Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study
Open accessAbstract
Objectives Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage.Methods In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration.Results In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC.Conclusions The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients.Key points • We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most previous studies trained data using a single network, so the complementary advantages of multiple networks were overlooked.
“Most of the previous studies trained the data using a single network; thus, the complementary advantages of multiple networks were overlooked (27, 28).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [28]
Cites
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
U1 snRNP regulates cancer cell migration and invasion in vitro
Oh, J.-M., Venters, C. C., Di, C., Pinto, A. M., Wan, L., Younis, I., Cai, Z., Arai, C., So, B. R., Duan, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13993-7 · Cited by 7,157 · see citing works
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084 · see citing works
Breast Cancer, Version 4.2017, NCCN Clinical Practice Guidelines in Oncology
Gradishar, W. J., Anderson, B. O., Balassanian, R., Blair, S. L., Burstein, H. J., Cyr, A. E., ELIAS, A. D., Farrar, W. B., Forero, A., Giordano, S. H., et al. (2018) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2018.0012 · Cited by 1,607 · see citing works
Breast Cancer, Version 3.2020, NCCN Clinical Practice Guidelines in Oncology
Gradishar, W. J., Anderson, B. O., Abraham, J., Aft, R., Agnese, D. M., Allison, K. H., Blair, S. L., Burstein, H. J., Dang, C. T., ELIAS, A. D., et al. (2020) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2020.0016 · Cited by 1,587 · see citing works
Yang, C., Zhao, H., Bruzzone, L., Benediktsson, J. A., Liang, Y., Liu, B., Zeng, X., Guan, R., Li, C., & Ouyang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-20215-y · Cited by 1,065 · see citing works
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15027-z · Cited by 607 · see citing works
Wang, K., Lu, X., Zhou, H., Gao, Y., Zheng, J., Tong, M., Wu, C., Liu, C., Huang, L., Jiang, T., et al. (2018) · Gut · doi:10.1136/gutjnl-2018-316204 · Cited by 503 · see citing works
Self-citationHylton, N. M., Blume, J. D., Bernreuter, W. K., Pisano, E. D., Rosen, M. A., Morris, E. A., Weatherall, P. T., Lehman, C. D., Newstead, G. M., Polin, S. M., et al. (2012) · Radiology · doi:10.1148/radiol.12110748 · Cited by 499 · see citing works
Liu, Z., Li, Z., Qu, J., Zhang, R., Zhou, X., Li, L., Sun, K., Tang, Z., Jiang, H., Li, H., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3190 · Cited by 494 · see citing works
Self-citationYamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30535-0 · Cited by 391 · see citing works
Haque, W. M., Verma, V., Hatch, S. S., Klimberg, V. S., Butler, E. B., & Teh, B. S. (2018) · Breast Cancer Research and Treatment · doi:10.1007/s10549-018-4801-3 · Cited by 380 · see citing works
Jiang, M., Li, C.-L., Luo, X.-M., Chuan, Z.-R., Lv, W.-Z., Li, X., Cui, X.-W., & Dietrich, C. F. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.01.028 · Cited by 214 · see citing works
Goetz, M. P., Gradishar, W. J., Anderson, B. O., Abraham, J., Aft, R., Allison, K. H., Blair, S. L., Burstein, H. J., Dang, C. T., ELIAS, A. D., et al. (2019) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2019.0009 · Cited by 195 · see citing works
Croshaw, R. L., Shapiro-Wright, H. M., Svensson, E., Erb, K. M., & Julian, T. B. (2011) · Annals of Surgical Oncology · doi:10.1245/s10434-011-1919-5 · Cited by 185 · see citing works
Pinder, S. E., Provenzano, E., Earl, H. M., & Ellis, I. O. (2006) · Histopathology · doi:10.1111/j.1365-2559.2006.02419.x · Cited by 154 · see citing works
Neoadjuvant chemotherapy in breast cancer: more than just downsizing
Derks, M. G. M., & van de Velde, C. J. �. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30914-2 · Cited by 118 · see citing works
Multimodality Imaging for Evaluating Response to Neoadjuvant Chemotherapy in Breast Cancer
