Performance of a Logistic Regression Model Using Paired <scp>miRNAs</scp> to Stratify Abnormal Mammograms for Benign Breast Lesions
Our miRNA classifier has produced promising results that could be used in conjunction with mammography or other imaging procedures to reduce unnecessary invasive diagnostic procedures for women who are unlikely to have a suspicious or worse result on a subsequent diagnostic biopsy. Additional studies will be conducted in larger cohorts to determine if the sensitivity of the classifier will be improved.
Authors
- Satoshi Kondou
- Therese B. Bevers (ORCID: https://orcid.org/0000-0001-6006-3108)
- Kelly A. McAleese
- Jennifer J. Hu (ORCID: https://orcid.org/0000-0002-0021-5699)
- Suzanne J. Rose (ORCID: https://orcid.org/0000-0001-5770-826X)
- Lora D. Barke (ORCID: https://orcid.org/0000-0002-3241-1678)
- S. Sánchez Campos (ORCID: https://orcid.org/0000-0003-2194-9971)
- Hideo Akiyama (ORCID: https://orcid.org/0009-0001-1555-7253)
- Thomas F. Soriano
- Jun Atsumi
Institutions
- John Muir Health (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of Miami (US)
- Stamford Hospital (US)
- The Women’s Imaging Center (US)
- Toray Industries, Inc. (Japan) (JP)
- Invision Sally Jobe (US)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1002/cam4.70767
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Toray Industries