Artificial Intelligence in Mammography-Based Breast Cancer Screening

2019-11-12 18:25:20 | BioPortfolio


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 Design


Breast Cancer




Not yet recruiting


Chinese University of Hong Kong

Results (where available)

View Results


Published on BioPortfolio: 2019-11-12T18:25:20-0500

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Medical and Biotech [MESH] Definitions

Measurement of relative composition of different BREAST tissue types often determined from MAMMOGRAPHY; ULTRASONOGRAPHY; or MRI.

Abnormal accumulation of lymph in the arm, shoulder and breast area associated with surgical or radiation breast cancer treatments (e.g., MASTECTOMY).

Metastatic breast cancer characterized by EDEMA and ERYTHEMA of the affected breast due to LYMPHATIC METASTASIS and eventual obstruction of LYMPHATIC VESSELS by the cancer cells.

Radiographic examination of the breast.

A infiltrating (invasive) breast cancer, relatively uncommon, accounting for only 5%-10% of breast tumors in most series. It is often an area of ill-defined thickening in the breast, in contrast to the dominant lump characteristic of ductal carcinoma. It is typically composed of small cells in a linear arrangement with a tendency to grow around ducts and lobules. There is likelihood of axillary nodal involvement with metastasis to meningeal and serosal surfaces. (DeVita Jr et al., Cancer: Principles & Practice of Oncology, 3d ed, p1205)

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