Rauch, G. M., Adrada, B. E., Kuerer, H. M., van la Parra, R. F. D., Leung, J., & Yang, W. T. (2016) · American Journal of Roentgenology · doi:10.2214/ajr.16.17223 · Cited by 111 · see citing works
Stephens, T. W. (2012) · Yearbook of Diagnostic Radiology · doi:10.1016/j.yrad.2012.03.050 · Cited by 99 · see citing works
Guo, X., Liu, Z., Sun, C., Zhang, L., Wang, Y., Li, Z., Shi, J., Wu, T., Cui, H., Zhang, J., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103018 · Cited by 99 · see citing works
Self-citationChoi, J. H., Kim, H., Kim, W., Lim, I., Lee, I., Byun, B. H., Noh, W. C., Seong, M.-K., Lee, S., Kim, B. I., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-77875-5 · Cited by 98 · see citing works
Xiong, Q., Zhou, X., Liu, Z., Lei, C., Yang, C., Yang, M., Zhang, L., Zhu, T., Zhuang, X., Liang, C., et al. (2019) · Clinical & Translational Oncology · doi:10.1007/s12094-019-02109-8 · Cited by 93 · see citing works
Self-citationVriens, B. E. P. J., de Vries, B., Lobbes, M. B. I., van Gastel, S. M., van den Berkmortel, F. W. P. J., Smilde, T. J., van Warmerdam, L. J. C., de Boer, M., van Spronsen, D. J., Smidt, M. L., et al. (2015) · European Journal of Cancer · doi:10.1016/j.ejca.2015.10.010 · Cited by 85 · see citing works
Rücker, G., & Schumacher, M. C. (2010) · Statistics in Medicine · doi:10.1002/sim.3937 · Cited by 79 · see citing works
Corben, A. D., Abi-Raad, R. F., Popa, I., Teo, C. H. Y., Macklin, E. A., Koerner, F. C., Taghian, A. G., & Brachtel, E. F. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2012-0290-oa · Cited by 76 · see citing works
Byra, M., Dobruch‐Sobczak, K., Klimonda, Z., Piotrzkowska‐Wróblewska, H., & Litniewski, J. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3008040 · Cited by 76 · see citing works
Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer
Reig, B., Heacock, L. C., Lewin, A. A., Cho, N., & Moy, L. P. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27145 · Cited by 60 · see citing works
Ma, Y., Zhang, S., Zang, L., Li, J., Li, J., Kang, Y., & Ren, W. (2016) · European Journal of Cancer · doi:10.1016/j.ejca.2016.09.031 · Cited by 57 · see citing works
Locoregional Therapy of Locally Advanced Breast Cancer: A Clinical Practice Guideline
Brackstone, M., Fletcher, G. G., Dayes, I. S., Madarnas, Y., Sengupta, S., Verma, S., & Members of the Breast Cancer Disease Site Group (2015) · Current Oncology · doi:10.3747/co.22.2316 · Cited by 43 · see citing works
Zhuang, X., Chen, C., Liu, Z., Zhang, L., Zhou, X., Cheng, M., Ji, F., Zhu, T., Lei, C., Zhang, J., et al. (2020) · Translational Oncology · doi:10.1016/j.tranon.2020.100831 · Cited by 40 · see citing works
Self-citationEun, N. L., Son, E. J., Gweon, H. M., Kim, J., & Youk, J. H. (2019) · European Radiology · doi:10.1007/s00330-019-06539-4 · Cited by 34 · see citing works
Arıcı, S., Erhan, S. Ş., Geredeli, Ç., Çekin, R., Sakin, A., & Cihan, Ş. (2020) · Oncology Research and Treatment · doi:10.1159/000508478 · Cited by 13 · see citing works
Nakashima, K., Uematsu, T., Harada, T. L., Takahashi, K., Nishimura, S., Tadokoro, Y., Hayashi, T., Watanabe, J., Sugino, T., & Notsu, A. (2021) · Breast Cancer · doi:10.1007/s12282-021-01249-6 · Cited by 11 · see citing works
Risk scoring system for predicting breast conservation after neoadjuvant chemotherapy
Ouldamer, L., Bendifallah, S., Pilloy, J., Arbion, F., Body, G., Brisson, C., Lavoué, V., Lévêque, J., & Daraï, É. (2019) · The Breast Journal · doi:10.1111/tbj.13303 · Cited by 8 · see citing works
Kim, C., Han, S., Won, K. Y., Hong, I. K., & Kim, D. Y. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030132 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A., & Zou, J. (2023) · Cell · doi:10.1016/j.cell.2023.01.035 · Cited by 597 · see citing works
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, C. W., Cheng, K.-T. T., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3357877 · Cited by 174 · see citing works
Zhang, J., Wu, J., Zhou, X. S., Shi, F., & Shen, D. G. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.001 · Cited by 145 · see citing works
Gu, J., Tong, T., Xu, D. M., Cheng, F., Fang, C., He, C., Wang, J., Wang, B., Yang, X., Wang, K., et al. (2022) · Cancer · doi:10.1002/cncr.34540 · Cited by 102 · see citing works
