Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study

Huang, YuHong; Zhu, Teng; Zhang, XiaoLing; Li, Wei; Zheng, XingXing; Cheng, MinYi; Ji, Fei; Zhang, LiuLu; Yang, CiQiu; Wu, ZhiYong; Ye, GuoLin; Lin, Ying; Wang, Kun

doi:10.1016/j.eclinm.2023.101899

Cited by 175Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisBreast Cancer Treatment Studies

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study

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Huang, YuHong, Zhu, Teng, Zhang, XiaoLing, Li, Wei, Zheng, XingXing, Cheng, MinYi, Ji, Fei, Zhang, LiuLu +5 more·2023·eClinicalMedicine

DOI 10.1016/j.eclinm.2023.101899PMC PMC10050775

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Abstract

Background: Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods: From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings: A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation: Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding: This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

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Predicting NAC response in patients with locally advanced breast cancer based on a deep learning algorithm is feasible.
“Researchers found that predicting NAC response in patients with locally advanced breast cancer based on a deep learning algorithm is feasible (13).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [13]
The Methods (Deep learning analysis) describe a deep-learning model, and the Discussion reports that MRI-based models predicted pathological complete response to NAC in validation cohorts. However, the Study participants section specifies invasive breast cancer, not a locally advanced cohort; the best-performing model also combined radiomics and deep-learning features rather than relying on deep learning alone. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Deep learning can improve the prediction ability of NAC response prediction in breast cancer.
“Many researchers found that deep learning can improve the prediction ability of NAC response prediction in breast cancer (13, 22).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [13]
The model was developed based on single-center data.
“the model was developed based on single-center data.”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [13]

