</phone>
      <email>esposito.antonio@hsr.it</email>
    </overall_contact>
    <location>
      <facility>
        <name>IRCCS San Raffaele</name>
        <address>
          <city>Milano</city>
          <zip>20132</zip>
          <country>Italy</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Antonio Esposito</last_name>
        <phone>02 2643 6102</phone>
        <email>esposito.antonio@hsr.it</email>
      </contact>
    </location>
    <location_countries>
      <country>Italy</country>
    </location_countries>
    <reference>
      <citation>Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24. Erratum In: Stat Med. 2019 Dec 30;38(30):5672.</citation>
      <PMID>30357870</PMID>
    </reference>
    <reference>
      <citation>Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973.</citation>
      <PMID>29305348</PMID>
    </reference>
    <reference>
      <citation>Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.</citation>
      <PMID>33867303</PMID>
    </reference>
    <reference>
      <citation>Tzolos E, McElhinney P, Williams MC, Cadet S, Dweck MR, Berman DS, Slomka PJ, Newby DE, Dey D. Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography. J Cardiovasc Comput Tomogr. 2021 Jan-Feb;15(1):81-84. doi: 10.1016/j.jcct.2020.03.007. Epub 2020 Apr 14.</citation>
      <PMID>32312662</PMID>
    </reference>
    <reference>
      <citation>Kolossvary M, Karady J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, Maurovich-Horvat P. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ Cardiovasc Imaging. 2017 Dec;10(12):e006843. doi: 10.1161/CIRCIMAGING.117.006843.</citation>
      <PMID>29233836</PMID>
    </reference>
    <reference>
      <citation>Lin A, Kolossvary M, Yuvaraj J, Cadet S, McElhinney PA, Jiang C, Nerlekar N, Nicholls SJ, Slomka PJ, Maurovich-Horvat P, Wong DTL, Dey D. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study. JACC Cardiovasc Imaging. 2020 Nov;13(11):2371-2383. doi: 10.1016/j.jcmg.2020.06.033. Epub 2020 Aug 26.</citation>
      <PMID>32861654</PMID>
    </reference>
    <reference>
      <citation>Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006 Feb 23;7:91. doi: 10.1186/1471-2105-7-91.</citation>
      <PMID>16504092</PMID>
    </reference>
    <reference>
      <citation>Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M; IBIS Network; Schlaggar BL, Pruett JR Jr, Piven J. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med. 2017 Jun 7;9(393):eaag2882. doi: 10.1126/scitranslmed.aag2882.</citation>
      <PMID>28592562</PMID>
    </reference>
    <reference>
      <citation>Brown PJ, Zhong J, Frood R, Currie S, Gilbert A, Appelt AL, Sebag-Montefiore D, Scarsbrook A. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT. Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2790-2799. doi: 10.1007/s00259-019-04495-1. Epub 2019 Sep 4.</citation>
      <PMID>31482428</PMID>
    </reference>
    <reference>
      <citation>Bardosi ZR, Dejaco D, Santer M, Kloppenburg M, Mangesius S, Widmann G, Ganswindt U, Rumpold G, Riechelmann H, Freysinger W. Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification. Cancers (Basel). 2022 Jan 18;14(3):477. doi: 10.3390/cancers14030477.</citation>
      <PMID>35158745</PMID>
    </reference>
    <reference>
      <citation>Cho HH, Lee HY, Kim E, Lee G, Kim J, Kwon J, Park H. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol. 2021 Nov 12;4(1):1286. doi: 10.1038/s42003-021-02814-7.</citation>
      <PMID>34773070</PMID>
    </reference>
    <reference>
      <citation>Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006 Nov-Dec;26(6):565-74. doi: 10.1177/0272989X06295361.</citation>
      <PMID>17099194</PMID>
    </reference>
    <reference>
      <citation>Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015 Mar 4;10(3):e0118432. doi: 10.1371/journal.pone.0118432. eCollection 2015.</citation>
      <PMID>25738806</PMID>
    </reference>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 1, 2023</study_first_submitted>
    <study_first_submitted_qc>September 1, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 8, 2023</study_first_posted>
    <last_update_submitted>September 1, 2023</last_update_submitted>
    <last_update_submitted_qc>September 1, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>IRCCS San Raffaele</investigator_affiliation>
      <investigator_full_name>Antonio Esposito</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <keyword>Coronary artery disease</keyword>
    <keyword>Computed Tomography</keyword>
    <keyword>Radiomic</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Risk assessment</keyword>
    <keyword>Major adverse cardiac events</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Coronary Artery Disease</mesh_term>
      <mesh_term>Myocardial Ischemia</mesh_term>
      <mesh_term>Coronary Disease</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/NCT06029777
