Breast MRI Radiomics in Predicting Response to Neoadjuvant Chemotherapy: A Systematic Review and Meta-Analysis
DOI:
https://doi.org/10.48048/tis.2026.12179Keywords:
Breast cancer, Diagnostic test accuracy, Radiomics, Cemas menyeluruh, Breast cancer, Diagnostic test accuracy, Radiomics, Magnetic resonance imagingAbstract
Introduction: Breast magnetic resonance imaging (MRI) has become a well-established tool for evaluating response to neoadjuvant chemotherapy for breast cancer; however, the results are limited by natural subjectivity and possible human error. This study evaluated the diagnostic performance (sensitivity, specificity, accuracy, and AUC value) of pathological complete response to neoadjuvant chemotherapy using the MRI imaging approach for radiomic texture analysis Materials and Methods: This systematic review and meta-analysis was conducted according to PRISMA guidelines. Four databases were searched until March 2025 and eligible studies were extracted by three readers. The selected articles were divided into 3 groups: Radiomics, clinical models, and radiomics + clinical models. Those groups were compared on each sensitivity, specificity, accuracy, and AUC value using a random-effects model. QUADAS-2, I² statistic, Cochran’s Q I 2 statistics, and Egger’s regression test for publication bias, and meta-regression were used. Results: 11 studies were included in this analysis. The pooled sensitivity, specificity, accuracy, and area under the curve (AUC) across all models were 0.76 (95% CI: 0.69 - 0.82), 0.72 (95% CI: 0.63 - 0.78), 0.77 (95% CI: 0.70 - 0.83), and 0.76 (95% CI: 0.72 - 0.81), respectively. The combined Radiomics + Clinical model demonstrated the highest diagnostic performance with a sensitivity of 0.79 (95% CI: 0.65 - 0.89), specificity of 0.75 (95% CI: 0.69 - 0.81), accuracy of 0.83 (95% CI: 0.68 - 0.91), and AUC of 0.81 (95% CI: 0.74 - 0.87). Conclusions: Radiomic MRI can enhance the predictive performance of neoadjuvant chemotherapy for breast cancer.
HIGHLIGHTS
- MRI-based radiomics enhances the predictive performance of response to neoadjuvant chemotherapy in breast cancer.
- MRI-based radiomics model achieved the highest specificity among the evaluated models.
- The combined MRI-based radiomics–clinical model demonstrated the highest diagnostic performance for predicting response to neoadjuvant chemotherapy.
- Radiomics supports precision oncology by enabling tailored breast cancer treatment strategies, potentially reducing overtreatment and improving patient quality of life.
GRAPHICAL ABSTRACT
Downloads
References
F Bray, M Laversanne, H Sung, J Ferlay, RL Siegel, I Soerjomataram and A Jemal. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 2024; 74(3), 229-263.
M Antonini, A Mattar, TM Pereira, LL Oliveira, MD Teixeira, AG Amorim, O Ferraro, LC de Oliveira, MNM Ramos, FP Cavalcante, F Zerwes, M Madeira, LR Soares, EC Millen, AL Frasson, FP Brenelli, G Facinak, R Fenilel, R Arakelian, R de Freitas Júniorn, MB dos Santos, HL Coutoo and LH Gebrim. Pathologic complete response and breast cancer survival post-neoadjuvant chemotherapy: A systematic review and meta-analysis of real-world data. Heliyon 2025; 11(10), e43069.
LM Spring, G Fell, A Arfe, C Sharma, R Greenup, KL Reynolds, BL Smith, B Alexander, SJ Isakoff, G Parmigiani, L Trippa and A Bardia. Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: A comprehensive meta-analysis. Clinical Cancer Research 2020; 26(12), 2838-2848.
W Haque, V Verma, S Hatch, VS Klimberg, EB Butler and BS Teh. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy. Breast Cancer Research and Treatment 2018; 170(3), 559-567.
T Bian, Z Wu, Q Lin, H Wang, Y Ge, S Duan, G Fu, C Cui and X Su. Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer. The British Journal of Radiology 2020; 93(1115), 20200287.
S Radhakrishna, S Agarwal, PM Parikh, K Kaur, S Panwar, S Sharma, A Dey, KK Saxena, M Chandra and S Sud. Role of magnetic resonance imaging in breast cancer management. South Asian Journal of Cancer 2018; 7(2), 069-071.
WJJ Weber, MS Jochelson, A Eaton, EC Zabor, AV Barrio, ML Gemignani, M Pilewskie, KJV Zee, M Morrow and M El-Tamer. MRI and prediction of pathologic complete response in the breast and axilla after neoadjuvant chemotherapy for breast cancer. Journal of the American College of Surgeons 2017; 225(6), 740-746.
F Dong, J Li, J Wang and X Yang. Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis. PLoS One 2024; 19(12), e0314653.
