Ignite Creation Date:
2025-12-25 @ 12:08 AM
Ignite Modification Date:
2025-12-25 @ 10:08 PM
Study NCT ID:
NCT07236658
Status:
RECRUITING
Last Update Posted:
2025-11-19
First Post:
2025-11-15
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Deep Learning for Musculoskeletal Complications in Breast Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002062', 'term': 'Bursitis'}], 'ancestors': [{'id': 'D007592', 'term': 'Joint Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D010808', 'term': 'Physical Examination'}], 'ancestors': [{'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 133}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2027-01-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-15', 'studyFirstSubmitDate': '2025-11-15', 'studyFirstSubmitQcDate': '2025-11-15', 'lastUpdatePostDateStruct': {'date': '2025-11-19', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-11-19', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-07-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'shoulder range of motion', 'timeFrame': 'shoulder range of motion will be measured in all directions using a goniometer before treatment and during follow-up visits. (0, month 1, month 3, month 6)', 'description': 'Shoulder range of motion will be measured in all directions using a goniometer before treatment and during follow-up visits'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Postmastectomy Lymphedema Syndrome', 'Breast Cancer Surgery Pain', 'Osteoporosis Secondary', 'Shoulder Adhesive Capsulitis', 'Axillary Web Syndrome']}, 'descriptionModule': {'briefSummary': "Survival after breast cancer has increased due to early diagnosis and advances in treatment methods. Musculoskeletal problems related to cancer and its treatment constitute a significant part of the daily practice of physiatrists and rehabilitation specialists involved in oncological rehabilitation.\n\nLymphedema can occur at any stage of a patient's life following breast cancer. Patients with breast cancer-related lymphedema require lifelong treatment, and as the stage of lymphedema progresses, response to therapy decreases. Advanced stages of lymphedema negatively affect functional status, and patients experience difficulties in performing activities of daily living.\n\nAxillary web syndrome (AWS) is characterized by a taut cord extending from the axilla to the volar surface of the wrist, typically appearing within the first 8 weeks postoperatively. AWS can complicate the administration of radiotherapy. Shoulder dysfunction may occur independently or in association with AWS. In particular, scapular dyskinesis developing after mastectomy can lead to secondary shoulder conditions such as rotator cuff syndrome or adhesive capsulitis, which are commonly observed in these patients.\n\nPeripheral neuropathy is frequently seen in patients receiving chemotherapy, adversely affecting daily life and sometimes preventing continuation of treatment. Other complications related to chemotherapy and radiotherapy include cardiotoxicity, pulmonary toxicity, fatigue, osteoporosis, and cognitive impairment.\n\nThere are also specific painful syndromes that may occur after breast cancer, including post-mastectomy pain syndrome, phantom breast pain, and musculoskeletal symptoms associated with aromatase inhibitors. All these conditions can significantly impair daily functioning and even hinder continuation of cancer treatment. Therefore, predicting these complications and implementing or developing preventive interventions is crucial.\n\nIf it is possible to predict the early development of lymphedema, axillary web syndrome, peripheral neuropathy, and painful syndromes after breast cancer, early intervention may prevent progression. This study is designed to develop and validate a predictive model using deep learning methods to determine the risk of these complications in patients undergoing breast cancer surgery. Among deep learning architectures, ResNet50, AlexNet, GoogleNet, and UNet, which have been widely used in recent studies, are planned to be implemented.\n\nAdditionally, based on the results of this study, a risk calculation program will be developed, allowing clinicians to input baseline patient data and calculate the individual patient's risk for each complication prior to treatment. No specific risk is expected in the study."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study will include adult women (over 18 years of age) who are scheduled to undergo surgery for unilateral breast cancer. Patients with a history of bilateral breast cancer, male breast cancer, inability to comply with follow-up visits, or those who are children, pregnant, postpartum, breastfeeding, in intensive care, or with impaired consciousness, as well as legally incapacitated individuals, will be excluded', 'genderDescription': 'being female (a genetic characteristic) and undergoing surgery due to breast cancer', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nbeing over 18 years of age being scheduled for surgery due to unilateral breast cancer\n\nExclusion Criteria:\n\nbeing unable to comply with follow-up visits bilateral breast cancer male breast cancer Children, pregnant women, postpartum and/or breastfeeding women Individuals in intensive care or with impaired consciousness Legally incapacitated persons will not be included in the study'}, 'identificationModule': {'nctId': 'NCT07236658', 'briefTitle': 'Deep Learning for Musculoskeletal Complications in Breast Cancer', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Ankara Etlik City Hospital'}, 'officialTitle': 'AI-Powered Deep Learning Models for Prospective Prediction of Musculoskeletal Complications After Breast Cancer Surgery: Focus on Lymphedema, Axillary Web Syndrome, Neuropathy, and Pain', 'orgStudyIdInfo': {'id': 'AEŞH-EK-2025-145'}}, 'armsInterventionsModule': {'interventions': [{'name': 'physical examination', 'type': 'DIAGNOSTIC_TEST', 'description': 'Demographic data and upper-extremity circumferential measurements, shoulder range of motion, upper-extremity dermatome examination, pathological diagnosis and stage, treatments received, comorbidities, and routine laboratory tests including ESR, CRP, complete blood count, ALT, AST, protein, albumin, BUN, creatinine, and GFR will be recorded. The VAS (Visual Analog Scale), Central Sensitization Inventory, Hospital Anxiety and Depression Scale, and Quick-DASH disability questionnaire will be completed.\n\nDuring monthly follow-ups, if the patient receives radiotherapy (RT) or chemotherapy (CT), these data will be documented in terms of number and dose. In addition to the physical examination performed at each follow-up visit (baseline, month 1, month 3, and month 6), the Hospital Anxiety and Depression Scale and the Quick-DASH disability questionnaire.\n\nAt the final 6-month follow-up, all assessments will be repeated, and data will be analyzed after the last patient has completed follow-up.'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Ankara', 'status': 'RECRUITING', 'country': 'Turkey (Türkiye)', 'facility': 'Ankara Etlik City Hospital', 'geoPoint': {'lat': 39.91987, 'lon': 32.85427}}], 'centralContacts': [{'name': 'Başak Mansız Kaplan', 'role': 'CONTACT', 'email': 'basakmansiz@hotmail.com', 'phone': '+905358582176'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'I do not consider sharing IPD ethically appropriate'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Ankara Etlik City Hospital', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Assoc. Prof.', 'investigatorFullName': 'Başak Mansız-Kaplan', 'investigatorAffiliation': 'Ankara Etlik City Hospital'}}}}