Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Prediction Of Chemoresistance In Women Undergoing Neo-Adjuvant Chemotherapy For Locally Advanced Breast Cancer: Volumetric Analysis Of First-Order Textural Features Extracted From Multiparametric MRI

M. Panzeri, C. Losio, A. Della Corte, E. Venturini, A. Ambrosi, P. Panizza, F. de Cobelli
Published 2018 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
Purpose To assess correlations between volumetric first-order texture parameters on baseline MRI and pathological response after neoadjuvant chemotherapy (NAC) for locally advanced breast cancer (BC). Materials and Methods 69 patients with locally advanced BC candidate to neoadjuvant chemotherapy underwent MRI within 4 weeks from the start of therapeutic regimen. T2, DWI, and DCE sequences were analyzed and maps were generated for Apparent Diffusion Coefficient (ADC), T2 signal intensity, and the following dynamic parameters: k-trans, peak enhancement, area under curve (AUC), time to maximal enhancement (TME), wash-in rate, and washout rate. Volumetric analysis of these parameters was performed, yielding a histogram analysis including first-order texture kinetics (percentiles, maximum value, minimum value, range, standard deviation, mean, median, mode, skewness, and kurtosis). Finally, correlations between these values and response to NAC (evaluated on the surgical specimen according to RECIST 1.1 criteria) were assessed. Results Out of 69 tumors, 33 (47.8%) achieved complete pathological response, 26 (37.7%) partial response, and 10 (14.5%) no response. Higher levels of AUCmax (p value = 0.0338), AUCrange (p value = 0.0311), and TME75 (p value = 0.0452) and lower levels of washout10 (p value = 0.0417), washout20 (p value = 0.0138), washout25 (p value = 0.0114), and washout30 (p value = 0.05) were predictive of noncomplete response. Conclusion Histogram-derived texture analysis of MRI images allows finding quantitative parameters predictive of nonresponse to NAC in women affected by locally advanced BC.
This paper references
Texture analysis in assessment and prediction of chemotherapy response in breast cancer
A. Ahmed (2013)
Neoadjuvant therapy for earlystage breast cancer: Current practice, controversies, and future directions
C. A. Santa-Maria (2015)
Apparent diffusion coefficient modifications in assessing gastro-oesophageal cancer response to neoadjuvant treatment: comparison with tumour regression grade at histology
F. Cobelli (2013)
Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.
R. Abramson (2013)
Heterogeneity in intratumoral regions with rapid gadolinium washout correlates Contrast Media & Molecular Imaging 7 with estrogen receptor status and nodal metastasis
B. Chaudhury (2015)
Improving tumour heterogeneityMRI assessment with histograms
N. Just (2014)
Role of the Apparent Diffusion Coefficient in the Prediction of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer.
E. Bufi (2015)
Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI.
M. L. Marinovich (2012)
Heterogeneity in intratumoral regions with rapid gadolinium washout correlates nodal metastasis
B Chaudhury (2015)
Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis
B. Chaudhury (2015)
Improving tumour heterogeneity MRI assessment with histograms
N. Just (2014)
3rd ESO–ESMO International Consensus Guidelines for Advanced Breast Cancer (ABC 3)
F. Cardoso (2017)
Intratumor partitioning and texture analysis of dynamic contrast‐enhanced (DCE)‐MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy
J. Wu (2016)
Texture analysis on MR images helps predicting non-response to NAC in breast cancer
N. Michoux (2015)
Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients
M. Pickles (2016)
Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes.
G. von Minckwitz (2012)

This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar