Ignite Creation Date:
2025-12-26 @ 5:19 PM
Ignite Modification Date:
2025-12-31 @ 11:14 AM
Study NCT ID:
NCT06690606
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-11-15
First Post:
2024-11-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
EOCRCPred: an AI Model to Predict Survival in EOCRC Patients After Surgery
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D015179', 'term': 'Colorectal Neoplasms'}], 'ancestors': [{'id': 'D007414', 'term': 'Intestinal Neoplasms'}, {'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D003108', 'term': 'Colonic Diseases'}, {'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D012002', 'term': 'Rectal Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 250}, 'targetDuration': '1 Month', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2024-11-12', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-11', 'completionDateStruct': {'date': '2025-05-11', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-11-13', 'studyFirstSubmitDate': '2024-11-12', 'studyFirstSubmitQcDate': '2024-11-13', 'lastUpdatePostDateStruct': {'date': '2024-11-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-11-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-05-11', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Assessment of Clinical Characteristics and Survival Status of Subjects Using the CRF Scale', 'timeFrame': '2024.11', 'description': 'Age, Gender, Race, Primary Site, Tumor Diameter, Differentiation Degree, Histology, TNM Stage, CEA, Surgery Type, Number of Resected Lymph Nodes, Tumor Deposits, Neural Invasion, Radiation Sequence, Chemotherapy Sequence, Systemic Therapy Sequence, Marital Status, Household Income, Survival Status, Postoperative Survival Time.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['AI model', 'prediction', 'Early-Onset Colorectal Cancer (EOCRC)', 'postoperative survival', 'surgery'], 'conditions': ['Colorectal Cancer']}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to develop a predictive model for overall survival in patients under the age of 50 who have undergone surgery for early-onset colorectal cancer (EOCRC). The main question it aims to answer is:\n\nCan machine learning models accurately predict the long-term survival of EOCRC patients after surgical treatment?\n\nParticipants who have already undergone surgery for EOCRC as part of their regular medical care will have their clinical data analyzed, with survival outcomes tracked through follow-up assessments. An online survival calculator will also be developed to aid clinicians and patients in predicting personalized survival outcomes.', 'detailedDescription': 'To avoid duplicating information that will be entered or uploaded elsewhere in the record, here is a concise summary of the key components of the study:\n\n* Study Title\\*\\*: \\*EOCRCPred: An AI Model to Predict Survival in Early-onset Colorectal Cancer Patients After Surgery\\*\n* Introduction\\*\\*:\n\nThis study addresses the increasing incidence and mortality of early-onset colorectal cancer (EOCRC) in patients under 50. EOCRC exhibits distinct clinical and pathological features compared to late-onset CRC, including higher recurrence rates and advanced disease stages at diagnosis. Current predictive models for postoperative outcomes in EOCRC are limited, highlighting the need for specialized tools to guide treatment decisions.\n\n* Objectives\\*\\*:\n\n 1. Develop AI models for predicting overall survival (OS) in postoperative M0 EOCRC patients.\n 2. Propose a new survival risk stratification system.\n 3. Deploy an online survival calculator to assist clinical decision-making.\n* Methods\\*\\*:\n\n * \\*\\*Data Source\\*\\*: SEER database (2010-2019) for training/testing; two Chinese hospitals for external validation (2014-2024).\n * \\*\\*Inclusion Criteria\\*\\*: Pathologically confirmed primary EOCRC, radical surgery (stage I-III), and complete follow-up.\n * \\*\\*Models\\*\\*: Six predictive models, including CoxPH, RSF, S-SVM, XGBSE, GBSA, and DeepSurv.\n * \\*\\*Evaluation Metrics\\*\\*: Discrimination (C-index, time-dependent AUC), calibration (Brier score, calibration curves), and clinical utility (Decision Curve Analysis).\n* Statistical Analysis\\*\\*:\n\nComparisons were made using t-tests, Mann-Whitney U tests, and chi-square tests, with P \\< 0.05 indicating significance.\n\n\\*\\*Risk Stratification\\*\\*: Risk groups were classified based on RSF-derived scores (low, intermediate, high), and survival differences were assessed via Kaplan-Meier curves and log-rank tests.