OJPM O’Donn, SA Gasior, MG Davey, E O’Malley, AJ Lowery, J McGarry, AM O’Connell, MJ Kerin abd P McCarthy. The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: A systematic review and network meta-analysis. European Journal of Radiology 2022; 157, 110561.
JE van Timmeren, D Cester, S Tanadini-Lang, H Alkadhi and B Baessler. Radiomics in medical imaging - “how-to” guide and critical reflection. Insights into Imaging 2020; 11(1), 91.
A Ibrahim, S Primakov, M Beuque, HC Woodruff, I Halilaj, G Wu, T Refaee, R Granzier, Y Widaatalla, R Hustinx, FM Mottaghy and P Lambin. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods 2021; 188, 20-29.
R Hajri, C Aboudaram, N Lassau, T Assi, L Antoun, JM Ribeiro, M Lacroix-Triki, S Ammari and C Balleyguier. Prediction of breast cancer response to neoadjuvant therapy with machine learning: A clinical, MRI-qualitative, and radiomics approach. Life 2025; 15(8), 1165.
K Drukker, A Edwards, C Doyle, J Papaioannou, K Kulkarni and ML Giger. Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. Journal of Medical Imaging 2019; 6(3), 1.
F Pesapane, GM Agazzi, A Rotili, F Ferrari, A Cardillo, S Penco, V Dominelli, O D’Ecclesiis, S Vignati, S Raimondi, A Bozzini, M Pizzamiglio, G Petralia, L Nicosia and E Cassano. Prediction of the pathological response to neoadjuvant chemotherapy in breast cancer patients with MRI-radiomics: A Systematic review and meta-analysis. Current Problems in Cancer 2022; 46(5), 100883.
ZJ Zhang, Q Wu, P Lei, X Zhu and B Li. MRI-based radiomics models for early predicting pathological response to neoadjuvant chemotherapy in triple-negative breast cancer: A systematic review and meta-analysis. Journal of Applied Clinical Medical Physics 2025; 26(10), e70296.
Z Liu, Z Li, J Qu, R Zhang, X Zhou, L Li, K Sun, Z Tang, H Jiang, H Li, Q Xiong, Y Ding, X Zhao, K Wang, Z Liu and J Tian. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: A multicenter study. Clinical Cancer Research 2019; 25(12), 3538-3547.
Q Xiong, X Zhou, Z Liu, C Lei, C Yang, M Yang, L Zhang, T Zhu, X Zhuang, C Liang, Z Liu, J Tian and K Wang. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy. Clinical and Translational Oncology 2020; 22(1), 50-59.
RWY Granzier, A Ibrahim, SP Primakov, S Samiei, TJA van Nijnatten, M de Boer, EM Heuts, FJ Hulsmans, A Chatterjee, P Lambin, MBI Lobbes, HC Woodruff and ML Smidt. 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(10), 2447.
EJ Sutton, N Onishi, DA Fehr, BZ Dashevsky, M Sadinski, K Pinker, DF Martinez, E Brogi, L Braunstein, P Razavi, M El-Tamer, V Sacchini, JO Deasy, EA Morris and H Veeraraghavan. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Research 2020; 22(1), 57.
L Kwak, C Santa-Maria, P Di Carlo, LA Mullen, KS Myers, E Oluyemi, B Panigrahi, J Rossi and EB Ambinder. Can breast MRI predict pathologic response following neoadjuvant chemotherapy for breast cancer? A retrospective cohort study. Clinical Imaging 2023; 101, 105-112.
HJ Eom, HH Kim, HJ Kim, WJ Choi, EY Chae, HJ Shin and JH Cha. Comparison of diffusion-weighted and contrast-enhanced MRI for monitoring response to neoadjuvant therapy in breast cancer. European Radiology 2025; 35, 8217-8227.
X Liu, A Yang, M Cao, Q Zhang and Y Cao. Research on predicting pathological complete response in breast cancer following neoadjuvant chemotherapy using a multiparametric MRI radiomics model. Journal of Radiation Research and Applied Sciences 2025; 18(3), 101769.
Y Yu, Z Wang, Q Wang, X Su, Z Li, R Wang, T Guo, W Gao, H Wang and B Zhang. Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients. Frontiers in Oncology 2024; 13, 1249339.
Y Li, Y Fan, D Xu, Y Li, Z Zhong, H Pan, B Huang, X Xie, Y Yang and B Liu. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer. Frontiers in Oncology 2023; 12, 1041142
Q Zeng, F Xiong, L Liu, L Zhong, F Cai and X Zeng. Radiomics based on DCE-MRI for predicting response to neoadjuvant therapy in breast cancer. Academic Radiology 2023; 30, S38-S49.
C Li, N Lu, Z He, Y Tan, Y Liu, Y Chen, Z Wu, J Liu, W Ren, L Mao, Y Yu, C Xie and H Yao. A noninvasive tool based on magnetic resonance imaging radiomics for the preoperative prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. Annals of surgical Oncology 2022; 29(12), 7685-7693.
