Ignite Creation Date:
2025-12-24 @ 3:40 PM
Ignite Modification Date:
2026-01-01 @ 4:10 PM
Study NCT ID:
NCT04918992
Status:
UNKNOWN
Last Update Posted:
2021-06-09
First Post:
2021-06-02
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Post Radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010386', 'term': 'Pelvic Neoplasms'}], 'ancestors': [{'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D001185', 'term': 'Artificial Intelligence'}], 'ancestors': [{'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2021-06-22', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-06', 'completionDateStruct': {'date': '2024-08-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-06-08', 'studyFirstSubmitDate': '2021-06-02', 'studyFirstSubmitQcDate': '2021-06-08', 'lastUpdatePostDateStruct': {'date': '2021-06-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-06-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'The sensitivity of AI prediction system in prediction the radiation proctitis candidates', 'timeFrame': 'baseline', 'description': 'The sensitivity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy'}], 'primaryOutcomes': [{'measure': 'The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis', 'timeFrame': 'baseline', 'description': 'The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy'}], 'secondaryOutcomes': [{'measure': 'The specificity of AI prediction system in prediction radiation proctitis', 'timeFrame': 'baseline', 'description': 'The specificity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['radiation proctitis', 'pelvic cancers', 'Artificial Intelligence'], 'conditions': ['Pelvic Cancer']}, 'descriptionModule': {'briefSummary': 'In this study, investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy. By the system, whether the participants achieve the radiation proctitis will be identified based on the radiomics features extracted from the post radiotherapy Magnetic Resonance Imaging (MRI) . The predictive power to discriminate the radiation proctitis individuals from non-radiation proctitis patients, will be validated in this multicenter, prospective clinical study.', 'detailedDescription': 'This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the radiation proctitis in patients with pelvic cancers based on the post-radiotherapy Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as pelvic cancers will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. Patients with pelvic cancers who received radiotherapy will be enrolled and their post-radiotherapy MRI images will be used to predict their radiation proctitis or not. The clinical symptoms, endoscopic findings, imaging and histopathology as a standard. The predictive efficacy will be tested in this multicenter, prospective clinical study.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'pelvic cancers who underwent radiotherapy will be enrolled in our study.', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* pathologically diagnosed as pelvic tumours\n* intending to receive or undergoing radiotherapy\n* MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed after radiotherapy\n\nExclusion Criteria:\n\n* insufficient imaging quality of MRI (e.g., lack of sequence, motion artifacts)\n* incomplete radiotherapy'}, 'identificationModule': {'nctId': 'NCT04918992', 'acronym': 'MRI-RP-2021', 'briefTitle': 'Post Radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers', 'organization': {'class': 'OTHER', 'fullName': 'Sixth Affiliated Hospital, Sun Yat-sen University'}, 'officialTitle': 'Post-radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers', 'orgStudyIdInfo': {'id': 'MRI-RP'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Artificial Intelligence', 'type': 'DIAGNOSTIC_TEST', 'description': 'investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy'}]}, 'contactsLocationsModule': {'locations': [{'zip': '510000', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'contacts': [{'name': 'Xinjuan Fan, MD', 'role': 'CONTACT'}], 'facility': 'the Sixth Affiliated Hospital of Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'zip': '510655', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'contacts': [{'name': 'Xinjuan Fan, MD', 'role': 'CONTACT', 'email': 'fanxjuan@mail.sysu.edu.cn', 'phone': '+86 13602442569'}], 'facility': 'the Sixth Affiliated Hospital of Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'zip': '650000', 'city': 'Kunming', 'state': 'Yunnan', 'country': 'China', 'contacts': [{'name': 'Zhenhui Li, MD', 'role': 'CONTACT', 'email': 'lizhenhui621@163.com', 'phone': '+86 13698736132'}], 'facility': 'The Third Affiliated Hospital of Kunming Medical College', 'geoPoint': {'lat': 25.03889, 'lon': 102.71833}}, {'zip': '310000', 'city': 'Hangzhou', 'state': 'Zhejiang', 'country': 'China', 'contacts': [{'name': 'Weidong Han, MD', 'role': 'CONTACT', 'email': 'hanwd@zju.edu.cn', 'phone': '+86 13819124503'}], 'facility': 'Sir Run Run Shaw Hospital', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}], 'centralContacts': [{'name': 'Xinjuan Fan, MD', 'role': 'CONTACT', 'email': 'fanxjuan@mail.sysu.edu.cn', 'phone': '+86 13602442569'}], 'overallOfficials': [{'name': 'Xinjuan Fan, MD', 'role': 'STUDY_CHAIR', 'affiliation': 'Sixth Affiliated Hospital, Sun Yat-sen University'}, {'name': 'Weidong Han, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Sir Run Run Shaw Hospital'}, {'name': 'Zhenhui Li, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Third Affiliated Hospital of Kunming Medical College.'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sixth Affiliated Hospital, Sun Yat-sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}