Ignite Creation Date:
2025-12-25 @ 4:31 AM
Ignite Modification Date:
2025-12-26 @ 3:33 AM
Study NCT ID:
NCT07299318
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-23
First Post:
2025-12-09
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D000077273', 'term': 'Thyroid Cancer, Papillary'}], 'ancestors': [{'id': 'D000231', 'term': 'Adenocarcinoma, Papillary'}, {'id': 'D000230', 'term': 'Adenocarcinoma'}, {'id': 'D002277', 'term': 'Carcinoma'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D013964', 'term': 'Thyroid Neoplasms'}, {'id': 'D004701', 'term': 'Endocrine Gland Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D006258', 'term': 'Head and Neck Neoplasms'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D013959', 'term': 'Thyroid Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 3200}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-01-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2026-05-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-22', 'studyFirstSubmitDate': '2025-12-09', 'studyFirstSubmitQcDate': '2025-12-22', 'lastUpdatePostDateStruct': {'date': '2025-12-23', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-23', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area Under the Receiver Operating Characteristic Curve for a Multimodal Deep Learning Model Based on Cervical Ultrasound in Predicting Lymph Node Metastasis', 'timeFrame': 'Within 2 months after the completion of subject enrollment', 'description': 'The researcher will employ a multimodal deep learning model that integrates preoperative cervical color Doppler ultrasound images with corresponding structured text reports. The final output of the model is a predicted probability of lymph node metastasis for each patient (a continuous value between 0 and 1). This predicted probability will be compared with postoperative histopathological diagnosis results (the gold standard). A receiver operating characteristic curve will be plotted for the model, and its area under the curve will be calculated.This is the gold standard metric for evaluating the discriminative ability of a binary classification model (metastasis vs. non-metastasis). A higher AUC value indicates stronger overall discriminative power of the model.'}, {'measure': 'Sensitivity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis', 'timeFrame': 'Within 2 months after the completion of subject enrollment.', 'description': 'This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly identify patients with papillary thyroid carcinoma who truly have cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed with lymph node metastasis through postoperative pathology, as well as the number of patients predicted as "positive" (i.e., predicted to have metastasis) by the model, in order to calculate the sensitivity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Sensitivity = Number of true positive patients / Total number of positive patients confirmed by postoperative pathology.'}, {'measure': 'Specificity of a Multimodal Deep Learning Model Based on Cervical Ultrasound for Predicting Lymph Node Metastasis', 'timeFrame': 'Within 2 months after the completion of subject enrollment.', 'description': 'This metric aims to evaluate the ability of the constructed multimodal deep learning model to correctly rule out patients with papillary thyroid carcinoma who have not developed cervical lymph node metastasis, under the optimal diagnostic threshold. Researchers need to collect the number of patients diagnosed without lymph node metastasis via postoperative pathology, as well as the number of patients predicted by the model as "negative" (i.e., predicted to have no metastasis), in order to calculate the specificity of the cervical ultrasound-based multimodal deep learning model in predicting lymph node metastasis. Calculation formula: Specificity = Number of true negative patients / Total number of negative patients confirmed by postoperative pathology.'}], 'secondaryOutcomes': [{'measure': 'The pathologically confirmed lymph node metastasis rate in the study cohort', 'timeFrame': 'Within 2 months after the completion of subject enrollment', 'description': 'It refers to the percentage of patients with at least one metastatic lymph node confirmed by postoperative pathological examination, relative to the total number of individuals in the corresponding study population. Researchers need to collect the number of patients diagnosed with lymph node metastasis through postoperative pathological examination.'