  org study id: PNRR-MAD-2022-12376633
  nct id: NCT06029777
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  CAD is a leading cause of mortality in Europe. cCTA is recommended to rule out obstructive
        CAD, but, in most patients, it shows non-obstructive CAD. The management of these patients is
        unclear due to lack of reproducible quantitative measurement, beyond stenosis severity,
        capable to assess the risk of disease progression towards developing MACEs. To improve
        identification and phenotypization of patients at high risk of disease progression, we
        propose the application of artificial intelligence algorithms to cCTA images to automatically
        extract periluminal radiomics features to characterize the atherosclerotic process. By
        leveraging machine-learning empowered radiomics we aim to improve patients' risk
        stratification in a robust, quantitative and reproducible fashion. By developing a novel
        quantitative AI based cCTA measure, we expect to provide a risk score capable to identify
        patients who can benefit of a more aggressive medical treatment and management, thus
        improving outcome
  Background and rationale
  
        In the last years, cCTA has become a pivotal diagnostic tool in the setting of suspected CAD.
        The latest ESC guidelines recommend cCTA as the first line diagnostic test for patients with
        symptoms suspected to originate from CAD, especially in intermediate pre-test probability (15
        to 85%). patients. The AHA and Italian guidelines essentially suggest the same approach. The
        recommendations are based on the extremely high accuracy of cCTA in ruling-out obstructive
        CAD. As result we assist to a tremendous increase in the number of patients undergoing CCTA
        in the daily clinical routine. Most of these patients (80%) result to not have obstructive
        CAD, but this result does not mean that all this patients are at low risk. The prognostic
        stratification of these patients is still an urgent unmet need. Among patients with absence
        of obstructive CAD we found patients with different degree of atherosclerotic burden and
        patients with different kind of plaques (high or low risk plaques) and different degrees of
        coronary wall and pericoronary inflammation. The prognostic stratification of these patients
        is still an urgent unmet need. The cCTA images include a lot of information with a great
        potential informative content about the atherosclerotic burden and the vulnerability of the
        non-obstructive CAD of each single patients, but all these information are not currently
        exploited in the clinical routine to change the patients management for a lack of robust and
        reproducible tools to extract this data in quantitative way and o integrate them in a
        prognostic risk score.
  
        In the last years, it has been shown that coronary artery plaque characteristics (e.g. lesion
        length, volume, stenosis, attenuation, remodeling index, etc) (2) and pericoronary adipose
        tissue (3) attenuation carry a significant prognostic value. However, it is known that many
        of these biomarkers suffer from low reproducibility, mainly due to technical constrains in
        automatic or semiautomatic separation of plaques from surrounding adipose tissue. This may
        cause the incorrect inclusion of plaques into the segmentation of pericoronary adipose tissue
        and vice-versa, leading to unreliable results in the assessment of plaque burden and plaque
        attenuation (4). Furthermore, the qualitative or semiquantitative evaluation of the
        aforementioned plaque characteristics and pericoronary fat density might reflect only part of
        the information available. In this scenario, radiomics-based assessment may unveil
        information hidden to the human eye, leading to better risk stratification of cardiovascular
        risk (5, 6).
  
        Our study adds two major improvements in prognostic risk stratification based on cCTA plaque
        and adipose tissue analysis. First, we propose a method to segment both the plaques and the
        pericoronary adipose tissue easily and independently by semiautomatic or automatic
        identification of the edge between plaque and fat (which is critical). In fact, our tool will
        perform a segmentation of all the tissue included in a circular range outside the coronary
        lumen with a diameter based on the lumen diameter itself. This technical solution will
        greatly increase reproducibility of segmentation. Secondly, we propose the use radiomics to
        analyze this circular pericoronary milieu, potentially individuating novel biomarkers
        -invisible to human eye- of CAD instability capable of providing important prognostic value.
