Ignite Creation Date:
2025-12-25 @ 2:22 AM
Ignite Modification Date:
2026-03-01 @ 10:02 AM
Study NCT ID:
NCT03706534
Status:
UNKNOWN
Last Update Posted:
2019-10-29
First Post:
2018-10-11
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}], 'ancestors': [{'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D001941', 'term': 'Breast Diseases'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D001706', 'term': 'Biopsy'}], 'ancestors': [{'id': 'D003581', 'term': 'Cytodiagnosis'}, {'id': 'D003584', 'term': 'Cytological Techniques'}, {'id': 'D019411', 'term': 'Clinical Laboratory Techniques'}, {'id': 'D019937', 'term': 'Diagnostic Techniques and Procedures'}, {'id': 'D003933', 'term': 'Diagnosis'}, {'id': 'D013048', 'term': 'Specimen Handling'}, {'id': 'D003949', 'term': 'Diagnostic Techniques, Surgical'}, {'id': 'D013514', 'term': 'Surgical Procedures, Operative'}, {'id': 'D008919', 'term': 'Investigative Techniques'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR'], 'maskingDescription': 'The study consisted of 10 readers with varying levels of training and experience providing analysis on a randomized set of 300 patients\' breast ultrasound data with and without S-Detect for Breast. Two reading periods separated by at least 3-week washout, totaling 600 cases analyzed per reader. PI and her associate have knowledge about patients diagnosis and other information. So, they are exclueded in readers for "reviewing". And all breast US images are de-indentified.'}, 'primaryPurpose': 'DEVICE_FEASIBILITY', 'interventionModel': 'CROSSOVER', 'interventionModelDescription': 'This clinical study performed by multiple reader multiple case (MRMC) study design, where as set of clinical readers evaulate under multiple reading condition. All Interpreting physician(reader) independently read all of the cases. (fully-crossed design).'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 300}}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2018-09-20', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2019-10', 'completionDateStruct': {'date': '2020-01-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2019-10-27', 'studyFirstSubmitDate': '2018-10-11', 'studyFirstSubmitQcDate': '2018-10-11', 'lastUpdatePostDateStruct': {'date': '2019-10-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2018-10-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2019-11-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Concordance rate', 'timeFrame': '2 days', 'description': 'Breast Imaging Reporting and Data System descriptors suggested by S-Detect for Breast are in good agreement with those selected by experts. In other words, the Breast Imaging Reporting and Data System Lexicon values generated by S-Detect for Breast are not statistically different from the consensus of experts.\n\nBreast Imaging Reporting and Data System Assessment Category Score: The user makes the final decision on the Assessment Category Score. Using this Score, S-Detect displays the assessment description.\n\nCategory 0: Incomplete - Need Additional Imaging Evaluation Category 1: Negative Category 2: Benign Category 3: Probably Benign Category 4a: Low suspicion for malignancy Category 4b: Moderate suspicion for malignancy Category 4c: High suspicion for Malignancy Category 5: Highly Suggestive of Malignancy Category 6: Known Biopsy-Proven Malignancy'}], 'secondaryOutcomes': [{'measure': 'Reporting time', 'timeFrame': '2 day', 'description': 'Measure reporting time of Breast Imaging Reporting and Data System Lexicon value in Breast imaging by radiologists without S-Detect for Breast and also measured report time by radiologists with S-Detect for Breast.'}, {'measure': 'Consensus', 'timeFrame': '2 day', 'description': 'Evaluate the consensus between manually reading of Breast Imaging without assistance and Automatically detection results(Breast Imaging Reporting and Data System Lexicons). Average of consensus is evaluated in both of Expert group and non-expert group.'}, {'measure': 'Accuracy', 'timeFrame': '7 day', 'description': 'Comparing to the Breast Biopsy results, The accuracy of Breast Imaging results by radiologists with CADx will be evaluated.'}, {'measure': 'Sensitivity', 'timeFrame': '7 day', 'description': 'Comparing to the Breast Biopsy results, The sensitivity of Breast Imaging results by radiologists with CADx will be evaluated.'}, {'measure': 'Specificity', 'timeFrame': '7 day', 'description': 'Comparing to the Breast Biopsy results, The specificity of Breast Imaging results by radiologists with CADx will be evaluated.'}, {'measure': 'Area Under Curve', 'timeFrame': '7 day', 'description': 'Comparing to the Breast Biopsy results, Area Under Curve (ROC analysis) of Breast Imaging results by radiologists with CADx will be evaluated.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Breast cancer', 'Breast Imaging'], 'conditions': ['Breast Cancer', 'Breast Lesions', 'Breast Mass']}, 'descriptionModule': {'briefSummary': 'This study evaluates a second review of ultrasound images of breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by Samsung Medical Imaging, to see if this artificial intelligence will help the Radiologist make more accurate diagnoses.', 'detailedDescription': 'Using ultrasound images prospectively acquired, the purpose of this study entails a second review of ultrasound images with suspicious breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by SamsungMedison Co.,Ltd.\n\nThe images will be reviewed by the radiologists twice: first without, and then with assistance of artificial intelligence program by SamsungMedison Co., Ltd.\n\nBIRADS system will be used in this study.\n\nThe objectives of the study are twofold: to quantify the statistical equivalence of radiologists\' opinion and AI\'s output (CADe), and to check BIRADS score-based diagnostic accuracy (CADx) that is gained by the Radiologists\' use of this interactive tool'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '19 Years', 'healthyVolunteers': True, 'eligibilityCriteria': '1. Inclusion Criteria:\n\n * Adult females or males recommended for ultrasound-guided breast lesion biopsy or ultrasound follow-up with at least one suspicious lesion\n * Age \\> 18 years\n * Able to provide informed consent\n2. Exclusion Criteria:\n\n * Unable to read and understand English\n * Unable or unwilling to provide informed consent\n * A patient with current or previous diagnosis of breast cancer in the same quadrant\n * Unable or unwilling to undergo study procedures\n3. Subject Characteristics\n\n 1. Number of Subjects: 300 subjects from 300 separate breast lesions can be acquired. If a subject has more than 1 suspicious lesion, each may be chosen by the radiologist attending as suitable for "second review".\n 2. Gender and Age of Subjects: Adult females or males aged 18 years or older who meet all of the inclusion criteria and none of the exclusion criteria will be considered for enrollment. Minors are excluded as breast cancer is very rare in this age group.\n 3. Racial and Ethnic Origin: There are no enrollment exclusions based on economic status, race, or ethnicity. Based on local and United States census data, the expected ethnic distribution will be approximately 26 Hispanic (approx. 16%) and 134 non-Hispanic people. Furthermore, the expected racial distribution is expected to be approximately 126 White (approx. 79% of the whole study), 21 Black or African America (13%), 8 Asian (5%), and 5 of other categories (3%).\n 4. Vulnerable Subjects: It is unlikely that any UR students or employees will be enrolled unless their primary physician refers them to UR Medicine Breast Imaging at Red Creek for breast ultrasound and a suspicious lesion is found. We do not expect any of these referrals to be from staffs who work directly with the PIs.'}, 'identificationModule': {'nctId': 'NCT03706534', 'briefTitle': 'Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms', 'organization': {'class': 'INDUSTRY', 'fullName': 'Samsung Medison'}, 'officialTitle': 'Breast Ultrasound Image Reviewed With Assistance of Deep Learning', 'orgStudyIdInfo': {'id': '300.08-2018-Samsungmedison-S'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'Manual review', 'description': 'The images will be reviewed by the radiologists using BIRADS scheme without any assistance of artificial assistance. This review will be done off-line using a separate program in entirely manual mode. During this review, BIRADS descriptor choices by each radiologist and the time it takes for the radiologist to make such decision will be stored. Radiologists also make assessment decision without any intervention from artificial intelligence. 