ArticleBreast care (Basel, Switzerland)2017
Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy.
Article in Breast care (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review.OncoTargets and therapy · 2020Review
- Is the whole larger than the sum of the parts? Integrated PET/MRI as a tool for response prediction.European journal of nuclear medicine and molecular imaging · 2018Article
- Advanced Imaging for Precision Medicine in Breast Cancer: From Morphology to Function.Breast care (Basel, Switzerland) · 2017Article
- Dynamic Contrast-enhanced and Diffusion-weighted Magnetic Resonance Imaging for Response Evaluation After Single-Dose Ablative Neoadjuvant Partial Breast Irradiation.Advances in radiation oncologyArticle
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe 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).
methodsBreast 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).
resultsThere 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).
conclusionSemi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.
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