Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-30 @ 3:13 AM
Study NCT ID:
NCT07306858
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-31
First Post:
2025-12-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'interventionBrowseModule': {'meshes': [{'id': 'D019370', 'term': 'Observation'}], 'ancestors': [{'id': 'D008722', 'term': 'Methods'}, {'id': 'D008919', 'term': 'Investigative Techniques'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 276}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12-16', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2026-01-15', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-26', 'studyFirstSubmitDate': '2025-12-15', 'studyFirstSubmitQcDate': '2025-12-15', 'lastUpdatePostDateStruct': {'date': '2025-12-31', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-12-29', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-29', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnostic performance of the deep learning model for differentiating acute and chronic osteoporotic vertebral compression fractures', 'timeFrame': 'At the time of image analysis', 'description': 'The diagnostic performance of the deep learning model in differentiating acute and chronic osteoporotic vertebral compression fractures based on CT images, evaluated using the area under the receiver operating characteristic curve (AUC).'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Osteoporotic vertebral compression fracture', 'Deep learning'], 'conditions': ['Osteoporotic Vertebral Compression Fractures']}, 'descriptionModule': {'briefSummary': 'Osteoporotic vertebral compression fractures are common in older adults and may present as either acute or chronic fractures. Correctly distinguishing acute from chronic fractures is clinically important because treatment strategies and management decisions differ depending on fracture chronicity. However, differentiating acute and chronic osteoporotic vertebral compression fractures based on imaging findings alone can be challenging in routine clinical practice.\n\nThis retrospective study aims to develop an intelligent diagnostic system based on computed tomography (CT) images to differentiate acute and chronic osteoporotic vertebral compression fractures. Clinical and imaging data from patients diagnosed with osteoporotic vertebral compression fractures will be collected from the First Affiliated Hospital of Chongqing Medical University and an additional medical center. A deep learning model will be trained to automatically analyze CT images and classify fractures as acute or chronic.\n\nThe results of this study may help improve the accuracy and efficiency of fracture chronicity assessment using CT images and provide supportive information for clinical decision-making regarding treatment selection in patients with osteoporotic vertebral compression fractures.', 'detailedDescription': 'This study is a retrospective, multicenter observational study designed to develop and evaluate a deep learning-based system for differentiating acute and chronic osteoporotic vertebral compression fractures using computed tomography (CT) images.\n\nPatients diagnosed with osteoporotic vertebral compression fractures who underwent both CT and magnetic resonance imaging (MRI) examinations will be retrospectively collected from the First Affiliated Hospital of Chongqing Medical University and one additional medical center between January 2023 and September 2025. Clinical data, including age, sex, and dual-energy X-ray absorptiometry (DXA) results, as well as complete DICOM-format CT and MRI images, will be collected. The interval between CT and MRI examinations must be less than two weeks. Patients with pathological fractures caused by infection or tumor, the presence of foreign materials such as bone cement or metallic hardware, or poor image quality with significant artifacts will be excluded.\n\nThe study workflow includes data collection, model development, performance evaluation, and model interpretability analysis. Multiple deep learning segmentation models, including U-Net, U-Mamba, and UNETR++, will first be evaluated for vertebral body segmentation performance. Based on the optimal segmentation results, classification models such as VGG-16, DenseNet-121, Vision Transformer (ViT), and Transformer-based architectures will be trained to differentiate acute and chronic compression fractures. The best-performing model will be selected to construct the final classification system.\n\nModel performance for segmentation tasks will be assessed using Dice similarity coefficient and loss values. Classification performance will be evaluated in an external validation dataset using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Receiver operating characteristic curves and confusion matrices will be generated to visualize model performance.\n\nTo improve model interpretability, gradient-weighted class activation mapping (Grad-CAM) will be applied to generate heatmaps highlighting image regions that contribute most to model predictions. These heatmaps will be overlaid on CT images to visually demonstrate how the model differentiates acute and chronic osteoporotic vertebral compression fractures.\n\nBased on a predefined sample size calculation assuming a sensitivity of 0.90, a significance level of 0.05, and an allowable error of 0.05, a total of 276 patients (138 acute and 138 chronic cases) are expected to be included in this study.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '40 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population consists of adult patients aged 40 years or older who were diagnosed with osteoporotic vertebral compression fractures and underwent CT and MRI examinations at the participating centers.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Inclusion Criteria:\n* Patients diagnosed with osteoporotic vertebral compression fractures.\n* Patients who underwent both CT and MRI examinations of the spine, with an interval of less than 2 weeks between examinations.\n* Availability of complete CT and MRI imaging data in DICOM format.\n* Availability of complete clinical information, including age, sex, and dual-energy X-ray absorptiometry (DXA) results.\n* Age 50 years or older at the time of imaging.\n\nExclusion Criteria:\n\n* Vertebral compression fractures caused by infection or malignancy.\n* Presence of foreign materials, including bone cement or metallic hardware.\n* Poor image quality or significant imaging artifacts that affect analysis.'}, 'identificationModule': {'nctId': 'NCT07306858', 'acronym': 'CT-DL-OVCF', 'briefTitle': 'CT-Based Deep Learning for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital of Chongqing Medical University'}, 'officialTitle': 'A Deep Learning Model Based on CT Images for Differentiating Acute and Chronic Osteoporotic Vertebral Compression Fractures', 'orgStudyIdInfo': {'id': 'KX2025-KYC1056-01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Acute Osteoporotic Vertebral Compression Fracture Group', 'description': 'Patients diagnosed with acute osteoporotic vertebral compression fractures based on clinical assessment and imaging findings.', 'interventionNames': ['Other: No Intervention (Observational Study)']}, {'label': 'Chronic Osteoporotic Vertebral Compression Fracture Group', 'description': 'Patients diagnosed with chronic osteoporotic vertebral compression fractures based on clinical assessment and imaging findings.', 'interventionNames': ['Other: No Intervention (Observational Study)']}], 'interventions': [{'name': 'No Intervention (Observational Study)', 'type': 'OTHER', 'description': 'This is a retrospective observational study. No therapeutic, diagnostic, or preventive intervention is assigned as part of the study. All analyses are based on previously acquired clinical and imaging data.', 'armGroupLabels': ['Acute Osteoporotic Vertebral Compression Fracture Group', 'Chronic Osteoporotic Vertebral Compression Fracture Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '400016', 'city': 'Chongqing', 'state': 'Chongqing Municipality', 'country': 'China', 'contacts': [{'name': 'Xin Fan', 'role': 'CONTACT', 'email': '202770@hospital.cqmu.edu.cn', 'phone': '+86 23 89011876'}, {'role': 'CONTACT', 'email': '202770@hospital.cqmu.edu.cn'}], 'facility': 'The First Affiliated Hospital of Chongqing Medical University', 'geoPoint': {'lat': 29.56026, 'lon': 106.55771}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': 'A final decision on sharing individual participant data has not been made at the time of registration. Potential data sharing will be considered in accordance with institutional review board approval, patient privacy protection, and relevant data governance policies.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Xin Fan', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Xin Fan', 'investigatorAffiliation': 'First Affiliated Hospital of Chongqing Medical University'}}}}