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Huang, Y., Shi, Z., Zhu, T., Zhou, T., Li, Y., Li, W., Qiu, H., Wang, S., He, L., Wu, Z., et al. (2025) · Advanced Science · doi:10.1002/advs.202413702 · Cited by 47 · see citing works
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A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer
Mao, N., Dai, Y., Zhou, H., Lin, F., Zheng, T., Li, Z., Yang, P., Zhao, F., Li, Q., Wang, W., et al. (2025) · Science Advances · doi:10.1126/sciadv.adr1576 · Cited by 51 · see citing works
“Many researchers found that deep learning can improve the prediction ability of NAC response prediction in breast cancer ( 13 , 22 ).”
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CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning
Liu, H., Wang, M., Lu, Y., Wang, Q., Lu, Y., Xing, F., & Xing, W. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.11.024 · Cited by 44 · see citing works
Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study
Zhu, T., Huang, Y., Li, W., Zhang, Y., Lin, Y., Cheng, M., Wu, Z., Ye, G., Lin, Y., & Wang, K. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000621 · Cited by 40 · see citing works
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A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives
Corredor, G., Bharadwaj, S., Pathak, T. B., Viswanathan, V. S., Toro, P., & Madabhushi, A. (2023) · Clinical Breast Cancer · doi:10.1016/j.clbc.2023.06.004 · Cited by 39 · see citing works
A comprehensive overview of metaplastic breast cancer: Features and treatments
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MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas
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Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI
Teng, X., Wang, Y., Nicol, A. J., Ching, J. C. F., Wong, E. K. Y., Lam, K. T. C., Zhang, J., Lee, S. W. Y., & Cai, J. (2024) · Diagnostics · doi:10.3390/diagnostics14161835 · Cited by 30 · see citing works
Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures
Guo, S., Pan, D., Su, N., Huang, M.-Q., Zhou, Z.-Z., Huang, W., & Tian, X. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1266721 · Cited by 25 · see citing works
Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases
Zhang, H.-W., Wang, Y., Hu, B., Song, B., Wen, Z.-J., Su, L., Chen, X.-M., Wang, X., Zhou, P., Zhong, X.-M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-80210-x · Cited by 24 · see citing works
Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study
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Risk stratification of papillary thyroid cancers using multidimensional machine learning
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Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging
Wang, J., He, Y., Yan, L., Chen, S., & Zhang, K. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.04.022 · Cited by 22 · see citing works
Interpretable multimodal deep learning model for predicting post-surgical international society of urological pathology grade in primary prostate cancer
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Predicting lymph node metastasis in papillary thyroid carcinoma: radiomics using two types of ultrasound elastography
Zhang, X., Zhang, D., Zhou, W., Wang, Z., Zhang, C., Li, J., Wang, L., & Cui, X. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00832-w · Cited by 20 · see citing works
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Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data
Zhuang, L., Park, S., Skates, S. J., Prosper, A. E., Aberle, D. R., & Hsu, W. W. Y. (2025) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2025.3577587 · Cited by 27 · see citing works
Towards interpretable imaging genomics analysis: Methodological developments and applications
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Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics
Qu, X., Zhang, L., Ji, W., Lin, J., & Wang, G. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1267838 · Cited by 20 · see citing works
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Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study
Zeng, H., Qiu, S.-Q., Zhuang, S., Wei, X., Wu, J., Zhang, R., Chen, K., Wu, Z., & Zhuang, Z. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1279982 · Cited by 20 · see citing works
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Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study
You, J., Huang, Y. M., Ouyang, L., Zhang, X., Chen, P., Wu, X., Jin, Z., Shen, H., Zhang, L., Chen, Q., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102499 · Cited by 20 · see citing works
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Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review
Krasniqi, E., Filomeno, L., Arcuri, T., Ferretti, G., Gasparro, S., Fulvi, A., Roselli, A., D’Onofrio, L., Pizzuti, L., Barba, M., et al. (2025) · Biology Direct · doi:10.1186/s13062-025-00661-8 · Cited by 19 · see citing works
Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer
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Integrating deep learning and radiomics for preoperative glioma grading using multi-center MRI data
Shi, Y. P., Ming, J., Chen, H., Sun, Y. H., & Jiang, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-20711-5 · Cited by 18 · see citing works
Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study
Zhou, H., Bai, H. X., Jiao, Z., Cui, B., Wu, J., Zheng, H., Yang, H., & Liao, W. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111136 · Cited by 18 · see citing works
