Principal Investigator

Jeffrey R
Awardee Organization

Duke University
United States

Fiscal Year
Activity Code
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date

Breast Cancer Detection Consortium

Mammography is an early detection modality for breast cancer that is implemented widely in the United States, has established benchmarks of performance, and in most studies throughout the world has been demonstrated to reduce mortality due to the disease. This relatively inexpensive x-ray imaging of the breast also provides a location that can be directly sampled through needle biopsy which leads generally to an unambiguous pathologic diagnosis of invasive cancer, carcinoma in situ, or benign findings. No system is perfect and mammographic screening, particularly in the US, prompts over 1.6 million biopsies per year detecting approximately 230,000 invasive and 60,000 non-invasive cancers for a positive predictive value of less than 20%. There may be substantial room to improve on this and reduce the number of biopsies but this improvement must not sacrifice detection rates so the negative predictive value (NPV, identification of true negatives) must remain very high. In this Biomarker Development Laboratory application, we propose to test whether a combination of mammographic feature analysis and candidate biomarkers that we have identified can achieve an NPV that would be acceptable to patients and providers to prevent unnecessary breast biopsies. One of the biomarkers is a type of circulating giant cell termed “Cancer Associated Macrophage Like” (CAML) that can only be detected using freshly drawn whole blood, we propose to conduct a prospective trial at Duke University in women undergoing breast cancer diagnosis. Our realistic goal is to accrue ~1000 women over the course of 4 years for which full field digital mammography has been performed. The images will undergo feature extraction for decision modeling. Blood will be analyzed for the presence and type of CAML cells, immunosignaturing using the high density peptide arrays developed by Stephen Johnston at Arizona State, and measurements of two specific analytes that have the highest sensitivity and specificity for basal type cancers, CA125 and TP53 autoantibodies. As feature analysis from imaging alone can achieve, at least for masses on mammography, an AUC of ~0.9, the study is designed to determine whether the biomarkers have sufficient complementary information to the imaging and each other to increase the AUC to 0.95 allowing us to identify a threshold where there is a 98% NPV. We will make use of the most careful and consistent standard operating procedures, the best candidate biomarkers, and the most well developed imaging algorithms to make this a definitive study.


  • Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. Journal of the American College of Radiology : JACR. 2018 Mar;15(3 Pt B):527-534. Epub 2018 Feb 2. PMID: 29398498
  • Harowicz MR, Saha A, Grimm LJ, Marcom PK, Marks JR, Hwang ES, Mazurowski MA. Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer? Journal of magnetic resonance imaging : JMRI. 2017 Nov;46(5):1332-1340. Epub 2017 Feb 9. PMID: 28181348
  • Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Academic radiology. 2017 Sep;24(9):1139-1147. Epub 2017 May 11. PMID: 28506510
  • Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast cancer research and treatment. 2019 Jan;173(2):455-463. Epub 2018 Oct 16. PMID: 30328048
  • Narayanan PL, Raza SEA, Hall AH, Marks JR, King L, West RB, Hernandez L, Guppy N, Dowsett M, Gusterson B, Maley C, Hwang ES, Yuan Y. Unmasking the immune microecology of ductal carcinoma in situ with deep learning. NPJ breast cancer. 2021 Mar 1;7(1):19. PMID: 33649333
  • Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, Lynch T, van Oirsouw M, Rogers K, Stone N, Wallis M, Teuwen J, Wesseling J, Hwang ES, Lo JY. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology. 2022 Apr;303(1):54-62. Epub 2022 Jan 4. PMID: 34981975
  • Tang CM, Zhu P, Li S, Makarova OV, Amstutz PT, Adams DL. Blood-based biopsies-clinical utility beyond circulating tumor cells. Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2018 Dec;93(12):1246-1250. Epub 2018 Oct 19. PMID: 30369050
  • Fackler MJ, Cho S, Cope L, Gabrielson E, Visvanathan K, Wilsbach K, Meir-Levi D, Lynch CF, Marks J, Geradts J, Regan MM, Viale G, Wolff AC, Sukumar S, Umbricht CB. DNA methylation markers predict recurrence-free interval in triple-negative breast cancer. NPJ breast cancer. 2020 Jan 31;6:3. doi: 10.1038/s41523-020-0145-3. eCollection 2020. PMID: 32025567
  • Hou R, Peng Y, Grimm LJ, Ren Y, Mazurowski MA, Marks JR, King LM, Maley CC, Hwang ES, Lo JY. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE transactions on bio-medical engineering. 2022 May;69(5):1639-1650. Epub 2022 Apr 21. PMID: 34788216
  • Mazurowski MA, Saha A, Harowicz MR, Cain EH, Marks JR, Marcom PK. Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer. Journal of magnetic resonance imaging : JMRI. 2019 Jun;49(7):e231-e240. Epub 2019 Jan 22. PMID: 30672045