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Automated semi-quantitative analysis of breast MRI: potential imaging biomarker for the prediction of tissue response to neoadjuvant chemotherapy

<b><i>Background:</i></b> We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC). <b><i>Methods:</i></b> Breast magnetic reso...

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Bibliographic Details
Main Authors: Dietzel, Matthias (Author) , Kaiser, Clemens G. (Author)
Format: Article (Journal)
Language:English
Published: August 2017
In: Breast care
Year: 2017, Volume: 12, Issue: 4, Pages: 231-236
ISSN:1661-3805
DOI:10.1159/000480226
Online Access:Verlag, Volltext: http://dx.doi.org/10.1159/000480226
Verlag, Volltext: https://www-karger-com.ezproxy.medma.uni-heidelberg.de/Article/FullText/480226
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Author Notes:Matthias Dietzel, Clemens Kaiser, Katja Pinker, Evelyn Wenkel, Matthias Hammon, Michael Uder, Barbara Bennani Baiti, Paola Clauser, Rüdiger Schulz-Wendtland, Pascal Baltzer
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Summary:<b><i>Background:</i></b> We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC). <b><i>Methods:</i></b> Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (&#x0394;TV and &#x0394;TD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis). <b><i>Results:</i></b> There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: &#x0394;TD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: &#x0394;TV). <b><i>Conclusion:</i></b> Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.
Item Description:Gesehen am 04.04.2018
Physical Description:Online Resource
ISSN:1661-3805
DOI:10.1159/000480226

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