Duodenal papilla radiomics-based prediction model for post-ERCP pancreatitis using machine learning: a retrospective multicohort study
Chen, K., Lin, H., Zhang, F., Chen, Z., Ying, H., Cao, L., Fang, J., Zhu, D., & Liang, K. (2024) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2024.03.031 · Cited by 18 · see citing works
Artificial Intelligence and Breast Cancer Management: From Data to the Clinic
Feng, K., Yi, Z., & Xu, B. (2025) · Cancer Innovation · doi:10.1002/cai2.159 · Cited by 18 · see citing works
Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor
Xiao, X., Han, X., Sun, Y., Zheng, G., Miao, Q., Zhang, Y., Tan, J., Liu, G., He, Q., Zhou, J., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00636-4 · Cited by 17 · see citing works
Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model
Feng, X., Shi, Y., Wu, M., Cui, G., Du, Y., Yang, J., Xu, Y., Wang, W., & Liu, F. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-01971-5 · Cited by 17 · see citing works
AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study
Ye, G., Wei, Z., Han, C., Wu, G., Wong, C., Liang, Y., Chen, X., Zhou, W., Gao, J., Liang, C., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103551 · Cited by 19 · see citing works
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Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians
Hai, D., Chen, F., Li, H., Wang, K., Zhang, J., Meng, J., Li, H., Xu, X., Qu, J., Wu, R., et al. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1597 · Cited by 16 · see citing works
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BMEA-ViT: Breast Cancer Classification Using Lightweight Customized Vision Transformer Architecture With Multi-Head External Attention
Maurya, R., Pandey, N. N., & Mahapatra, S. (2025) · IEEE Access · doi:10.1109/access.2025.3547862 · Cited by 16 · see citing works
Intratumoral Microbiome–related MRI Model for Predicting Breast Cancer Shrinkage Pattern Following Neoadjuvant Therapy
Huang, Y., Song, X., Chen, Y., Qiu, H., Zhou, T., Wang, S., Zhou, Y., Li, W., Lin, Y., Wang, Q., et al. (2025) · Radiology · doi:10.1148/radiol.243545 · Cited by 15 · see citing works
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Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy
Xu, S., Ying, Y., Hu, Q., Li, X., Li, Y., Xiong, H., Chen, Y., Ye, Q., Li, X., Liu, Y., et al. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00929-2 · Cited by 15 · see citing works
Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
Guo, J., Meng, W., Li, Q., Zheng, Y., Yin, H., Liu, Y., Zhao, S., & Ma, J. (2024) · Bioengineering · doi:10.3390/bioengineering11070663 · Cited by 15 · see citing works
Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study
Fan, M., Wang, K., Pan, D., Cao, X., Li, Z., He, S., Xie, S., You, C., Gu, Y., & Li, L. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05487-y · Cited by 15 · see citing works
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Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method
Ren, W., Xi, X., Zhang, X., Wang, K., Liu, M., Wang, D., Du, Y., Sun, J., & Zhang, G. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.110305 · Cited by 15 · see citing works
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M2Fusion: Multi-time Multimodal Fusion for Prediction of Pathological Complete Response in Breast Cancer
Zhang, S., Du, S., Sun, C., Li, B., Shao, L., Zhang, L., Wang, K., Liu, Z., & Tian, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72086-4_43 · Cited by 14 · see citing works
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Coupling Habitat Radiomic Analysis with the Diversification of the Tumor ecosystem: Illuminating New Strategy in the Assessment of Postoperative Recurrence of Non-Muscle Invasive Bladder Cancer
Li, H., Sui, Y., Tao, Y., Cao, J., Jiang, X. W., Wang, B., & Du, Y. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.09.036 · Cited by 14 · see citing works
Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX
Zhang, R., Wang, K., Wang, S., Wang, C., Cao, T., Ci, C., Xu, M., & Ge, M. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02129-z · Cited by 13 · see citing works
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Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights
Wu, X., Xia, Y., Lou, X., Huang, K., Wu, L., & Gao, C. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-01983-1 · Cited by 13 · see citing works
Longitudinal Image Data for Outcome Modeling.
van Timmeren, J. E., Bussink, J., Koopmans, P. J., Smeenk, R., & Monshouwer, R. (2024) · Clinics in oncology · doi:10.1016/j.clon.2024.06.053 · Cited by 13 · see citing works
Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study
Zeng, Q., Liu, L., He, C., Zeng, X., Wei, P., Xu, D., Mao, N., & Yu, T. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.10.033 · Cited by 12 · see citing works
Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study
Tang, W., Chen, J., Kong, Q., Liu, C., Chen, S., Ding, S., Liu, B., Feng, Z., Li, Y., Dai, Y., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103298 · Cited by 11 · see citing works
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Artificial Intelligence in Obstetric and Gynecological MR Imaging
Saida, T., Gu, W., Hoshiai, S., Ishiguro, T., Sakai, M., Amano, T., Nakahashi, Y., Shikama, A., Satoh, T., & Nakajima, T. (2024) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2024-0077 · Cited by 10 · see citing works