10 radiologists review manually.', 'interventionNames': ['Device: Ultrasound Image review with CADe', 'Device: Ultrasound Image review with CADx', 'Device: Ultrasound Image manual review', 'Procedure: Biopsy']}, {'type': 'EXPERIMENTAL', 'label': 'Review by S-Detect for Breast', 'description': 'The same images will be separately processed by the artificial intelligence system (S-Detect for Breast) by Samsung. The two results, one by the radiologists and the other by artificial intelligence system, will be compared to statistically quantify equivalence (CADe).', 'interventionNames': ['Device: Ultrasound Image review with CADe', 'Device: Ultrasound Image manual review']}, {'type': 'EXPERIMENTAL', 'label': 'Review with assistance of S-Detect for Breast', 'description': 'Second, the images will be reviewed by the radiologists with the help of artificial intelligence system, which is an interactive tool automatically providing recommendations on BIRADS descriptor choices that can be modified by the radiologists. The radiologists, after selecting all the descriptors of BIRADS, will decide the assessment categories. These decisions will be compared with the ground truths generated from the biopsy results or a 24-month follow-up (CADx).', 'interventionNames': ['Device: Ultrasound Image review with CADx', 'Device: Ultrasound Image manual review', 'Procedure: Biopsy']}], 'interventions': [{'name': 'Ultrasound Image review with CADe', 'type': 'DEVICE', 'otherNames': ['S-Detect', 'S-Detect for Breast', 'CADe', 'Computer-Assisted Detection Device'], 'description': 'This software is a computer-aided detection (CADe) software application, designed to assist radiologist to analyze breast ultrasound images. S-Detect automatically segments and classifies shape, orientation, margin, lesion boundary, echo pattern, and posterior feature characteristics of user-selected region of interest. The device uses deep learning methods to perform tissue segmentation and classification of images.', 'armGroupLabels': ['Manual review', 'Review by S-Detect for Breast']}, {'name': 'Ultrasound Image review with CADx', 'type': 'DEVICE', 'otherNames': ['S-Detect', 'S-Detect for Breast', 'CADx', 'Computer-Assisted Diagnostic Device'], 'description': 'This software is also a computer-assisted diagnostic(CADx) software application, designed to assist a medical doctor in determining diagnosis by presenting whether a lesion is malignant in a breast ultrasound image obtained from an ultrasound imaging device.', 'armGroupLabels': ['Manual review', 'Review with assistance of S-Detect for Breast']}, {'name': 'Ultrasound Image manual review', 'type': 'DEVICE', 'otherNames': ['Convetional Ultrasound image'], 'description': 'The images will be reviewed by the radiologists using BIRADS scheme without any assistance of artificial assistance. This review will be done off-line using a separate program in entirely manual mode. During this review, BIRADS descriptor choices by each radiologist and the time it takes for the radiologist to make such decision will be stored.', 'armGroupLabels': ['Manual review', 'Review by S-Detect for Breast', 'Review with assistance of S-Detect for Breast']}, {'name': 'Biopsy', 'type': 'PROCEDURE', 'description': 'Suspicious lesions found on breast ultrasound are then followed either by ultrasound guided biopsy or ultrasound imaging every 6 months for two years. For those who undergo biopsy, ultrasound provides images which are used to localize the lesion and guide the placement of the biopsy needle. The sample is sent to pathology for diagnosis, while the ultrasound guidance images are stored. For those who have imaging follow-up, ultrasound images of the breast mass are obtained, digitally stored and interpreted by the radiologist typically using BIRADS scheme.', 'armGroupLabels': ['Manual review', 'Review with assistance of S-Detect for Breast']}]}, 'contactsLocationsModule': {'locations': [{'zip': '14642', 'city': 'Rochester', 'state': 'New York', 'country': 'United States', 'facility': 'University of Rochester', 'geoPoint': {'lat': 43.15478, 'lon': -77.61556}}], 'overallOfficials': [{'name': "Avice O'Connell", 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Department of Imaging Sciences, University of Rochester'}, {'name': 'Kevin Parker', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Department of Electrical & Computer Engineering, University of Rochester'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Samsung Medison', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'University of Rochester', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}