Jay Baker

Overview:

As a radiologist in the Division of Breast Imaging, I am interested in studying techniques to better detect and assess breast lesions that may represent breast cancer. The major focus of my research activity involves identifying features of breast lesions on mammography/tomosynthesis, ultrasound and MRI that reliably indicate breast cancer or, equally important, reliably indicate a lesion is not breast cancer and biopsy can be safely avoided. 

Breast cancer is the most common malignancy occurring in women and the second most frequent cause of non-skin cancer deaths among women. Screening mammography programs have repeatedly shown a reduction in the mortality from breast cancer by 30 to 50%. However, breast imaging suffers from a lack of specificity. The result is that 60 to 80% of breast biopsies performed in this country are for benign lesions and are therefore - in retrospect - unnecessary. Because of the overlap in imaging features of benign and malignant lesions, however, these lesions cannot be differentiated without tissue sampling, and the extraordinary number of breast biopsies performed markedly increases the cost of breast cancer prevention programs and is an impediment to breast screening for some women. To overcome this limitation, we are working to identify previously unrecognized features of breast lesions.  Some of these features appear to confirm that a lesion is definitively benign without the need for biopsy.  Other features have identified a particular appearance for breast cancer with features that mimic other benign lesions, thus allowing earlier diagnosis and fewer overlooked breast cancers. We are assessing these features with large reader studies to both determine the accuracy and to confirm that the features can be taught and recognized by radiologists at all levels of breast imaging experience.  If successful, widespread adoption and recognition of these features may greatly reduce the number of women who undergo a needle biopsy for a benign breast lesion.  Conversely, widespread recognition of other features may reduce delays in diagnosis of breast cancer.

Positions:

Professor of Radiology

Radiology, Breast Imaging
School of Medicine

Chief, Breast Imaging

Radiology, Breast Imaging
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

B.A. 1988

University of Pennsylvania

M.D. 1992

Duke University

Resident, Radiology

Duke University

Grants:

Breast Elemental Composition Imaging

Administered By
Radiology
Awarded By
National Institutes of Health
Role
Investigator
Start Date
End Date

Publications:

Findings on Surveillance Imaging After Preoperative Partial Breast Irradiation for Early Stage Breast Cancer.

PURPOSE: To evaluate the mammographic sequelae of preoperative accelerated partial breast irradiation (APBI) delivered via either stereotactic radiosurgery or a conventionally fractionated regimen. METHODS AND MATERIALS: This multicenter, retrospective study evaluated surveillance mammograms from patients enrolled in 2 prospective, preoperative APBI clinical trials. At 1 site, 31 patients with cT1N0 invasive carcinomas or low- or intermediate-grade ductal carcinoma in situ (<2 cm) received preoperative stereotactic radiosurgery and had a total of 186 mammograms available for review. At the second site, 180 mammograms from 25 patients with cT1-2 (<3 cm) unifocal invasive carcinomas treated with conventionally fractionated, preoperative APBI were reviewed. Findings were compared with those of 26 early stage breast cancers treated with conventional postoperative whole breast radiation therapy. RESULTS: At a median follow-up of 61 months, 17 patients (55%) treated with single-dose APBI exhibited exuberant fat necrosis at the lumpectomy site. Fat necrosis was believed to be clinically palpable in 5 (16%) of these patients within the first 3 years of follow-up. Exuberant fat necrosis developed in 5 patients (20%) treated with fractionated APBI over a median 68-month follow-up period but only 2 of those patients (8%) who underwent conventional whole breast radiation therapy. CONCLUSIONS: In situ tumor targeting in the preoperative setting allows relative sparing of normal tissue but results in a larger and more vigorous area of change on surveillance imaging, potentially reflecting the interaction of surgical resection with an irradiated tissue bed. High-dose stereotactic radiosurgery in particular increases the risk of developing a uniquely robust and well-demarcated pattern of fat necrosis on mammogram that may also present clinically. With many ongoing studies evaluating the preoperative treatment approach, defining the landscape of expected imaging sequelae will provide useful anticipatory guidance for clinicians and patients.
Authors
Natarajan, B; Spiegel, D; Nichols, EM; Feigenberg, S; Blitzblau, R; Broadwater, G; Duffy, EA; Baker, JA; Horton, JK
MLA Citation
Natarajan, Brahma, et al. “Findings on Surveillance Imaging After Preoperative Partial Breast Irradiation for Early Stage Breast Cancer.Int J Radiat Oncol Biol Phys, vol. 102, no. 4, Nov. 2018, pp. 1374–81. Pubmed, doi:10.1016/j.ijrobp.2018.05.059.
URI
https://scholars.duke.edu/individual/pub1346394
PMID
30170870
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
102
Published Date
Start Page
1374
End Page
1381
DOI
10.1016/j.ijrobp.2018.05.059