\n\nThis streamlined summary covers the primary goals, methodology, and analysis without repeating specifics that will be detailed in other sections of the record.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '49 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Consecutive patients with early-onset, Stage I-III colorectal cancer who underwent radical resection at Putuo Hospital and Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, both affiliated with Shanghai University of Traditional Chinese Medicine, were retrospectively collected between January 2014 and June 2024.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Primary EOCRC confirmed by pathological histological examination (ICD-10 codes: C18.0, C18.2-18.9, C19.9, C20.9)\n* Radical surgery performed (Specific Surgery Codes 30-70, including partial/subtotal colectomy, hemicolectomy, right/left colectomy, and total colectomy, as well as partial or total removal of other organs and regional lymph nodes)\n* Stage I-III disease according to the 7th AJCC-TNM system\n\nExclusion Criteria:\n\n* Patients with multiple primary cancers or other malignancies\n* Survival time of less than 1 month, or absence of postoperative follow-up information\n* Incomplete critical clinical feature information'}, 'identificationModule': {'nctId': 'NCT06690606', 'briefTitle': 'EOCRCPred: an AI Model to Predict Survival in EOCRC Patients After Surgery', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai University of Traditional Chinese Medicine'}, 'officialTitle': 'EOCRCPred: an AI Model for Predicting Overall Survival in Early-Onset Stage I-III Colorectal Cancer Patients Post-Radical Resection-A SEER Database and Dual-Center Chinese Medical Institutions Study', 'orgStudyIdInfo': {'id': 'PTEC-A-2024-61 (S)'}, 'secondaryIdInfos': [{'id': '2021tszk01', 'type': 'OTHER_GRANT', 'domain': 'Health System of PuTuo District in Shanghai'}, {'id': '2023-BSH-02', 'type': 'OTHER_GRANT', 'domain': 'Putuo District Central Hospital'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'external validation cohort', 'description': 'The external validation cohort was composed of primary EOCRC patients who underwent radical resection at Putuo Hospital and Yueyang Hospital, both affiliated with Shanghai University of Traditional Chinese Medicine. The cohort includes patients diagnosed between January 2014 and June 2024.\n\nInclusion criteria: Patients with pathologically confirmed primary EOCRC, aged under 50 years, and who received radical surgery (stages I-III according to the AJCC 7th edition).\n\nExclusion criteria: Patients with multiple primary cancers, survival time under 1 month, or missing critical data.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '200062', 'city': 'Shanghai', 'country': 'China', 'contacts': [{'name': 'Wanli Deng', 'role': 'CONTACT'}], 'facility': 'Putuo Hospital, Shanghai University of Traditional Chinese Medicine', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}, {'zip': '200062', 'city': 'Shanghai', 'country': 'China', 'contacts': [{'name': 'Yabin Gong', 'role': 'CONTACT', 'email': 'gongyabin@hotmail.com', 'phone': '+86 18918923873'}], 'facility': 'Yueyang Hospital of Integrated Traditional Chinese and Western Medicine', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Wanli Deng', 'role': 'CONTACT', 'email': 'tcmdwl@163.com', 'phone': '+86 13166062368'}], 'overallOfficials': [{'name': 'Wanli Deng', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Putuo Hospital, Shanghai University of Traditional Chinese Medicine'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': "The decision to share Individual Participant Data (IPD) has not been finalized due to several considerations. We are evaluating factors such as privacy and confidentiality risks, even with anonymization, as well as the ethical and legal implications of data sharing. Additionally, we must assess whether participants' consent allows for broader data sharing beyond the initial study purpose. These factors are under review, and a final decision will be made with careful consideration of participant protection and compliance with regulatory requirements."}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai University of Traditional Chinese Medicine', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Wanli Deng, MD', 'investigatorAffiliation': 'Shanghai University of Traditional Chinese Medicine'}}}}