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/NCT06032767</url>
    </required_header>
    <id_info>
      <org_study_id>B2022-420-Y01</org_study_id>
      <nct_id>NCT06032767</nct_id>
    </id_info>
    <brief_title>Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning</brief_title>
    <official_title>Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Sun Yat-sen University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Sun Yat-sen University</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The goal of this observational study is to apply the CNN-based DL method to extract the&#xD;
        three-dimensional spatial information of IMRT dose distribution to predict the occurrence&#xD;
        probability of serious radiotherapy and chemotherapy induced oral mucositis(SRCOM), and&#xD;
        compare with a model based on dosimetry, NTCP or doseomics to improve the prediction accuracy&#xD;
        of SRCOM, thus guiding the clinical planning design, reducing the occurrence probability of&#xD;
        OM, and may have the potential value of preventing serious complications and improving the&#xD;
        quality of life in patients with nasopharyngeal carcinoma.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 11, 2023</start_date>
    <completion_date type="Anticipated">December 30, 2024</completion_date>
    <primary_completion_date type="Anticipated">September 30, 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>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>RTOG/EROTC Acute Radiation Reaction Scoring Standard</measure>
      <time_frame>through radiation therapy, an average of 7 weeks</time_frame>
      <description>Toxicity records of oral mucosal Reaction in patients are conducted by professionally trained oncologists</description>
    </primary_outcome>
    <enrollment type="Anticipated">480</enrollment>
    <condition>Nasopharyngeal Carcinoma</condition>
    <condition>Oral Mucositis</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>observational group</intervention_name>
      <description>patients initially diagnosed with nasopharyngeal carcinoma treated with IMRT</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Primary-treated NPC patients undergoing IMRT&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Initial diagnosis, pathological histological diagnosis, the pathological type is&#xD;
               non-keratotic carcinoma (according to the WHO pathological classification).&#xD;
  &#xD;
            -  Initial intensity-modulated radiotherapy (Intensity modulated radiation therapy,&#xD;
               IMRT).&#xD;
  &#xD;
            -  No previous radiotherapy was received.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients with recurrent nasopharyngeal carcinoma.&#xD;
  &#xD;
            -  Radiotherapy plan cannot be obtained.&#xD;
  &#xD;
            -  Previous history of malignancy; previous radiotherapy.&#xD;
  &#xD;
            -  The primary lesion and cervical metastatic lesions have received surgical treatment&#xD;
               (except for diagnostic treatment).&#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_contact>
      <last_name>Fangyun Xie, M.D.</last_name>
      <phone>+8613902205880</phone>
      <email>xiefy@sysucc.org.cn</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>puyun ouyang, M.D.</last_name>
      <phone>+8618565382769</phone>
      <email>ouyangpy@sysucc.org.cn</email>
    </overall_contact_backup>
    <results_reference>
      <citation>Wolden SL, Chen WC, Pfister DG, Kraus DH, Berry SL, Zelefsky MJ. Intensity-modulated radiation therapy (IMRT) for nasopharynx cancer: update of the Memorial Sloan-Kettering experience. Int J Radiat Oncol Biol Phys. 2006 Jan 1;64(1):57-62. doi: 10.1016/j.ijrobp.2005.03.057. Epub 2005 Jun 2.</citation>
      <PMID>15936155</PMID>
    </results_reference>
    <results_reference>