Preoperative Partial Breast Radiation Therapy: Short-term Imaging Outcomes With Two Unique Treatment Regimens

Authors
Horton, JK; Baker, JA; Blitzblau, R; Georgiade, GS; Hwang, ES; Duffy, EA; Morgan, M; Feigenberg, SJ; Citron, W; Kesmodel, S; Bellavance, E; Drogula, C; Tkaczuk, K; Galandak, J; Nichols, EM
MLA Citation
Horton, J. K., et al. “Preoperative Partial Breast Radiation Therapy: Short-term Imaging Outcomes With Two Unique Treatment Regimens.” International Journal of Radiation Oncology*Biology*Physics, vol. 93, no. 3, Elsevier BV, 2015, pp. E46–E46. Crossref, doi:10.1016/j.ijrobp.2015.07.658.
URI
https://scholars.duke.edu/individual/pub1130774
Source
crossref
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
93
Published Date
Start Page
E46
End Page
E46
DOI
10.1016/j.ijrobp.2015.07.658

Breast tomosynthesis: state-of-the-art and review of the literature.

Authors
MLA Citation
Baker, Jay A., and Joseph Y. Lo. “Breast tomosynthesis: state-of-the-art and review of the literature.Acad Radiol, vol. 18, no. 10, Oct. 2011, pp. 1298–310. Pubmed, doi:10.1016/j.acra.2011.06.011.
URI
https://scholars.duke.edu/individual/pub733258
PMID
21893296
Source
pubmed
Published In
Acad Radiol
Volume
18
Published Date
Start Page
1298
End Page
1310
DOI
10.1016/j.acra.2011.06.011

Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.

This study investigates the effect of class imbalance in training data when developing neural network classifiers for computer-aided medical diagnosis. The investigation is performed in the presence of other characteristics that are typical among medical data, namely small training sample size, large number of features, and correlations between features. Two methods of neural network training are explored: classical backpropagation (BP) and particle swarm optimization (PSO) with clinically relevant training criteria. An experimental study is performed using simulated data and the conclusions are further validated on real clinical data for breast cancer diagnosis. The results show that classifier performance deteriorates with even modest class imbalance in the training data. Further, it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features. Finally, it is shown that there is no clear preference between oversampling and no compensation approach and some guidance is provided regarding a proper selection.
Authors
Mazurowski, MA; Habas, PA; Zurada, JM; Lo, JY; Baker, JA; Tourassi, GD
MLA Citation
Mazurowski, Maciej A., et al. “Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.Neural Netw, vol. 21, no. 2–3, Mar. 2008, pp. 427–36. Pubmed, doi:10.1016/j.neunet.2007.12.031.
URI
https://scholars.duke.edu/individual/pub711847
PMID
18272329
Source
pubmed
Published In
Neural Networks : the Official Journal of the International Neural Network Society
Volume
21
Published Date
Start Page
427
End Page
436
DOI
10.1016/j.neunet.2007.12.031

Effect of display resolution on the detection of mammographie lesions

For diagnosis of breast cancer by mammography, the mammograms must be viewed by a radiologist. The purpose of this study was to determine the effect of display resolution on the specific clinical task of detection of breast lesions by a human observer. Using simulation techniques, this study proceeded through four stages. First, we inserted simulated masses and calcifications into raw digital mammograms. The resulting images were processed according to standard image processing techniques and appropriately windowed and leveled. The processed images were blurred according to MTFs measured from a clinical Cathode Ray Tube display. JNDMetrix, a Visual Discrimination Model, examined the images to estimate human detection. The model results suggested that detection of masses and calcifications decreased under standard CRT resolution. Future work will confirm these results with human observer studies. (This work was supported by grants NIH R21-CA95308 and USAMRMC W81XWH-04-1-0323.).
Authors
Saunders, RS; Samei, E; Johnson, J; Baker, J
MLA Citation
Saunders, R. S., et al. “Effect of display resolution on the detection of mammographie lesions.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 5749, Sept. 2005, pp. 243–50. Scopus, doi:10.1117/12.595682.
URI
https://scholars.duke.edu/individual/pub758684
Source
scopus
Published In
Progress in Biomedical Optics and Imaging Proceedings of Spie
Volume
5749
Published Date
Start Page
243
End Page
250
DOI
10.1117/12.595682