Self-citationBig data and artificial intelligence in cancer research
Wu, X., Li, W., & Tu, H. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.10.006 · Cited by 81 · see citing works
Tong, T., Gu, J., Xu, D., Song, L., Zhao, Q., Cheng, F., Yuan, Z., Tian, S., Yang, X., Tian, J., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02258-8 · Cited by 77 · see citing works
Self-citationLiu, Y., Wang, Y., Wang, Y., Xie, Y. S., Cui, Y., Feng, S., Yao, M., Qiu, B., Shen, W., Chen, D., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101562 · Cited by 71 · see citing works
Self-citationZhang, J., Wu, Q., Yin, W., Yang, L., Xiao, B., Wang, J., & Yao, X. (2023) · BMC Cancer · doi:10.1186/s12885-023-10817-2 · Cited by 60 · see citing works
Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review
Brunetti, N., Calabrese, M., Martinoli, C., & Tagliafico, A. S. (2022) · Diagnostics · doi:10.3390/diagnostics13010058 · Cited by 57 · see citing works
Huang, Y., Wang, X., Cao, Y. J., Lan, X., Hu, X., Mou, F., Chen, H., Gong, X., Li, L., Tang, S., et al. (2025) · Radiology · doi:10.1148/radiol.241805 · Cited by 56 · see citing works
Zhou, Z., Adrada, B. E., Candelaria, R. P., Elshafeey, N. A., Böge, M., Mohamed, R. M. M., Pashapoor, S., Sun, J., Xu, Z., Panthi, B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-27518-2 · Cited by 52 · see citing works
Radiomics and artificial intelligence in breast imaging: a survey
Zhang, T., Tan, T., Samperna, R., Li, Z., Gao, Y., Wang, X., Han, L., Yu, Q., Beets‐Tan, R. G. H., & Mann, R. M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10543-y · Cited by 51 · see citing works
Yu, F., Miao, S., Li, C., Hang, J., Deng, J., Ye, X., & Liu, Y. (2023) · European Radiology · doi:10.1007/s00330-023-09555-7 · Cited by 49 · see citing works
Li, Y., Fan, Y., Xu, D., Li, Y., Zhong, Z., Pan, H., Huang, B., Xie, X., Yang, Y., & Liu, B. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1041142 · Cited by 48 · see citing works
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
“Most of the previous studies trained the data using a single network; thus, the complementary advantages of multiple networks were overlooked ( 27 , 28 ).”
Artificial intelligence in breast imaging: Current situation and clinical challenges
You, C., Shen, Y., Sun, S., Zhou, J., Li, J., Su, G.-H., Michalopoulou, E., Peng, W., Gu, Y., Guo, W., et al. (2023) · Exploration · doi:10.1002/exp.20230007 · Cited by 45 · see citing works
Predicting Prolonged Length of ICU Stay through Machine Learning
Wu, J., Lin, Y., Li, P., Hu, Y., Zhang, L., & Kong, G. (2021) · Diagnostics · doi:10.3390/diagnostics11122242 · Cited by 47 · see citing works
Chia, J. L. L., He, G. S., Ngiam, K. Y., Hartman, M., Ng, Q. X., & Goh, S. S. N. (2025) · Cancers · doi:10.3390/cancers17020197 · Cited by 40 · see citing works
Ultrasound radiomics in personalized breast management: Current status and future prospects
Gu, J., & Jiang, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.963612 · Cited by 37 · see citing works
Self-citationArtificial intelligence in breast ultrasound: application in clinical practice
Fruchtman-Brot, H., & Mango, V. L. (2023) · ULTRASONOGRAPHY · doi:10.14366/usg.23116 · Cited by 35 · see citing works
Wu, L., Ye, W., Liu, Y., Chen, D., Wang, Y., Cui, Y., Li, P., Li, Z., Liu, Z., Liu, M., et al. (2022) · Breast Cancer Research · doi:10.1186/s13058-022-01580-6 · Cited by 35 · see citing works
Self-citationDuwe, G., Mercier, D., Wiesmann, C., Kauth, V., Moench, K., Junker, M., Neumann, C., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Cancer Medicine · doi:10.1002/cam4.7398 · Cited by 31 · see citing works
Tong, T., Li, D., Gu, J., Chen, G., Bai, G., Yang, X., Wang, K., Jiang, T., & Tian, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3216031 · Cited by 28 · see citing works
Self-citationArtificial Intelligence for Breast Ultrasound: AJR Expert Panel Narrative Review
Bahl, M., Chang, J. M., Mullen, L. A., & Berg, W. A. (2024) · American Journal of Roentgenology · doi:10.2214/ajr.23.30645 · Cited by 27 · see citing works
Prospective Clinical Research of Radiomics and Deep Learning in Oncology: A Translational Review.
Zhang, X.-P., Zhang, Y., Zhang, G., Qiu, X.-T., Tan, W., Yin, X., & Liao, L. (2022) · Critical reviews in oncology/hematology · doi:10.1016/j.critrevonc.2022.103823 · Cited by 22 · see citing works
Gu, J., Pan, J., Hu, J., Dai, L., Zhang, K., Wang, B., He, M., Zhao, Q., & Jiang, T. (2023) · Cancer · doi:10.1002/cncr.34772 · Cited by 27 · see citing works
Self-citationQin, X., Hu, X., Xiao, W., Zhu, C., Ma, Q., & Zhang, C. (2023) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s400166 · Cited by 24 · see citing works
Kalvala, A. K., Nimma, R., Bagde, A., Surapaneni, S. K., Patel, N., Arthur, P., Sun, L. J., Singh, R. K., Kommineni, N., Nathani, A., et al. (2022) · Biochimie · doi:10.1016/j.biochi.2022.12.008 · Cited by 22 · see citing works
Cai, L., Sidey-Gibbons, C. J., Nees, J., Riedel, F., Schäfgen, B., Togawa, R., Killinger, K., Heil, J., Pfob, A., & Golatta, M. (2023) · European Radiology · doi:10.1007/s00330-023-10238-6 · Cited by 22 · see citing works
Li, Z., Liu, X., Gao, Y., Lu, X., & Lei, J. (2024) · La radiologia medica · doi:10.1007/s11547-024-01783-1 · Cited by 21 · see citing works
Jiang, C. F., Zhang, X., Qu, T., Yang, X., Xiu, Y., Yu, X. Y., Zhang, S., Qiao, K., Meng, H., Li, X., et al. (2024) · Translational Oncology · doi:10.1016/j.tranon.2024.101985 · Cited by 21 · see citing works
Self-citationUpdates in Artificial Intelligence for Breast Imaging
Bahl, M. (2021) · Seminars in Roentgenology · doi:10.1053/j.ro.2021.12.005 · Cited by 20 · see citing works
Artificial intelligence in breast imaging: potentials and challenges
Li, J.-W., Sheng, D., Chen, J., You, C., Liu, S., Xu, H., & Chang, C. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acfade · Cited by 22 · see citing works
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
Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends
Wang, R., Chen, F., Chen, H., Lin, C. X., Shuai, J., Wu, Y., Ma, L., Hu, X., Wu, M. H., Wang, J., et al. (2025) · MedComm · doi:10.1002/mco2.70247 · Cited by 22 · see citing works
Han, J., Hua, H., Fei, J., Liu, J., Guo, Y., Ma, W., & Chen, J. (2024) · Clinical Breast Cancer · doi:10.1016/j.clbc.2024.01.005 · Cited by 19 · see citing works
Emerging uses of artificial intelligence in breast and axillary ultrasound
Trepanier, C., Huang, A., Liu, M. Z., & Ha, R. S. (2023) · Clinical Imaging · doi:10.1016/j.clinimag.2023.05.007 · Cited by 19 · see citing works
Potential application of artificial intelligence in cancer therapy
Riaz, I. B., Khan, M. A., & Haddad, T. C. (2024) · Current Opinion in Oncology · doi:10.1097/cco.0000000000001068 · Cited by 19 · see citing works
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
Liu, Y., Wang, Y., Huang, J., Pei, S., Wang, Y., Cui, Y., Yan, L., Yao, M., Wang, Y., Zhu, Z., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105896 · Cited by 16 · see citing works
ELSayed, B. B., Alksas, A., Shehata, M., Mahmoud, A. H., Zaky, M. M., Alghandour, R., Abdelwahab, K. M., Abdelkhalek, M., Ghazal, M. A., Contractor, S. G., et al. (2023) · Cancers · doi:10.3390/cancers15215288 · Cited by 16 · see citing works
Wang, X., Hua, H., Han, J., Zhong, X., Liu, J., & Chen, J. (2023) · Clinical Breast Cancer · doi:10.1016/j.clbc.2023.05.010 · Cited by 15 · see citing works
He, M., Chen, Z., Liu, S., Chen, Y., Zhang, H., Zhang, L., Zhao, J., Yang, J., Zhang, X., Shen, L., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01639-2 · Cited by 15 · see citing works
