Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining

Area of Science:

• Medical imaging informatics
• Data science in healthcare
• Radiology research

Background:

• Millions of breast imaging examinations generate vast datasets.
• Structured reporting tools, based on the BI-RADS lexicon, are widely used.
• Existing data is often stored in accessible formats.

Purpose of the Study:

• To describe structured reporting in breast imaging.
• To outline the development of large databases for data mining.
• To highlight opportunities for improving breast cancer detection and screening.

Main Methods:

• Utilizing structured reporting tools based on the BI-RADS lexicon.
• Developing large, agile databases for complex data mining.
• Leveraging robust computing power for data analysis.

Main Results:

• Structured reporting generates millions of breast imaging data points.
• Accessible storage facilitates data utilization.
• Advanced computing enables data mining for improved outcomes.

Conclusions:

• Collaboration between data scientists and breast imagers is crucial.
• Data mining holds significant potential for breast cancer detection and screening optimization.
• New data technologies can advance outcomes research and precision medicine.