Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010051', 'term': 'Ovarian Neoplasms'}], 'ancestors': [{'id': 'D004701', 'term': 'Endocrine Gland Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D010049', 'term': 'Ovarian Diseases'}, {'id': 'D000291', 'term': 'Adnexal Diseases'}, {'id': 'D005831', 'term': 'Genital Diseases, Female'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D005833', 'term': 'Genital Neoplasms, Female'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D000091662', 'term': 'Genital Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D006058', 'term': 'Gonadal Disorders'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 32}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-09-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-04-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-26', 'studyFirstSubmitDate': '2025-08-26', 'studyFirstSubmitQcDate': '2025-08-26', 'lastUpdatePostDateStruct': {'date': '2025-09-04', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-09-04', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-04-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'OCS product inspection', 'timeFrame': 'Before the operation'}]}, 'conditionsModule': {'conditions': ['Ovarian Cancer']}, 'descriptionModule': {'briefSummary': 'To study the performance of OCS products based on exosome detection technology in monitoring the recurrence of baseline CA125-negative ovarian cancer patients after initial treatment, and to explore reliable methods for monitoring the recurrence of baseline CA125-negative ovarian cancer patients after initial treatment.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with ovarian cancer', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion criteria:\n\n1. Women over 18 years old;\n2. Pathologically confirmed as stage I-IV primary epithelial ovarian malignant tumor;\n3. ECOG \\< 2;\n4. The expected survival period exceeds 6 months;\n5. CA125 ≤ 35 U/ml before initial treatment;\n6. Undergo surgery and adjuvant chemotherapy;\n7. The period between diagnosis and enrollment does not exceed 8 weeks;\n8. Be willing to provide blood samples for OCS testing during the research process;\n9. Sign the informed consent form.\n\nExclusion criteria\n\n1. Have had other malignant tumors within the last five years;\n2. Have received any treatment for ovarian cancer;\n3. Benign ovarian mass;\n4. Non-primary ovarian tumors;\n5. Combined with other malignant tumors;\n6. Patients receiving neoadjuvant therapy;\n7. Pregnancy.'}, 'identificationModule': {'nctId': 'NCT07153705', 'briefTitle': 'OCS Products Based on Exosome Technology Were Applied in the Recurrence Monitoring Study After the Initial Treatment of Baseline CA125-negative Ovarian Cancer', 'organization': {'class': 'OTHER', 'fullName': 'Shandong University'}, 'officialTitle': 'OCS Products Based on Exosome Technology Were Applied in the Recurrence Monitoring Study After the Initial Treatment of Baseline CA125-negative Ovarian Cancer', 'orgStudyIdInfo': {'id': 'CM-2401'}}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Beihua Kong', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'archiater', 'investigatorFullName': 'Beihua Kong', 'investigatorAffiliation': 'Shandong University'}}}}