      <citation>Li K, Yang L, Hu QY, Chen XZ, Chen M, Chen Y. Oral Mucosa Dose Parameters Predicting Grade &gt;/=3 Acute Toxicity in Locally Advanced Nasopharyngeal Carcinoma Patients Treated With Concurrent Intensity-Modulated Radiation Therapy and Chemotherapy: An Independent Validation Study Comparing Oral Cavity versus Mucosal Surface Contouring Techniques. Transl Oncol. 2017 Oct;10(5):752-759. doi: 10.1016/j.tranon.2017.06.011. Epub 2017 Jul 21.</citation>
      <PMID>28738294</PMID>
    </results_reference>
    <results_reference>
      <citation>Elad S, Yarom N, Zadik Y, Kuten-Shorrer M, Sonis ST. The broadening scope of oral mucositis and oral ulcerative mucosal toxicities of anticancer therapies. CA Cancer J Clin. 2022 Jan;72(1):57-77. doi: 10.3322/caac.21704. Epub 2021 Oct 29.</citation>
      <PMID>34714553</PMID>
    </results_reference>
    <results_reference>
      <citation>Soutome S, Yanamoto S, Nishii M, Kojima Y, Hasegawa T, Funahara M, Akashi M, Saito T, Umeda M. Risk factors for severe radiation-induced oral mucositis in patients with oral cancer. J Dent Sci. 2021 Oct;16(4):1241-1246. doi: 10.1016/j.jds.2021.01.009. Epub 2021 Feb 9.</citation>
      <PMID>34484592</PMID>
    </results_reference>
    <results_reference>
      <citation>Li PJ, Li KX, Jin T, Lin HM, Fang JB, Yang SY, Shen W, Chen J, Zhang J, Chen XZ, Chen M, Chen YY. Predictive Model and Precaution for Oral Mucositis During Chemo-Radiotherapy in Nasopharyngeal Carcinoma Patients. Front Oncol. 2020 Nov 5;10:596822. doi: 10.3389/fonc.2020.596822. eCollection 2020.</citation>
      <PMID>33224892</PMID>
    </results_reference>
    <results_reference>
      <citation>Gabrys HS, Buettner F, Sterzing F, Hauswald H, Bangert M. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia. Front Oncol. 2018 Mar 5;8:35. doi: 10.3389/fonc.2018.00035. eCollection 2018.</citation>
      <PMID>29556480</PMID>
    </results_reference>
    <results_reference>
      <citation>Zhen X, Chen J, Zhong Z, Hrycushko B, Zhou L, Jiang S, Albuquerque K, Gu X. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol. 2017 Oct 12;62(21):8246-8263. doi: 10.1088/1361-6560/aa8d09.</citation>
      <PMID>28914611</PMID>
    </results_reference>
    <results_reference>
      <citation>Ibragimov B, Toesca D, Chang D, Yuan Y, Koong A, Xing L. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys. 2018 Oct;45(10):4763-4774. doi: 10.1002/mp.13122. Epub 2018 Sep 10.</citation>
      <PMID>30098025</PMID>
    </results_reference>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 4, 2023</study_first_submitted>
    <study_first_submitted_qc>September 11, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 13, 2023</study_first_posted>
    <last_update_submitted>September 11, 2023</last_update_submitted>
    <last_update_submitted_qc>September 11, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 13, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Sun Yat-sen University</investigator_affiliation>
      <investigator_full_name>Fang-Yun Xie</investigator_full_name>
      <investigator_title>professor</investigator_title>
    </responsible_party>
    <keyword>Nasopharyngeal Carcinoma</keyword>
    <keyword>Oral Mucositis</keyword>
    <keyword>deep learning</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Carcinoma</mesh_term>
      <mesh_term>Nasopharyngeal Carcinoma</mesh_term>
      <mesh_term>Mucositis</mesh_term>
      <mesh_term>Stomatitis</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
    </patient_data>
    <!-- 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/NCT06032767
  org study id: B2022-420-Y01
  nct id: NCT06032767
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this observational study is to apply the CNN-based DL method to extract the
        three-dimensional spatial information of IMRT dose distribution to predict the occurrence
        probability of serious radiotherapy and chemotherapy induced oral mucositis(SRCOM), and
        compare with a model based on dosimetry, NTCP or doseomics to improve the prediction accuracy
        of SRCOM, thus guiding the clinical planning design, reducing the occurrence probability of
        OM, and may have the potential value of preventing serious complications and improving the
        quality of life in patients with nasopharyngeal carcinoma.
