Rationale and Objectives
To compare the value of radiomics and diameter% based on pre- and early-treatment
dynamic enhanced MR (DCE-MRI) of the breast in predicting response to neoadjuvant
therapy (NAT) in breast cancer and to construct a tool for early noninvasive prediction
of NAT outcomes.
Materials and Methods
Retrospective analysis of clinical and imaging data of 142 patients with primary invasive
breast cancer who underwent DCE-MRI before and after two cycles of NAT at our institution.
Enroled patients were randomly assigned in a 7:3 ratio to the training group and the
test group. Patients were divided into pathological complete response (pCR) and non-pathological
complete response groups based on surgical pathology findings after NAT. The maximum
diameter relative regression values (Diameter%) before and after treatment were calculated
and the conventional imaging Diameter% model was constructed. Based on pre- and early-NAT
DCE-MRI, the optimal features of pre-NAT, early-NAT, and delta radiomics were screened
using redundancy analysis, least absolute shrinkage, and selection operator methods
to construct the corresponding radiomics model and calculate the Radscores. Indicators
that were statistically significant in the univariate analysis of clinical data were
further screened by stepwise regression and combined with Radscores to construct the
fusion model. All models were evaluated and compared.
Results
In the test set, the area under the curve (AUC) of the delta radiomics model (0.87)
was higher than that of the pre-NAT, early-NAT radiomics models (0.57, 0.78) and the
Diameter% model (0.83). The fusion model had the best efficacy in predicting pCR after
NAT, with AUCs of 0.91 in the training and test sets. And its nomogram plot showed
that Radscore of early-NAT radiomics had the greatest weight. In the test set, the
fusion model and Delta radiomics model improved the efficacy of predicting pCR by
35.56% and 14.19%, respectively, compared to the Diameter% model (P = 0 and .039). Clinical decision curves showed the highest overall clinical benefit
for the fusion model.
Conclusion
Radiomics, especially delta and early-NAT radiomics, may be potential biomarkers for
early noninvasive prediction of NAT outcomes. And a fusion model constructed from
meaningful clinicopathological indicators combined with radiomics can effectively
predict NAT response.
Key Words
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Neoadjuvant chemotherapy, endocrine therapy, and targeted therapy for breast cancer: ASCO guideline.Ann Surg Oncol. 2022; 29: 1489-1492
- Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis.Clin Cancer Res. 2020; 26: 2838-2848
- Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy.Breast Cancer Res Treat. 2018; 170: 559-567
- Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer.Drug Des Dev Ther. 2020; 14: 2423-2433
- Imaging neoadjuvant therapy response in breast cancer.Radiology. 2017; 285: 358-375
- Breast M.R.I.: guidelines from the European Society of Breast Imaging.Eur Radiol. 2008; 18: 1307-1318
- NCCN Guidelines® Insights: Breast Cancer, Version 4.2021.J Natl Compr Cancer Netw. 2021; 19: 484-493
- Abbreviated MRI of the breast: does it provide value?.J Magn Reson Imaging. 2019; 49: e85-e100
- RECIST 1.1 - standardisation and disease-specific adaptations: perspectives from the RECIST Working Group.Eur J Cancer. 2016; 62: 138-145
- MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.Breast Cancer Res. 2018; 20: 34
- Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer. 2012; 48: 441-446
- Treatment response prediction using ultrasound-based pre-, post-early, and delta radiomics in neoadjuvant chemotherapy in breast cancer.Front Oncol. 2022; 12748008
- Estimating the benefits of therapy for early-stage breast cancer: the St. Gallen International Consensus Guidelines for the primary therapy of early breast cancer 2019.Ann Oncol. 2019; 30: 1541-1557
- CSCO breast cancer guideline: precise, economical and oriental.Sci China Life Sci. 2020; 63: 1410-1412
- Eighth Edition of the AJCC Cancer Staging Manual: Breast Cancer.Ann Surg Oncol. 2018; 25: 1783-1785
- A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival.The Breast. 2003; 12: 320-327
