Artificial Intelligence in Mammography-Based Breast Cancer Screening

Breast Cancer Screening With Mammography: Diagnostic Assessment of an Artificial

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.

Study Overview

• Status: Withdrawn
• Conditions: Breast Cancer
• Intervention / Treatment: Other: mammography

• Study Type: Observational

Contacts and Locations

Study Locations

• Hong Kong
  • Shatin
    • Hong Kong, Shatin, Hong Kong
      • The Chinese University of Hong Kong, Prince of Wale Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• 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.

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
accuracy	proportion of true results(both true positives and true negatives) among whole instances	3 years
sensitivity	true positive rate in percentage(%) derived by ROC analysis	3 years
specificity	true negative rate in percentage (%) derived by ROC analysis	3 years
area under curve (AUC)	area under receiver operating characteristic (ROC) curve in percentage (%)	3 years

Collaborators and Investigators

• Sponsor: Chinese University of Hong Kong
• Collaborators: IBM China/Hong Kong Limited

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2020
• Primary Completion (Actual): December 31, 2023
• Study Completion (Actual): December 31, 2023

Study Registration Dates

• First Submitted: November 6, 2019
• First Submitted That Met QC Criteria: November 6, 2019
• First Posted (Actual): November 8, 2019

Study Record Updates

• Last Update Posted (Actual): February 7, 2024
• Last Update Submitted That Met QC Criteria: February 5, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Keywords: artificial intelligence; deep learning; mammography
• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: 2019.629

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No