Radiology
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


DOI: 10.1148/radiol.2481070846
This Article
Right arrow Figures Only
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Appendix E1
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Li, K.-L.
Right arrow Articles by Hylton, N. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Li, K.-L.
Right arrow Articles by Hylton, N. M.
(Radiology 2008;248:79-87.)
© RSNA, 2008


Breast Imaging

Invasive Breast Cancer: Predicting Disease Recurrence by Using High-Spatial-Resolution Signal Enhancement Ratio Imaging1

Ka-Loh Li, PhD, Savannah C. Partridge, PhD, Bonnie N. Joe, MD, PhD, Jessica E. Gibbs, Ying Lu, PhD, Laura J. Esserman, MD, and Nola M. Hylton, PhD

1 From the Departments of Radiology (K.L.L., B.N.J., J.E.G., Y.L., N.M.H.) and Surgery (L.J.E.), University of California, San Francisco, 1 Irving St, Room AC-109, San Francisco, CA 94143-1290; and Department of Radiology, University of Washington, Seattle, Wash (S.C.P.). Received May 15, 2007; revision requested July 20; revision received October 22; accepted January 15, 2008; final version accepted February 19. Supported by National Institutes of Health grant CA 069587. Address correspondence to K.L.L., 185 Berry St, Suite 180A, San Francisco, CA 94107-0946 (e-mail: ka-loh.li{at}radiology.ucsf.edu).

Purpose: To retrospectively evaluate high-spatial-resolution signal enhancement ratio (SER) imaging for the prediction of disease recurrence in patients with breast cancer who underwent preoperative magnetic resonance (MR) imaging.

Materials and Methods: This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. From 1995 to 2002, gadolinium-enhanced MR imaging data were acquired with a three time point high-resolution method in women undergoing neoadjuvant therapy for invasive breast cancers. Forty-eight women (mean age, 49.1 years; range, 29.7–72.4 years) were divided into recurrence-free or recurrence groups. Volume measurements were tabulated for SER values between set ranges; cutoff criteria were defined to predict disease recurrence after surgery. Wilcoxon rank sum tests and the multivariate Cox proportional hazards regression model were used for evaluation.

Results: Breast tumor volume calculated from the number of voxels with SER values above a threshold corresponding to the upper limit of mean redistribution rate constant in benign tumors (0.88 minutes–1) and the volume of cancerous breast tissue infiltrating into the parenchyma were important predictors of disease recurrence. Seventy-five percent of patients with recurrence and 100% of deceased patients were identified as being at high risk for recurrence. Thirty percent of patients with recurrence and 67% of deceased patients were identified as having high risk before chemotherapy. No patients in the recurrence-free group were misidentified as likely to have recurrence. All three prechemotherapy parameters (total tumor volume, tumor volumes with high and low SER) and the postchemotherapy tumor volume with high SER were significantly different between the two groups. The multivariate Cox proportional hazards regression showed that, of the three prechemotherapy covariates, only the low SER and high SER tumor volumes (P = .017 and .049, respectively) were significant and independent predictors of tumor recurrence. Tumor volume with high SER was the only significant postchemotherapy covariate predictor (P = .038).

Conclusion: High-spatial-resolution SER imaging may improve prediction for patients at high risk for disease recurrence and death.

Supplemental material: http://radiology.rsnajnls.org/cgi/content/full/248/1/79/DC1

© RSNA, 2008




This article has been cited by other articles:


Home page
JNMHome page
J. H. Lee, E. L. Rosen, and D. A. Mankoff
The Role of Radiotracer Imaging in the Diagnosis and Management of Patients with Breast Cancer: Part 2--Response to Therapy, Other Indications, and Future Directions
J. Nucl. Med., May 1, 2009; 50(5): 738 - 748.
[Abstract] [Full Text] [PDF]