  observational model: Cohort
  time perspective: Prospective
  measure: RTOG/EROTC Acute Radiation Reaction Scoring Standard
  time frame: through radiation therapy, an average of 7 weeks
  description: Toxicity records of oral mucosal Reaction in patients are conducted by professionally trained oncologists
  intervention type: Other
  intervention name: observational group
  description: patients initially diagnosed with nasopharyngeal carcinoma treated with IMRT
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: N/A
  maximum age: N/A
  healthy volunteers: No
  last name: Fangyun Xie, M.D.
  phone: +8613902205880
  email: xiefy@sysucc.org.cn
  last name: puyun ouyang, M.D.
  phone: +8618565382769
  email: ouyangpy@sysucc.org.cn
  citation: Wolden SL, Chen WC, Pfister DG, Kraus DH, Berry SL, Zelefsky MJ. Intensity-modulated radiation therapy (IMRT) for nasopharynx cancer: update of the Memorial Sloan-Kettering experience. Int J Radiat Oncol Biol Phys. 2006 Jan 1;64(1):57-62. doi: 10.1016/j.ijrobp.2005.03.057. Epub 2005 Jun 2.
  PMID: 15936155
  citation: Li K, Yang L, Hu QY, Chen XZ, Chen M, Chen Y. Oral Mucosa Dose Parameters Predicting Grade >/=3 Acute Toxicity in Locally Advanced Nasopharyngeal Carcinoma Patients Treated With Concurrent Intensity-Modulated Radiation Therapy and Chemotherapy: An Independent Validation Study Comparing Oral Cavity versus Mucosal Surface Contouring Techniques. Transl Oncol. 2017 Oct;10(5):752-759. doi: 10.1016/j.tranon.2017.06.011. Epub 2017 Jul 21.
  PMID: 28738294
  citation: Elad S, Yarom N, Zadik Y, Kuten-Shorrer M, Sonis ST. The broadening scope of oral mucositis and oral ulcerative mucosal toxicities of anticancer therapies. CA Cancer J Clin. 2022 Jan;72(1):57-77. doi: 10.3322/caac.21704. Epub 2021 Oct 29.
  PMID: 34714553
  citation: Soutome S, Yanamoto S, Nishii M, Kojima Y, Hasegawa T, Funahara M, Akashi M, Saito T, Umeda M. Risk factors for severe radiation-induced oral mucositis in patients with oral cancer. J Dent Sci. 2021 Oct;16(4):1241-1246. doi: 10.1016/j.jds.2021.01.009. Epub 2021 Feb 9.
  PMID: 34484592
  citation: Li PJ, Li KX, Jin T, Lin HM, Fang JB, Yang SY, Shen W, Chen J, Zhang J, Chen XZ, Chen M, Chen YY. Predictive Model and Precaution for Oral Mucositis During Chemo-Radiotherapy in Nasopharyngeal Carcinoma Patients. Front Oncol. 2020 Nov 5;10:596822. doi: 10.3389/fonc.2020.596822. eCollection 2020.
  PMID: 33224892
  citation: Gabrys HS, Buettner F, Sterzing F, Hauswald H, Bangert M. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia. Front Oncol. 2018 Mar 5;8:35. doi: 10.3389/fonc.2018.00035. eCollection 2018.
  PMID: 29556480
  citation: Zhen X, Chen J, Zhong Z, Hrycushko B, Zhou L, Jiang S, Albuquerque K, Gu X. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol. 2017 Oct 12;62(21):8246-8263. doi: 10.1088/1361-6560/aa8d09.
  PMID: 28914611
  citation: Ibragimov B, Toesca D, Chang D, Yuan Y, Koong A, Xing L. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys. 2018 Oct;45(10):4763-4774. doi: 10.1002/mp.13122. Epub 2018 Sep 10.
  PMID: 30098025
  responsible party type: Principal Investigator
  investigator affiliation: Sun Yat-sen University
  investigator full name: Fang-Yun Xie
  investigator title: professor
  mesh term: Carcinoma
  mesh term: Nasopharyngeal Carcinoma
  mesh term: Mucositis
  mesh term: Stomatitis
  sharing ipd: Undecided