Dynamic contrast‐enhanced magnetic resonance imaging and invasive breast cancer: Primary lesion kinetics correlated with axillary lymph node extracapsular extension

Loiselle, Christopher; Eby, Peter R.; Peacock, Sue; Kim, Janice N.; Lehman, Constance D.

doi:10.1002/jmri.22389

Cited by 9 publications

(11 citation statements)

References 34 publications

(45 reference statements)

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“…The selection frequency of the features in the leave-one-out cross validation indicates the importance of considering the phenotype categories in the prediction of breast cancer recurrence, as they are consistently selected by our model in all cross-validation loops. In addition, the selection frequency of the heterogeneity kinetic statistic features largely outperforms the standard aggregate kinetic measures (9,31–33), such as maximum peak enhancement, hot spot signal enhancement ratio, and features from the characteristic kinetic curve, which suggests that more comprehensive characterization of spatial tumor heterogeneity from imaging can ultimately result in better prognostic imaging markers.…”

Section: Discussionmentioning

confidence: 99%

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Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles

Ashraf¹,

Daye²,

Gavenonis³

et al. 2014

Radiology

140

147

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Purpose To present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles. Materials and Methods The authors retrospectively analyzed dynamic contrast material–enhanced (DCE) magnetic resonance (MR) images of the breast in 56 women (mean age, 55.6 years; age range, 37–74 years) diagnosed with estrogen receptor–positive breast cancer between 2005 and 2010. The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Primary tumors were assayed with a validated gene expression assay that provides a score for the likelihood of recurrence. A multiparametric imaging phenotype vector was extracted for each tumor by using quantitative morphologic, kinetic, and spatial heterogeneity features. Multivariate linear regression was performed to test associations between DCE MR imaging features and recurrence likelihood. To identify intrinsic imaging phenotypes, hierarchical clustering was performed on the extracted feature vectors. Multivariate logistic regression was used to classify tumors at high versus low or medium risk of recurrence. To determine the additional value of intrinsic phenotypes, the phenotype category was tested as an additional variable. Receiver operating characteristic analysis and the area under the receiver operating characteristic curve (Az) were used to assess classification performance. Results There was a moderate correlation (r = 0.71, R2 = 0.50, P < .001) between DCE MR imaging features and the recurrence score. DCE MR imaging features were predictive of recurrence risk as determined by the surrogate assay, with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected, with two including only low- and medium-risk tumors. When the phenotype category was used as an additional variable, the Az increased to 0.82 (P < .01). Conclusion Intrinsic imaging phenotypes exist for breast cancer tumors and correlate with recurrence likelihood as determined with gene expression profiling. These imaging biomarkers could ultimately help guide treatment decisions.

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Section: Discussionmentioning

confidence: 99%

“…The following kinetic features were extracted: maximum peak enhancement (33), peak enhancement, wash-in slope, washout slope, and time to peak (TTP) of the characteristic kinetic curve (31,32) and the hot spot signal enhancement ratio (34). …”

Section: Methodsmentioning

confidence: 99%

Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles

Ashraf¹,

Daye²,

Gavenonis³

et al. 2014

Radiology

140

147

View full text Add to dashboard Cite

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“…3,7 However, surgical interrogation of axillary nodes is considerably invasive and can result in arm stiffness, pain, decreased mobility, and lymphedema. 3,6 Additionally, with the recent publication of Z0011 trial from the American College of Surgeons Oncology Group (ACOSOG), 8 the value of a completion axillary node dissection has been questioned as there was no significant difference in survival or disease free survival in women who underwent axillary node dissection after 1-2 positive sentinel nodes versus those women who did not. A non-invasive method for accurate assessment of nodal involvement would be a significant aid in making breast cancer treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

“…However, there has been relatively less work in the literature that uses DCE-MRI for diagnostic evaluation of axillary lymph node metastasis. A representative work along this line is, 6 in which the authors use the maximum peak enhancement (MPE) of the primary lesion to predict axillary node extracapsular extension. DCE MRI kinetic features that represent the properties of contrast agent uptake by the tissue have been shown to be associated with tumor characterization.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] For post-mastectomy patients with axillary node metastasis, radiation therapy has been shown to result in improved disease free and overall survival. [4][5][6] Surgical assessment of the axilla either with axillary node dissection or sentinel node biopsy remains the gold standard for the assessment of nodal status. 3,7 However, surgical interrogation of axillary nodes is considerably invasive and can result in arm stiffness, pain, decreased mobility, and lymphedema.…”

Section: Introductionmentioning

confidence: 99%

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Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

et al. 2012

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The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from 69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve, the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope (WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic features. ANOVA analysis was done to select features that differ significantly between node positive and node negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively). These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to guide personalized treatment choices for women diagnosed with breast cancer.

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Imaging of Lymph Nodes

Barbieri

Härmä

Thoeny

2017

MRI and CT of the Female Pelvis

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Dynamic contrast‐enhanced magnetic resonance imaging and invasive breast cancer: Primary lesion kinetics correlated with axillary lymph node extracapsular extension

Cited by 9 publications

References 34 publications

Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles

Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

Imaging of Lymph Nodes

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