Matthias Dietzel1, Clemens Kaiser2, Katja Pinker3,4, Evelyn Wenkel1, Matthias Hammon1, Michael Uder1, Barbara Bennani Baiti4, Paola Clauser4, Rüdiger Schulz-Wendtland1, Pascal Baltzer4. 1. Department of Radiology, University Hospital Erlangen-Nürnberg, Erlangen, Germany. 2. Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim, Germany. 3. Department of Radiology, Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA. 4. Medical University of Vienna, Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Vienna, Austria.
Abstract
BACKGROUND: 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). METHODS: 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 (ΔTV and ΔTD). 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). RESULTS: 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: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV). CONCLUSION: Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.
BACKGROUND: 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). METHODS: 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 (ΔTV and ΔTD). 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). RESULTS: 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: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV). CONCLUSION: Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.
Entities:
Keywords:
Breast MRI; Imaging biomarkers; Primary systemic chemotherapy; Therapy response
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