Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Reig, Beatriu; Heacock, Laura; Lewin, Alan A.; Cho, Nariya; Moy, Linda

doi:10.1002/jmri.27145

Cited by 36 publications

(22 citation statements)

References 134 publications

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“…The targeted HER2 + BC treatment regimen and new improved TNBC chemotherapy regimens significantly improved the pathology complete response (pCR) rate. However, the response to preoperative chemotherapy is variable, and complete tumor regression, confirmed by histopathology as pCR, occurs in an average of 19% of patients (range 0.3–50.3%, depending on the immunohistochemical subtype of BC) [ 4 , 5 ]. Approximately 20–30% of BCs remain insensitive to NAC, and chemotherapy delays the necessary surgical treatment, increases the risk of metastasis, and may contribute to side effects [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the response to preoperative chemotherapy is variable, and complete tumor regression, confirmed by histopathology as pCR, occurs in an average of 19% of patients (range 0.3–50.3%, depending on the immunohistochemical subtype of BC) [ 4 , 5 ]. Approximately 20–30% of BCs remain insensitive to NAC, and chemotherapy delays the necessary surgical treatment, increases the risk of metastasis, and may contribute to side effects [ 3 , 4 , 5 ]. In addition, the largest group is that of partial responders, which is significantly heterogeneous, and as a result, the prediction of the precise rate of response is difficult.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Dobruch‐Sobczak

Piotrzkowska-Wróblewska

Karwat

et al. 2021

Cancers

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The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Dobruch‐Sobczak

Piotrzkowska-Wróblewska

Karwat

et al. 2021

Cancers

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“…However, lack of standardisation and susceptibility to artefacts and image distortion remains problematic. When analysed in combination with other parameters measured by MRF, a more accurate evaluation of tumour progression might be achieved [ 92 , 96 ]. With more quantitative information being collected by MRF, personalised medicine and treatment plans can potentially be developed for individual cancer patients [ 97 ].…”

Section: Potential Future Developments For Cancer Managementmentioning

confidence: 99%

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Ding

Velasco

et al. 2021

Cancers

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Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians’ judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

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“…A systematic review demonstrated that MRI-based radiomics achieved overall promising performance in NACT response prediction ( Granzier et al, 2019 ) and residual tumor size evaluation ( Kim et al, 2018a ), while a DCE-MRI-based predictive model achieved better accuracy than a model using other parametric images ( Fowler et al, 2017 ). Radiomics features derived from the pretreatment MRI have been used for predicting response to NACT for breast cancer ( Uematsu et al, 2010 ; Braman et al, 2017 ; Santamaría et al, 2017 ; Reig et al, 2020 ). Our previous study used DCE-MRI to identify and validate predictive imaging biomarkers for NACT using two datasets with different imaging protocols for training and testing ( Fan et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

Fan

Chen

You

et al. 2021

Front. Mol. Biosci.

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Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.

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Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Cited by 36 publications

References 134 publications

Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

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