Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions
ABSTRACT Background Mammography is effective in reducing breast cancer mortality, but it has false positive results that cause subsequent interventions such as biopsy or interval repeat mammography. Thus, there is a clinical unmet need for accurate molecular classifiers that can reduce unnecessary a...
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| Format: | Article |
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Wiley
2025-04-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70767 |
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| author | Hideo Akiyama Lora Barke Therese B. Bevers Suzanne J. Rose Jennifer J. Hu Kelly A. McAleese Shellie S. Campos Satoshi Kondou Jun Atsumi Thomas F. Soriano |
| author_facet | Hideo Akiyama Lora Barke Therese B. Bevers Suzanne J. Rose Jennifer J. Hu Kelly A. McAleese Shellie S. Campos Satoshi Kondou Jun Atsumi Thomas F. Soriano |
| author_sort | Hideo Akiyama |
| collection | DOAJ |
| description | ABSTRACT Background Mammography is effective in reducing breast cancer mortality, but it has false positive results that cause subsequent interventions such as biopsy or interval repeat mammography. Thus, there is a clinical unmet need for accurate molecular classifiers that can reduce unnecessary additional imaging and/or invasive diagnostic procedures for low‐risk women. Method We performed miRNA profiling on a prospectively collected serum specimen obtained from each of the 432 subjects who received an abnormal mammogram or imaging result and then selected 265 subjects for further analysis. The miRNA classifier, named EarlyGuard, was generated based on a novel logistic regression model using “paired miRNAs” where the two miRNAs of interest exhibit the same properties. Results The classifier developed using the training set of 174 subjects enrolled at seven investigative sites resulted in a negative predictive value (NPV) and a sensitivity of 96.4% and 91.2%, respectively. The classifier was validated using the test set consisting of 91 subjects enrolled at three investigative sites, two of which were not included in the training set. The resulting NPV and sensitivity were estimated similarly to be 96.9% and 95.8%, respectively. Conclusions 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. |
| format | Article |
| id | doaj-art-73dbd795838444048a9e6a2cdaa2a42e |
| institution | DOAJ |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-73dbd795838444048a9e6a2cdaa2a42e2025-08-20T03:15:05ZengWileyCancer Medicine2045-76342025-04-01148n/an/a10.1002/cam4.70767Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast LesionsHideo Akiyama0Lora Barke1Therese B. Bevers2Suzanne J. Rose3Jennifer J. Hu4Kelly A. McAleese5Shellie S. Campos6Satoshi Kondou7Jun Atsumi8Thomas F. Soriano9Toray Industries, Inc. Kamakura Kanagawa JapanInvision Sally Jobe/Radiology Imaging Associates Englewood Colorado USADivision of OVP, Department of Clinical Cancer Prevention, Cancer Prevention and Population Sciences The University of Texas MD Anderson Cancer Center Houston Texas USADepartment of Research and Discovery, Stamford Health, Breast Center Stamford Health Stamford Connecticut USADepartment of Public Health Science University of Miami School of Medicine Miami Florida USAThe Women's Imaging Center Denver Colorado USAJohn Muir Health, Walnut Creek and Concord California USAToray Industries, Inc. Kamakura Kanagawa JapanToray Industries, Inc. Tokyo JapanDiagnostic Oncology CRO, LLC Oxford Connecticut USAABSTRACT Background Mammography is effective in reducing breast cancer mortality, but it has false positive results that cause subsequent interventions such as biopsy or interval repeat mammography. Thus, there is a clinical unmet need for accurate molecular classifiers that can reduce unnecessary additional imaging and/or invasive diagnostic procedures for low‐risk women. Method We performed miRNA profiling on a prospectively collected serum specimen obtained from each of the 432 subjects who received an abnormal mammogram or imaging result and then selected 265 subjects for further analysis. The miRNA classifier, named EarlyGuard, was generated based on a novel logistic regression model using “paired miRNAs” where the two miRNAs of interest exhibit the same properties. Results The classifier developed using the training set of 174 subjects enrolled at seven investigative sites resulted in a negative predictive value (NPV) and a sensitivity of 96.4% and 91.2%, respectively. The classifier was validated using the test set consisting of 91 subjects enrolled at three investigative sites, two of which were not included in the training set. The resulting NPV and sensitivity were estimated similarly to be 96.9% and 95.8%, respectively. Conclusions 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.https://doi.org/10.1002/cam4.70767abnormal mammogrambenign breast lesionsbreast cancerliquid biopsyserum miRNA |
| spellingShingle | Hideo Akiyama Lora Barke Therese B. Bevers Suzanne J. Rose Jennifer J. Hu Kelly A. McAleese Shellie S. Campos Satoshi Kondou Jun Atsumi Thomas F. Soriano Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions Cancer Medicine abnormal mammogram benign breast lesions breast cancer liquid biopsy serum miRNA |
| title | Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions |
| title_full | Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions |
| title_fullStr | Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions |
| title_full_unstemmed | Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions |
| title_short | Performance of a Logistic Regression Model Using Paired miRNAs to Stratify Abnormal Mammograms for Benign Breast Lesions |
| title_sort | performance of a logistic regression model using paired mirnas to stratify abnormal mammograms for benign breast lesions |
| topic | abnormal mammogram benign breast lesions breast cancer liquid biopsy serum miRNA |
| url | https://doi.org/10.1002/cam4.70767 |
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