Literature DB >> 25360603

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

Xia Li1, Richard G Abramson, Lori R Arlinghaus, Hakmook Kang, Anuradha Bapsi Chakravarthy, Vandana G Abramson, Jaime Farley, Ingrid A Mayer, Mark C Kelley, Ingrid M Meszoely, Julie Means-Powell, Ana M Grau, Melinda Sanders, Thomas E Yankeelov.   

Abstract

OBJECTIVES: The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer.
MATERIALS AND METHODS: Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC.
RESULTS: The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24).
CONCLUSIONS: The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

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Year:  2015        PMID: 25360603      PMCID: PMC4471951          DOI: 10.1097/RLI.0000000000000100

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  21 in total

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Journal:  Breast Cancer Res Treat       Date:  2012-04-04       Impact factor: 4.872

2.  Multiparametric imaging of tumor response to therapy.

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3.  Diffuse optical spectroscopic imaging correlates with final pathological response in breast cancer neoadjuvant chemotherapy.

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4.  Diffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.

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5.  Diffusion-weighted imaging in evaluating the response to neoadjuvant breast cancer treatment.

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7.  A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer.

Authors:  Xia Li; E Brian Welch; Lori R Arlinghaus; A Bapsi Chakravarthy; Lei Xu; Jaime Farley; Mary E Loveless; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie A Means-Powell; Vandana G Abramson; Ana M Grau; John C Gore; Thomas E Yankeelov
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8.  Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.

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9.  Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer.

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10.  Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging.

Authors:  A Fangberget; L B Nilsen; K H Hole; M M Holmen; O Engebraaten; B Naume; H-J Smith; D R Olsen; T Seierstad
Journal:  Eur Radiol       Date:  2010-12-03       Impact factor: 5.315

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  61 in total

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Review 5.  Translating preclinical MRI methods to clinical oncology.

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7.  Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

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10.  The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains.

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