18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer
Zheng, X., Huang, Y.-H., Lin, Y.-Y., Zhu, T., Zou, J., Wang, S., & Wang, K. (2023) · EJNMMI Research · doi:10.1186/s13550-023-01053-7 · Cited by 11 · see citing works
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A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors
Wang, Z., He, C., Hu, Y., Luo, H., Li, C., Wu, X., Zhang, Y., Li, J., & Cai, J. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1363756 · Cited by 9 · see citing works
The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer
Wang, J., Tian, C., Zheng, B., Zhang, J., Jiao, D., Qu, J., & Liu, Z. (2024) · BMC Cancer · doi:10.1186/s12885-024-12257-y · Cited by 9 · see citing works
Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning
Lin, F., Wang, K., Lai, M., Yang, W., Chen, C., Wang, Y., & Wang, R. (2025) · Scientific Reports · doi:10.1038/s41598-024-72539-0 · Cited by 11 · see citing works
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Evaluation of a novel ensemble model for preoperative ovarian cancer diagnosis: Clinical factors, O-RADS, and deep learning radiomics
Wu, Y., Fan, L., Shao, H., Li, J., Yin, W., Yin, J., Zhu, W., Zhang, P., Zhang, C., & Wang, J. (2025) · Translational Oncology · doi:10.1016/j.tranon.2025.102335 · Cited by 9 · see citing works
Neoadjuvant Chemotherapy and Neoadjuvant Chemotherapy With Immunotherapy Result in Different Tumor Shrinkage Patterns in Triple-Negative Breast Cancer
Zou, J., Zhang, L., Chen, Y., Lin, Y., Cheng, M., Zheng, X., Zhuang, X., & Wang, K. (2023) · Journal of Breast Cancer · doi:10.4048/jbc.2023.0136 · Cited by 9 · see citing works
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Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study
Wang, F., Yan, C. Y., Qin, Y. H., Wang, Z. M., Liu, D., He, Y., Yang, M., Wen, L., & Zhang, D. (2024) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s449737 · Cited by 9 · see citing works
Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study
pan, N., Shi, L., He, D.-L., Zhao, J., Xiong, L., Ma, L., Li, J., Ai, K., Zhao, L., & Huang, G. (2024) · Discover Oncology · doi:10.1007/s12672-024-00980-8 · Cited by 8 · see citing works
Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study
Zhu, X., Li, J., Li, H., Wang, K.-F., Zhang, J., Meng, J., Wu, R., Zhang, M.-L., & Du, H. (2024) · Gland surgery · doi:10.21037/gs-24-416 · Cited by 7 · see citing works
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Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review
Zhang, T., Zhao, L., Cui, T., Zhou, Y., Li, P., Luo, C., Wei, J., & Hu, H. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06641-w · Cited by 8 · see citing works
Evaluating axillary lymph node metastasis risks in breast cancer patients via Semi-ALNP: a multicenter study
Qu, L., Zhu, J., Mei, X., Yi, Z., Luo, N., Yuan, S., Liu, X. E., Liu, M., Xie, H., Hu, X., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103311 · Cited by 8 · see citing works
Time‐Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach
Liu, J., Xu, L., Wang, G., Zeng, W., Zeng, H., Wen, C., Xu, W., He, Z., Qin, G., & Chen, W. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29405 · Cited by 8 · see citing works
Recent Advancements of Nanomedicine in Breast Cancer Surgery
Meng, X., Wang, X., Zhang, Z., Song, L., & Chen, J. (2024) · International Journal of Nanomedicine · doi:10.2147/ijn.s494364 · Cited by 8 · see citing works
Research progress of artificial intelligence in evaluating the efficacy of neoadjuvant chemotherapy for breast cancer
Wei, W., Ma, M., & Liu, Z. (2024) · EngMedicine · doi:10.1016/j.engmed.2024.100024 · Cited by 7 · see citing works
MR imaging of breast cancer: Interpretable radiomics analysis to assess treatment response and survival prognosis after neoadjuvant therapy
Hou, J., Yang, F., Gao, Y., Cai, H., Li, X., Lin, C., Hong, M., He, X., Shu, Z., Chen, X., et al. (2025) · International Journal of Cancer · doi:10.1002/ijc.35514 · Cited by 7 · see citing works
Anatomically Based Multitask Deep Learning Radiomics Nomogram Predicts the Implant Failure Risk in Sinus Floor Elevation
Zhu, Y., Liu, Y., Zhao, Y., Lu, Q., Wang, W., Chen, T., Ji, P., & Chen, T. (2025) · Clinical Oral Implants Research · doi:10.1111/clr.70011 · Cited by 7 · see citing works
Multimodal prediction based on ultrasound for response to neoadjuvant chemotherapy in triple negative breast cancer
Lyu, M., Yi, S., Li, C., Xie, Y., Liu, Y., Xu, Z., Wei, Z., Lin, H., Zheng, Y., Huang, C., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01057-7 · Cited by 7 · see citing works
Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI
Zhang, Y., Cai, J., Cui, C., Qi, S., & Zhao, D. (2025) · BMC Cancer · doi:10.1186/s12885-025-15095-8 · Cited by 7 · see citing works
Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE‐MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study
Huang, Y., Cao, Y. J., Hu, X., Lan, X., Chen, H., Tang, S., Li, L., Cheng, Y., Gong, X., Wang, W., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29188 · Cited by 7 · see citing works
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Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy
Jiang, Z., Yuan, F., Zhang, Q., Zhu, J., Xu, M., Hu, Y., Hou, C., & Liu, X. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1182 · Cited by 7 · see citing works
Development of a Dual‐Plane MRI ‐Based Deep Learning Model to Assess the 1‐Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy
Wang, K., Lin, F., Liao, Z., Wang, Y., Zhang, T., & Wang, R. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29639 · Cited by 7 · see citing works
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Deep learning model for the early prediction of pathologic response following neoadjuvant chemotherapy in breast cancer patients using dynamic contrast-enhanced MRI
Lv, M., Zhao, B., Mao, Y. K., Wang, Y., Su, X., Zhang, Z., Wu, J., Gao, X., & Wang, Q. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1491843 · Cited by 7 · see citing works
Differentiation of early relapse and late relapse in intermediate- and high-risk neuroblastoma with an 18F-FDG PET/CT-based radiomics nomogram
Feng, L., Yao, X., Lu, X., Wang, C., Wang, W., & Yang, J. (2024) · Abdominal Radiology · doi:10.1007/s00261-023-04181-9 · Cited by 6 · see citing works
Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model
Chen, Y., Ruan, X., Wang, X., Li, P., Chen, Y., Feng, B., Wen, X., Sun, J., Zheng, C., Zou, Y., et al. (2025) · Abdominal Radiology · doi:10.1007/s00261-024-04766-y · Cited by 6 · see citing works
Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy
Tenghui, W., Xinyi, L., Ziyi, S., Yanting, Z., Ziqian, M., Yiwen, Z., & Gan, L. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1525285 · Cited by 6 · see citing works
Application of Machine Learning to Breast MR Imaging
Gullo, R. L., van Veldhuizen, V., Roa, T., Kapetas, P., Teuwen, J., & Pinker, K. (2025) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2025-0021 · Cited by 7 · see citing works
Longitudinal MRI-based deep learning model for predicting pathological complete response in breast cancer: a multicenter, retrospective cohort study
Huang, X. C., Xu, Z., Zhao, Y., Wang, Y., Liu, Y., Hu, W., Zhao, K., Yao, L., He, J., Yu, Y., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-025-01256-2 · Cited by 6 · see citing works
Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review
Bao, Z., Du, J., Zheng, Y., Guo, Q., & Ji, R. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1363812 · Cited by 6 · see citing works
Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort
Du, S., Xie, W., Si, G., Zhao, R., Wang, H., Tian, J., Liu, J., Liu, Z., & Zhang, L. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02009-6 · Cited by 6 · see citing works
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Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study
Jin, Y., Zhao, M., Su, T., Fan, Y., Ouyang, Z., & Lv, F. (2025) · Journal of Medical Internet Research · doi:10.2196/69864 · Cited by 6 · see citing works
Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset
Yu, Q.-S., Shan, J.-Y., Ma, J., Gao, G., Tao, B., Qiao, G.-Y., Zhang, J., Wang, T., Zhao, Y.-F., Qin, X., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05403-z · Cited by 6 · see citing works
Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study
Li, Y., Li, W., Wang, L., Wang, X., Gao, S., Liao, Y., Ji, Y., Lin, L., Liu, Y., & Chen, J. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2023.1272814 · Cited by 5 · see citing works
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Habitat‐based radiomic model for predicting muscle invasion in bladder cancer: A multi‐center study using enhanced‐CT and machine learning
Du, Y., Li, H., Sui, Y., Tao, Y., Cao, J., Jiang, X. W., Wang, B., & Xue, B. (2025) · Medical Physics · doi:10.1002/mp.18021 · Cited by 5 · see citing works
Differentiating triple-negative breast cancer and atypical fibroadenomas using an ultrasound-based radiomics nomogram
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Authors: Huang, Y., Zhu, T., Zhang, X., Li, W., Zheng, X., Cheng, M., Ji, F., Zhang, L., Yang, C., Wu, Z., Ye, G., Lin, Y., & Wang, K.
Affiliations: Guangdong Academy of Medical Sciences (CN)
Sun Yat-sen University (CN)
First People's Hospital of Foshan (CN)
Corresponding author: Ye, GuoLin
Lin, Ying
Wang, Kun
ORCID: Zhu, Teng
Li, Wei
Cheng, MinYi
Ji, Fei
Zhang, LiuLu
Yang, CiQiu
Lin, Ying
Wang, Kun
Venue: eClinicalMedicine
Publisher: Elsevier BV
Published: 2023-04
Volume: 58
Pages: 101899
ISSN: 2589-5370 (ISSN-L)
DOI: 10.1016/j.eclinm.2023.101899
PMC: PMC10050775
OpenAlex: W4360796946
Cited by: 175
References: 55
Citations by year: 2026: 49 · 2025: 67 · 2024: 46 · 2023: 13
FWCI: 28.98
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: EClinicalMedicine (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJHBF202109)
Basic and Applied Basic Research Foundation of Guangdong Province (2020A1515010346, 2021A1515011570, 2022A1515012277)
Scientific and Technological Planning Project of Guangzhou City
National Natural Science Foundation of China (82103093)
Guangzhou Municipal Science and Technology Project (202002030236, 202102021055)
Foundation for Innovative Research Groups of the National Natural Science Foundation of China (82171898)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords: breast cancer, pathological complete response, neoadjuvant chemotherapy, multiparametric MRI, longitudinal MRI, radiomics, deep features, ensemble learning, stacking ensemble, breast cancer subtypes, triple-negative breast cancer, HER2-positive breast cancer, hormone receptor-positive HER2-negative breast cancer, treatment response prediction, feature selection, external validation, multilayer perceptron

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Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study

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