P McAnena, BM Moloney, R Browne, N O’Halloran, L Walsh, S Walsh, D Sheppard, KJ Sweeney, MJ Kerin and AJ Lowery. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer. BMC Medical Imaging 2022; 22(1), 225.
X Zhang, X Teng, J Zhang, Q Lai and J Cai. Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity. Breast Cancer Researc 2024; 26(1), 77.
G Zheng, J Peng, Z Shu, H Jin, L Han, Z Yuan, X Qin, J Hou, X He and X Gong. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: Use of MRI radiomics data from three regions with multiple machine learning algorithms. Journal of Cancer Research and Clinical Oncology 2024; 150(3), 147.
RM Mohamed, B Panthi, BE Adrada, M Boge, RP Candelaria, H Chen, MS Guirguis, KK Hunt, L Huo, KP Hwang, A Korkut, JK Litton, TW Moseley, S Pashapoor, MM Patel, B Reed, ME Scoggins, JB Son, A Thompson, …, GM Rauch. Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Scientific Reports 2024; 14, 16073.
H Lee, JH Lee, JE Lee, YM Na, MH Park, JS Lee and HS Lim. Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI. Scientific Reports 2024; 14(1), 21691.
Y Chen, Y Qi and K Wang. Neoadjuvant chemotherapy for breast cancer: An evaluation of its efficacy and research progress. Frontiers in Oncology 2023; 13, 1169010
C Sekine, N Uchiyama, C Watase, T Murata, S Shiino, K Jimbo, E Iwamoto, S Takayama, H Kurihara, K Satomi, M Yoshida, T Kinoshita and A Suto. Preliminary experiences of PET/MRI in predicting complete response in patients with breast cancer treated with neoadjuvant chemotherapy. Molecular and Clinical Oncology 2021; 16(2), 51.
BZ Bodalal, I Wamelink, S Trebeschi and RGH Beets-Tan. Radiomics in immuno-oncology. Immuno-Oncology and Technology 2021; 9, 100028.
V Romeo, G Accardo, T Perillo, L Basso, N Garbino, E Nicolai, S Maurea and M Salvatore. Assessment and prediction of response to neoadjuvant chemotherapy in breast cancer: A comparison of imaging modalities and future perspectives. Cancers 2021; 13(14), 3521.
PD Moyya, M Asaithambi and AK Ramaniharan. Extraction of radiomic features from breast DCE-MRI Responds to pathological changes in patients during neoadjuvant chemotherapy treatment. In: Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST), Strasbourg, France. 2019.
Y Yu, R Chen, J Yi, K Huang, X Yu, J Zhang and C Song. Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data. The Breast 2024; 77, 103786.
X Dong, J Meng, J Xing, S Jia, X Li and S Wu. Predicting Axillary lymph node metastasis in young onset breast cancer: A clinical-radiomics nomogram based on DCE-MRI. Breast Cancer: Targets and Therapy 2025; 17, 103-113.
W Shi, Y Su, R Zhang, W Xia, Z Lian, N Mao, Y Wang, A Zhang, X Gao and Y Zhang. Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor. Cancer Imaging 2024; 24(1), 122.
B Goorts, TJA van Nijnatten, L de Munck, M Moossdorff, EM Heuts, M de Boer, MBI Lobbes and ML Smidt. Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in breast cancer patients. Breast Cancer Research and Treatment 2017; 163(1), 83-91.
A Crosbie, TK Le, Y Zhang, R Das, F Ades, C Davis and A Gogate. Neoadjuvant treatment and survival outcomes by pathologic complete response in HER2-negative early breast cancers. Future Oncology 2023; 19(3), 229-244.
X Wu, Y Xia, X Lou, K Huang, L Wu and C Gao. Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights. Breast Cancer Research 2025; 27(1), 29.
YJ Qi, GH Su, C You, X Zhang, Y Xiao, YZ Jiang and ZM Shao. Radiomics in breast cancer: Current advances and future directions. Cell Reports Medicine 2024; 5(9), 101719.
M Fan, X Wu, J Yu, Y Liu, K Wang, T Xue, T Zeng, S Chen and L Li. Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer. Frontiers in Oncology 2023; 13, 1057841
Z Shi, X Huang, Z Cheng, Z Xu, H Lin, C Liu, X Chen, C Liu, C Liang, C Lu, Y Cui, C Han, J Qu, J Shen and Z Liu. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer. Radiology 2023; 308(1), e222830
T Zhang, L Zhao, T Cui, Y Zhou, P Li, C Luo, J Wei and H Hu. Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: A comprehensive review. Journal of Translational Medicine 2025; 23(1), 681.
DM Ye, HT Wang and T Yu. The application of radiomics in breast MRI: A review. Technology in Cancer Research & Treatment 2020; 19, 1533033820916191.
Published
Issue
Section
License
Copyright (c) 2025 Walailak University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
