Ignite Creation Date:
2025-12-26 @ 2:43 PM
Ignite Modification Date:
2025-12-26 @ 2:43 PM
Study NCT ID:
NCT06366906
Status:
COMPLETED
Last Update Posted:
2024-04-16
First Post:
2024-03-19
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000077195', 'term': 'Squamous Cell Carcinoma of Head and Neck'}], 'ancestors': [{'id': 'D002294', 'term': 'Carcinoma, Squamous Cell'}, {'id': 'D002277', 'term': 'Carcinoma'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D006258', 'term': 'Head and Neck Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 319}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-05-10', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2024-02-10', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-04-15', 'studyFirstSubmitDate': '2024-03-19', 'studyFirstSubmitQcDate': '2024-04-15', 'lastUpdatePostDateStruct': {'date': '2024-04-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-04-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-02-10', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AUC(the area under the curve) values of the model', 'timeFrame': '10 years（This is a retrospective research，we collect 10 years patients, but the project we implement data collection and analysis is 9 months）', 'description': 'The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['HNSCC', 'AI', 'Radiomic', 'MRI']}, 'descriptionModule': {'briefSummary': 'Introduction: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions.\n\nAim: To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features.\n\nMethods: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The radiomics features that affects the prediction of OCLNM in OC and OP SCC. A total of 319 patients with early-stage OC or OP SCC from the hospitals', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Pathologically confirmed, previously untreated oral and oropharyngeal squamous cell carcinoma with radical resection;\n2. MRI examination was performed two weeks before surgery;\n3. All patients with neck dissection and the status of regional lymph nodes was confirmed via pathological examination;\n4. All patients had no clinical evidence of nodal involvement.\n\nExclusion Criteria:\n\n1. Other malignant tumor, such as adenoid cystic carcinoma;\n2. a lack of complete MRI imaging or poor MRI imaging quality;\n3. patients had undergone neck dissection or treated non-surgically;\n4. patients with metastatic disease.'}, 'identificationModule': {'nctId': 'NCT06366906', 'briefTitle': '10-year Retrospective Study of Oral and Maxillofacial Squamous Cell Carcinoma', 'organization': {'class': 'OTHER', 'fullName': 'Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University'}, 'officialTitle': 'Clinicopathological and Prognostic Analysis of Oral and Maxillofacial Squamous Cell Carcinoma: a Single-center 10-year Retrospective Study', 'orgStudyIdInfo': {'id': 'SYSKY-2023-426-01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Cohort A', 'description': 'Randomly (121 cases) divided as the training and test sets in a 7:3 ratio.', 'interventionNames': ['Diagnostic Test: The Resnet50 deep learning (DL) model']}, {'label': 'Cohort B', 'description': 'Segmented into two groups based on the batched collected, which were defined as external validation set1 (n = 68) and external validation set2 (n = 130)', 'interventionNames': ['Diagnostic Test: The Resnet50 deep learning (DL) model']}], 'interventions': [{'name': 'The Resnet50 deep learning (DL) model', 'type': 'DIAGNOSTIC_TEST', 'description': 'The predictive capability of the above Resnet50 deep learning (DL) model was validated in the test set. Based on the AUC and ACC, the best prediction model was identified. To explore the robust of the selected model, ROC analysis was performed the in the external validation set. Moreover, the Log-rank test was applied to evaluate the prognostic value of the model.', 'armGroupLabels': ['Cohort A', 'Cohort B']}]}, 'contactsLocationsModule': {'locations': [{'zip': '510000', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': 'Sun yat-sen memorial hospital', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'zip': '510000', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': 'Sun yat-sun memorial hospital', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}