- 3D Slicer as an image computing platform for the Quantitative Imaging Network.Magn Reson Imaging. 2012; 30: 1323-1341
- Radiomics features based on automatic segmented MRI images: prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy.Eur J Radiol. 2022; 146110095
- Introduction to radiomics.J Nucl Med. 2020; 61: 488-495
- Radiomics based on dynamic contrast-enhanced MRI to early predict pathologic complete response in breast cancer patients treated with neoadjuvant therapy.Acad Radiol. 2022; S1076-6332 ([published online ahead of print, 2022 Dec 21])00611-0https://doi.org/10.1016/j.acra.2022.11.006
- Selection of important variables and determination of functional form for continuous predictors in multivariable model building.Stat Med. 2007; 26: 5512-5528
- Efficacy evaluation of neoadjuvant chemotherapy in breast cancer by MRI.Contrast Media Mol Imaging. 2022; 20224542288
- Expert consensus on neoadjuvant therapy for breast cancer in China (2019 Edition).Chin J Cancer. 2019; 29: 390-400
- Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer.Eur Radiol. 2017; 27: 1901-1911
- Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy - results from ACRIN 6657/I-SPY TRIAL.Radiology. 2012; 263: 663-672
- Robustness evaluation of a deep learning model on sagittal and axial breast DCE-MRIs to predict pathological complete response to neoadjuvant chemotherapy.J Pers Med. 2022; 12: 953
- Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.Magn Reson Imaging. 2013; 31: 1457-1464
- Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer.Clin Cancer Res. 2008; 14: 6580-6589
- Meta-analysis of magnetic resonance imaging in detecting residual breast cancer after neoadjuvant therapy.J Natl Cancer Inst. 2013; 105: 321-333
- Mutant allele frequency-based intra-tumoral genetic heterogeneity related to the tumor shrinkage mode after neoadjuvant chemotherapy in breast cancer patients.Front Med. 2021; 8651904
- Pattern of tumor shrinkage during neoadjuvant chemotherapy is associated with prognosis in low-grade luminal early breast cancer.Radiology. 2018; 286: 49-57
- Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients.Investig Radiol. 2019; 54: 110-117
- Neoadjuvant chemotherapy with MRI monitoring for breast cancer.Br J Surg. 2017; 104: 1177-1187
- MRI radiomics for assessment of molecular subtype, pathological complete response, and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy.Acad Radiol. 2022; 29: S145-S154
- Differences between ipsilateral and contralateral early parenchymal enhancement kinetics predict response of breast cancer to neoadjuvant therapy.Acad Radiol. 2022; 29: 1469-1479
- MRI-based radiomics for the diagnosis of triple-negative breast cancer: a meta-analysis.Clin Radiol. 2022; 77: 655-663
- MRI-based radiomics analysis for the pretreatment prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients: a multicenter study.Cancers. 2021; 13: 2447
- Delta-radiomics increases multicentre reproducibility: a phantom study.Med Oncol. 2020; 37: 38
- MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.Radiother Oncol. 2021; 164: 73-82
- A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.Breast Cancer Res. 2020; 22: 57
- Delta-radiomics based on dynamic contrast-enhanced MRI predicts pathologic complete response in breast cancer patients treated with neoadjuvant chemotherapy.Cancers. 2022; 14: 3515
- Role of texture analysis in breast MRI as a cancer biomarker: a review.J Magn Reson Imaging. 2019; 49: 927-938
- Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes.Radiology. 2017; 282: 665-675
- Association between breast cancer's prognostic factors and 3D textural features of non-contrast-enhanced T(1) weighted breast MRI.Br J Radiol. 2022; 9520210702
- Breast cancer treatment: a review.JAMA. 2019; 321: 288-300
- Neoadjuvant therapy for HER2-positive breast cancer.Rev Recent Clin Trials. 2017; 12: 81-92
- Predictive role of molecular subtypes in response to neoadjuvant chemotherapy in breast cancer patients in Northeast China.Asian Pac J Cancer Prev. 2011; 12: 2411-2417
- A deep look into radiomics.Radiol Med. 2021; 126: 1296-1311
Article info
Publication history
Published online: May 09, 2023
Accepted:
April 11,
2023
Received in revised form:
April 9,
2023
Received:
March 4,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
