Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer
Authors:
- 10 March 2022
- journal article
- research article
- Published by Springer Nature in European Radiology
- Vol. 32 (8) , 5759-5772
Abstract
Objectives To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. Methods From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. Results In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller–Payne G4-5) compared with non-MHR (Miller–Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. Conclusions Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. Key Points • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.Keywords
Funding Information
- National Financial Appropriation Research Project (2017YFC1309100)
- National Scientific Foundation of China (81971695)
- Set Sail Project of the First Affiliated Hospital of China Medical University (36028)
This publication has 46 references indexed in Scilit:
- DCE‐MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findingsMagnetic Resonance in Medicine, 2013
- Neoadjuvant treatment of breast cancerAnnals of Oncology, 2012
- Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY TRIALRadiology, 2012
- Quantitative Analysis of Clinical Dynamic Contrast-enhanced MR Imaging for Evaluating Treatment Response in Human Breast CancerRadiology, 2010
- American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast CancerJournal of Clinical Oncology, 2010
- Quantified Tumor T1 Is a Generic Early-Response Imaging Biomarker for Chemotherapy Reflecting Cell ViabilityClinical Cancer Research, 2010
- New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)European Journal of Cancer, 2009
- Breast MRI: guidelines from the European Society of Breast ImagingEuropean Radiology, 2008
- Lesion T2 relaxation times and volumes predict the response of malignant breast lesions to neoadjuvant chemotherapyMagnetic Resonance Imaging, 2008
- Decision Curve Analysis: A Novel Method for Evaluating Prediction ModelsMedical Decision Making, 2006