  
        In detail, the novelty of our research lies in the fact that 1) pericoronary adipose tissue
        and plaques will be treated as a single milieu, thus significantly reducing the effort for
        accurate tissue segmentation and reducing reproducibility concerns; 2) radiomics will be
        applied to extract meaningful information invisible to human eye capable. Furthermore, this
        radiomics approach will be supported by machine learning (ML) models including regularized
        regression, genetic algorithms and deep learning, due to the capability of ML to directly
        manage and assess the huge amount of data extracted from the radiological images. Thus, the
        final outcome of our research will be an algorithm capable of predicting the risk of MACEs of
        the single patient by automatically analyzing peri-luminal coronary tissue radiomics data
        derived from cCTA performed in the routine clinical practice.
  observational model: Cohort
  time perspective: Retrospective
  measure: Composite outcome
  time frame: 48 months from CCTA
  description: All-cause mortality, myocardial infarction, due to unstable angina or heart hospitalization failure, late coronary revascularization
  arm group label: Retrospective cohort
  description: The retrospective cohort will include 2190 patients who underwent a clinically indicated cCTA between 2017 and 2019 at the Radiology Unit of San Raffaele Hospital.
  No other interventions will be performed. Patients will be solely contacted via a telephone call to assess their clinical status.
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Antonio Esposito
  phone: 02 2643 6102
  email: esposito.antonio@hsr.it
  facility: 
  status: Recruiting
  contact: 
  country: Italy
  citation: Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24. Erratum In: Stat Med. 2019 Dec 30;38(30):5672.
  PMID: 30357870
  citation: Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973.
  PMID: 29305348
  citation: Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.
  PMID: 33867303
  citation: Tzolos E, McElhinney P, Williams MC, Cadet S, Dweck MR, Berman DS, Slomka PJ, Newby DE, Dey D. Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography. J Cardiovasc Comput Tomogr. 2021 Jan-Feb;15(1):81-84. doi: 10.1016/j.jcct.2020.03.007. Epub 2020 Apr 14.
  PMID: 32312662
  citation: Kolossvary M, Karady J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, Maurovich-Horvat P. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ Cardiovasc Imaging. 2017 Dec;10(12):e006843. doi: 10.1161/CIRCIMAGING.117.006843.
  PMID: 29233836
  citation: Lin A, Kolossvary M, Yuvaraj J, Cadet S, McElhinney PA, Jiang C, Nerlekar N, Nicholls SJ, Slomka PJ, Maurovich-Horvat P, Wong DTL, Dey D. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study. JACC Cardiovasc Imaging. 2020 Nov;13(11):2371-2383. doi: 10.1016/j.jcmg.2020.06.033. Epub 2020 Aug 26.
  PMID: 32861654
  citation: Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006 Feb 23;7:91. doi: 10.1186/1471-2105-7-91.
  PMID: 16504092
  citation: Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M; IBIS Network; Schlaggar BL, Pruett JR Jr, Piven J. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med. 2017 Jun 7;9(393):eaag2882. doi: 10.1126/scitranslmed.aag2882.
  PMID: 28592562
  citation: Brown PJ, Zhong J, Frood R, Currie S, Gilbert A, Appelt AL, Sebag-Montefiore D, Scarsbrook A. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT. Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2790-2799. doi: 10.1007/s00259-019-04495-1. Epub 2019 Sep 4.
  PMID: 31482428
  citation: Bardosi ZR, Dejaco D, Santer M, Kloppenburg M, Mangesius S, Widmann G, Ganswindt U, Rumpold G, Riechelmann H, Freysinger W. Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification. Cancers (Basel). 2022 Jan 18;14(3):477. doi: 10.3390/cancers14030477.
  PMID: 35158745
  citation: Cho HH, Lee HY, Kim E, Lee G, Kim J, Kwon J, Park H. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol. 2021 Nov 12;4(1):1286. doi: 10.1038/s42003-021-02814-7.
  PMID: 34773070
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  responsible party type: Principal Investigator
  investigator affiliation: IRCCS San Raffaele
  investigator full name: Antonio Esposito
  investigator title: Professor
  mesh term: Coronary Artery Disease
  mesh term: Myocardial Ischemia
  mesh term: Coronary Disease