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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

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.


Condition or disease Intervention/treatment
Breast Cancer Other: mammography

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Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Breast Cancer Screening With Mammography: Diagnostic Assessment of an Artificial
Estimated Study Start Date : January 2020
Estimated Primary Completion Date : December 2023
Estimated Study Completion Date : June 2024

Resource links provided by the National Library of Medicine



Intervention Details:
  • Other: mammography
    standard mammography including craniocaudal (CC) and mediolateral oblique (MLO) views


Primary Outcome Measures :
  1. area under curve (AUC) [ Time Frame: 3 years ]
    area under receiver operating characteristic (ROC) curve in percentage (%)

  2. accuracy [ Time Frame: 3 years ]
    proportion of true results(both true positives and true negatives) among whole instances

  3. sensitivity [ Time Frame: 3 years ]
    true positive rate in percentage(%) derived by ROC analysis

  4. specificity [ Time Frame: 3 years ]
    true negative rate in percentage (%) derived by ROC analysis



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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   Yes
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.
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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04156880


Contacts
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Contact: Chiu Wing CHU 35052299 winniechu@cuhk.edu.hk

Sponsors and Collaborators
Chinese University of Hong Kong
IBM China/Hong Kong Limited

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Responsible Party: Professor Winnie W.C. Chu, Professor, Chinese University of Hong Kong
ClinicalTrials.gov Identifier: NCT04156880     History of Changes
Other Study ID Numbers: NTEC-2019-0655
First Posted: November 8, 2019    Key Record Dates
Last Update Posted: November 8, 2019
Last Verified: November 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Professor Winnie W.C. Chu, Chinese University of Hong Kong:
mammography
artificial intelligence
deep learning
Additional relevant MeSH terms:
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Breast Neoplasms
Neoplasms by Site
Neoplasms
Breast Diseases
Skin Diseases