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Artificial Intelligence in Mammography-Based Breast Cancer Screening

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ClinicalTrials.gov Identifier: NCT04156880
Recruitment Status : Not yet recruiting
First Posted : November 8, 2019
Last Update Posted : November 8, 2019
Sponsor:
Collaborator:
IBM China/Hong Kong Limited
Information provided by (Responsible Party):
Professor Winnie W.C. Chu, Chinese University of Hong Kong

Tracking Information
First Submitted Date November 6, 2019
First Posted Date November 8, 2019
Last Update Posted Date November 8, 2019
Estimated Study Start Date January 2020
Estimated Primary Completion Date December 2023   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: November 6, 2019)
  • area under curve (AUC) [ Time Frame: 3 years ]
    area under receiver operating characteristic (ROC) curve in percentage (%)
  • accuracy [ Time Frame: 3 years ]
    proportion of true results(both true positives and true negatives) among whole instances
  • sensitivity [ Time Frame: 3 years ]
    true positive rate in percentage(%) derived by ROC analysis
  • specificity [ Time Frame: 3 years ]
    true negative rate in percentage (%) derived by ROC analysis
Original Primary Outcome Measures Same as current
Change History No Changes Posted
Current Secondary Outcome Measures Not Provided
Original Secondary Outcome Measures Not Provided
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title Artificial Intelligence in Mammography-Based Breast Cancer Screening
Official Title Breast Cancer Screening With Mammography: Diagnostic Assessment of an Artificial
Brief Summary

Breast cancer (BC) is the most common cancer among women in worldwide and the second leading cause of cancer-related death.

As the corner stone of BC screening, mammography is recognized as one of useful imaging modalities to reduce BC mortality, by virtue of early detection of BC. However, mammography interpretation is inherently subjective assessment, and prone to overdiagnosis.

In recent years, artificial intelligence (AI)-Computer Aided Diagnosis (CAD) systems, characterized by embedded deep-learning algorithms, have entered into the field of BC screening as an aid for radiologist, with purpose to optimize conventional CAD system with weakness of hand-crafted features extraction. For now, stand-alone performance of novel AI-CAD tools have demonstrated promising accuracy and efficiency in BC diagnosis, largely attributed to utilization of convolution neural network(CNNs), and some of them have already achieved radiologist-like level. On the other hand, radiologists' performance on BC screening has shown to be enhanced, by leveraging AI-CAD system as decision support tool. As increasing implementation of commercial AI-CAD system, robust evaluation of its usefulness and cost-effectiveness in clinical circumstances should be undertaken in scenarios mimicking real life before broad adoption, like other emerging and promising technologies. This requires to validate AI-CAD systems in BC screening on multiple, diverse and representative datasets and also to estimate the interface between reader and system. This proposed study seeks to investigate the breast cancer diagnostic performance of AI-CAD system used for reading mammograms. In this work, we will employ a commercially available AI-CAD tool based on deep-learning algorithms (IBM Watson Imaging AI Solution) to identify and characterize the suspicious breast lesions on mammograms. The potential cancer lesions can be labeled and their mammographic features and malignancy probability will be automatically reported. After AI post-processing, we shall further carry out statistical analysis to determine the accuracy of AI-CAD system for BC risk prediction.

Detailed Description Not Provided
Study Type Observational
Study Design Observational Model: Cohort
Time Perspective: Retrospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
Sampling Method Probability Sample
Study Population This is a single institutional retrospective cohort study of patients with mammographic examinations. All patients' data will be retrieved via the electronic patient database of our institution. Patient demographics, imaging and histological data, disease and treatment history will be recorded, including age at onset, details of chemotherapy, time interval of metastasis from diagnosis, surgery for the primary tumor, the length of survival, clinical outcome and so on.
Condition Breast Cancer
Intervention Other: mammography
standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views
Study Groups/Cohorts Not Provided
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Not yet recruiting
Estimated Enrollment
 (submitted: November 6, 2019)
1000
Original Estimated Enrollment Same as current
Estimated Study Completion Date June 2024
Estimated Primary Completion Date December 2023   (Final data collection date for primary outcome measure)
Eligibility Criteria

Inclusion Criteria:

  • Women who had undergone standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views..
  • Histopathology-proven diagnosis is available for patients with breast malignancy, including invasive breast cancer, carcinoma in situ, and borderline lesion et al.
  • As reference standard of benign nature, results from pathology or clinical long-term follow-up (>=2 years) examinations are available for cases without breast malignancy.

Exclusion Criteria:

  • Patients with concurring lesions on mammograms that may influence subsequent AI post-process.
  • Patients without available pathologic diagnosis or long-term follow-up (>=2 years) examinations.
  • Patients who had undergone breast surgical intervention (e.g. lumpectomy and mammoplasty) prior to first mammography.
  • Patients diagnosed with other kinds of malignancy, concurrent with metastasis or infiltration/invasion to breast.
Sex/Gender
Sexes Eligible for Study: Female
Ages Child, Adult, Older Adult
Accepts Healthy Volunteers Yes
Contacts
Contact: Chiu Wing CHU 35052299 winniechu@cuhk.edu.hk
Listed Location Countries Not Provided
Removed Location Countries  
 
Administrative Information
NCT Number NCT04156880
Other Study ID Numbers NTEC-2019-0655
Has Data Monitoring Committee Not Provided
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement Not Provided
Responsible Party Professor Winnie W.C. Chu, Chinese University of Hong Kong
Study Sponsor Chinese University of Hong Kong
Collaborators IBM China/Hong Kong Limited
Investigators Not Provided
PRS Account Chinese University of Hong Kong
Verification Date November 2019