Ignite Creation Date:
2025-12-25 @ 1:14 AM
Ignite Modification Date:
2026-01-01 @ 2:09 PM
Study NCT ID:
NCT06451393
Status:
RECRUITING
Last Update Posted:
2024-06-11
First Post:
2024-06-04
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D013274', 'term': 'Stomach Neoplasms'}], 'ancestors': [{'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': 'D013272', 'term': 'Stomach Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D020360', 'term': 'Neoadjuvant Therapy'}], 'ancestors': [{'id': 'D003131', 'term': 'Combined Modality Therapy'}, {'id': 'D013812', 'term': 'Therapeutics'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2013-02-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-06', 'completionDateStruct': {'date': '2026-12-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-06-08', 'studyFirstSubmitDate': '2024-06-04', 'studyFirstSubmitQcDate': '2024-06-08', 'lastUpdatePostDateStruct': {'date': '2024-06-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-06-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-09-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Pathological Complete Response', 'timeFrame': 'Assessed within 30 days after radical resection surgery.', 'description': 'Pathological complete response (pCR) was defined as no viable cells remained in the primary tumor lesions and the dissected lymph nodes.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Advanced gastric cancer', 'Neoadjuvant chemotherapy', 'Multimodal', 'Pathological complete response'], 'conditions': ['Gastric Cancer', 'Chemotherapy Effect']}, 'descriptionModule': {'briefSummary': 'This study aims to develop a multimodal model combining radiomic and pathomic features to predict pathological complete response (pCR) in advanced gastric cancer patients undergoing neoadjuvant chemotherapy (NAC). The researchers intended to collected pre-intervention CT images and pathological slides from patients, extract radiomic and pathomic features, and build a prediction model using machine learning algorithms. The model will be validated using a separate cohort of patients. This research intend to build a radiomic-pathomic model that can outperform models based on either radiomic or pathomic features alone, aiming to improve the prediction of pCR in gastric cancer.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '20 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received neoadjuvant chemotherapy and radical gastrectomy;', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received NAC and radical gastrectomy;\n* patients who underwent abdominal multidetector computed tomography (CT) inspection, gastroscope, and tumor tissue biopsy before any intervention started;\n* Lesions that are assessable according to The Response Evaluation Criteria in Solid Tumors Version 1.1\n\nExclusion Criteria:\n\n* Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection;\n* patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling;\n* patients with insufficient data.'}, 'identificationModule': {'nctId': 'NCT06451393', 'briefTitle': 'Predicting Gastric Cancer Response to Chemo With Multimodal AI Model', 'organization': {'class': 'OTHER', 'fullName': 'Sixth Affiliated Hospital, Sun Yat-sen University'}, 'officialTitle': 'A Radio-Pathomic Multimodal Machine Learning Model for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer: A Retrospective Observational Study', 'orgStudyIdInfo': {'id': 'E2021088'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Neoadjuvant chemotherapy with radical tumor resection surgery', 'description': '(i) Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection; (ii) patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling; (iii) patients with insufficient data.', 'interventionNames': ['Drug: Neoadjuvant chemotherapy with radical tumor resection surgery']}], 'interventions': [{'name': 'Neoadjuvant chemotherapy with radical tumor resection surgery', 'type': 'DRUG', 'description': 'All patients were pathologically diagnosed as advanced gastric cancer, all receive neoadjuvant chemotherapy, after the completion of neoadjuvant chemotherapy, all patients receive radical tumor resection surgery (partial gastrectomy or total gastrectomy, as proper).', 'armGroupLabels': ['Neoadjuvant chemotherapy with radical tumor resection surgery']}]}, 'contactsLocationsModule': {'locations': [{'zip': '510655', 'city': 'Guangzhou', 'state': 'Guangdong', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Xiangen Lu, Master', 'role': 'CONTACT', 'email': 'zslyllb@mail.sysu.edu.cn', 'phone': '+86 20 3837 9764'}], 'facility': 'The Sixth Affiliated Hospital, Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}], 'centralContacts': [{'name': 'Yonghe Chen, MD', 'role': 'CONTACT', 'email': 'chenyhe@mail2.sysu.edu.cn', 'phone': '+86 135 6038 6150'}, {'name': 'Junsheng Peng, MD', 'role': 'CONTACT', 'email': 'pengjsh@mail.sysu.edu.cn', 'phone': '+86 13802963578'}], 'overallOfficials': [{'name': 'Junsheng Peng, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Sixth Affiliated Hospital, Sun Yat-sen University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': 'The data that support the findings of this study are available from Dr. Junsheng Peng (E-mail: pengjsh@mail.sysu.edu.cn) upon reasonable request.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sixth Affiliated Hospital, Sun Yat-sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}