}, {'measure': 'Adjusted Odds Ratios for Clinical Factors Associated with Pathologically Confirmed Lymph Node Metastasis', 'timeFrame': 'Within 2 months after the completion of subject enrollment', 'description': 'Researchers need to collect the outcome variable (i.e., postoperatively pathologically confirmed lymph node metastasis status) and its exposure variables (such as the specific location of the primary tumor within the thyroid gland, maximum tumor diameter, patient age, etc.). Using these variables, the adjusted odds ratios are calculated to reflect, after adjusting for other confounding factors, how many times more likely patients with a specific exposure characteristic (e.g., tumor located in the upper pole) are to have lymph node metastasis compared to patients in the reference group (e.g., tumor located in the lower pole).'}, {'measure': 'The weighted Kappa coefficient for the consistency between model-predicted pTNM stage and pathological stage', 'timeFrame': 'Within 2 months after the completion of subject enrollment', 'description': "Researchers need to collect and record the model-predicted pTNM stage and the patient's true pTNM stage to evaluate the consistency between the model-predicted complete pTNM stage and the pathological stage."}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Papillary Thyroid Carcinoma']}, 'descriptionModule': {'briefSummary': 'Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy in clinical practice, accounting for approximately 85% of all thyroid malignancies. The occurrence of cervical lymph node metastasis further increases the risk of local tumor recurrence and distant metastasis, thereby reducing patient survival rates. Pathological examinations reveal that approximately 30-80% of PTC patients have lymph node metastasis. Early detection of metastatic lymph nodes and the development of individualized treatment plans are crucial for improving patient prognosis. Currently, the primary method for diagnosing lymph node metastasis is ultrasound-guided fine-needle aspiration, but its accuracy is limited by sample quality and carries a risk of false-negative results. In recent years, deep learning technology has demonstrated significant potential in the field of medical image analysis. Therefore, the investigators aim to develop a deep learning model based on neck ultrasound to more accurately predict lymph node metastasis.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Clinical data from patients who underwent thyroidectomy at West China Hospital of Sichuan University and its affiliated branch hospitals between October 2020 and October 2025 were retrospectively collected and analyzed.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nCases aged 18-80 years who underwent thyroid ultrasound examination and postoperative pathological examination of the thyroid.\n\nCases with a first-time diagnosis of papillary thyroid carcinoma. Cases who underwent lymph node dissection\n\nExclusion Criteria:\n\nCases aged \\<18 years or \\>80 years. Cases with poor-quality ultrasound images. Cases with incompletely visualized nodules. Cases with images showing multiple distinct lesions. Cases belonging to special populations. Cases with concurrent other tumors. Cases with a history of thyroid cancer resection'}, 'identificationModule': {'nctId': 'NCT07299318', 'briefTitle': 'Multimodal Deep Learning for Lymph Node Metastasis in Thyroid Cancer', 'organization': {'class': 'OTHER', 'fullName': 'West China Hospital'}, 'officialTitle': 'A Multicenter Study on Developing a Multimodal Deep Learning Model Based on Color Doppler Ultrasound for Predicting Lymph Node Metastasis and Cancer Staging in Papillary Thyroid Carcinoma', 'orgStudyIdInfo': {'id': '2025(2352)'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Papillary thyroid carcinoma group', 'interventionNames': ['Other: not intervention']}], 'interventions': [{'name': 'not intervention', 'type': 'OTHER', 'description': 'This is a retrospective observational study in which participants will not undergo any interventions, and only data collection and analysis will be performed on the participants.', 'armGroupLabels': ['Papillary thyroid carcinoma group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '610041', 'city': 'Chengdu', 'state': 'Sichuan', 'country': 'China', 'contacts': [{'name': 'Jianyong Lei', 'role': 'CONTACT', 'email': 'leijianyong@scu.edu.cn', 'phone': '+86 19983137992'}, {'name': 'Yu Feng', 'role': 'CONTACT', 'email': '1350502131@qq.com', 'phone': '+86 15183042703'}], 'facility': 'West China hospital of Sichuan University', 'geoPoint': {'lat': 30.66667, 'lon': 104.06667}}], 'centralContacts': [{'name': 'Jianyong Lei', 'role': 'CONTACT', 'email': 'leijianyong@scu.edu.cn', 'phone': '+86 19983137992'}, {'name': 'Yu Feng', 'role': 'CONTACT', 'email': '1350502131@qq.com'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'West China Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Clinical Doctorate', 'investigatorFullName': 'Yu Feng', 'investigatorAffiliation': 'West China Hospital'}}}}