4.7 Article

Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer by Using MR Imaging and Quantitative 1H MR Spectroscopy

Journal

RADIOLOGY
Volume 251, Issue 3, Pages 653-662

Publisher

RADIOLOGICAL SOC NORTH AMERICA
DOI: 10.1148/radiol.2512080553

Keywords

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Funding

  1. National Institutes of Health [NIH/NCI R01 CA90437, CA127967]

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Purpose: To compare changes in the concentration of choline-containing compounds (tCho) and in tumor size at follow-up after neoadjuvant chemotherapy (NAC) between patients who achieved pathologic complete response (pCR) and those who did not (non-pCR). Materials and Methods: This study was approved by the institutional review board and was compliant with HIPAA; each patient gave informed consent. Thirty-five patients (mean age, 48 years +/- 11 [standard deviation]; range, 29-75 years) with breast cancer were included. Treatment included doxorubicin and cyclophosphamide followed by a taxane-based regimen. Changes in tCho and tumor size in pCR versus non-pCR groups were compared by using the two-way Mann-Whitney nonparametric test. Receiver operating characteristic (ROC) analysis was performed to differentiate between them and the area under the ROC curve (AUC) was compared. Results: In the pCR group, the tCho level change was greater compared with change in tumor size (P = .003 at first followup, P = .01 at second follow-up), but they were not significantly different in the non-pCR group. Changes in tumor size and tCho level at the first follow-up study were not significantly different between the pCR and non-pCR groups but reached significance at the second follow-up. In ROC analysis, the magnetic resonance (MR) imaging and MR spectroscopic parameters had AUCs of 0.65-0.68 at first follow-up; at second follow-up, AUC for change in tumor size was 0.9, AUC for change in tCho was 0.73. Conclusion: Patients who show greater reduction in tCho compared with changes in tumor size are more likely to achieve pCR. The change in tumor size halfway through therapy was the most accurate predictor of pCR. (C) RSNA, 2009

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