Jia, Y., Wu, R., Lu, X., Duan, Y., Zhu, Y., Ma, Y., & Nie, F. (2023) · Cancers · doi:10.3390/cancers15030838 · Cited by 14 · see citing works
Wang, J., Dai, J., Cheng, Y., Wang, X., Deng, R., & Zhu, H. (2025) · Translational Oncology · doi:10.1016/j.tranon.2025.102435 · Cited by 13 · see citing works
Zhang, J., Teng, X., Zhang, X., Lam, S., Lin, Z., Liang, Y., Yu, H., Siu, S. W. K., Chang, A. T. Y., Zhang, H., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-45477-6 · Cited by 13 · see citing works
Li, H., Zhao, Y., Duan, J., Gu, J., Liu, Z., Zhang, H., Zhang, Y., & Li, Z. (2024) · Displays · doi:10.1016/j.displa.2024.102698 · Cited by 13 · see citing works
Self-citationTian, S., Yu, Y.-H., Shi, K., Jiang, Y., Song, H., Wang, Y., Yan, X., Zhong, Y., & Shao, G. (2024) · Nephrology · doi:10.1111/nep.14376 · Cited by 13 · see citing works
Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method
Zou, H., Shi, S., Yang, X., Ma, J., Fan, Q., Chen, X., Wang, Y., Zhang, M., Song, J., Jiang, Y., et al. (2022) · BioMedical Engineering OnLine · doi:10.1186/s12938-022-01057-9 · Cited by 11 · see citing works
Self-citationXie, L., Liu, Z., Pei, C., Liu, X., Cui, Y., He, N., & Hu, L. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1099650 · Cited by 11 · see citing works
Liu, Y., Liu, Q., Hu, Q.-H., Shi, J., Liu, G.-L., Liu, H., & Shu, S.-C. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.07.036 · Cited by 11 · see citing works
Chang, J. M., Leung, J. W. T., Heacock, L. C., Lee, S. H., Moon, W. K., & Hooley, R. J. (2026) · Radiology · doi:10.1148/radiol.233101 · Cited by 10 · see citing works
Miao, Y., Tang, S., Zhang, Z., Song, J., Liu, Z., Chen, Q., & Zhang, M. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05797-w · Cited by 10 · see citing works
Huang, J., Lu, Y., Tan, Y., Liu, F., Li, Y.-L., Wang, X., Huang, J., Lin, S.-Y., Huang, G.-L., Zhang, Y.-T., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000002105 · Cited by 10 · see citing works
Wang, M., Mo, S., Li, G., Zheng, J., Wu, H., Tian, H., Chen, J., Tang, S., Chen, Z., Xu, J., et al. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02113-7 · Cited by 9 · see citing works
Byra, M., Dobruch‐Sobczak, K., Piotrzkowska‐Wróblewska, H., Klimonda, Z., & Litniewski, J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac8c82 · Cited by 9 · see citing works
Long, B., Zhang, H., Zhang, H., Chen, W., Sun, Y., Tang, R., Lin, Y., Fu, Q., Yang, X., Cui, L., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-03099-9 · Cited by 9 · see citing works
Self-citationJiang, W., Deng, X., Zhu, T., Fang, J., & Li, J. (2023) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s418376 · Cited by 8 · see citing works
Xie, J., Wei, J., Shi, H., Lin, Z., Lu, J., Zhang, X., & Wan, C. (2025) · BMC Medical Imaging · doi:10.1186/s12880-024-01543-7 · Cited by 8 · see citing works
Wei, W., Ma, M., & Liu, Z. (2024) · EngMedicine · doi:10.1016/j.engmed.2024.100024 · Cited by 7 · see citing works
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
Zhou, P., Qian, H., Zhu, P., Ben, J., Chen, G., Chen, Q., Chen, L., Chen, J., & He, Y. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2024.1485681 · Cited by 7 · see citing works
Shi, Z., Ma, Y., Ma, X., Jin, A., Zhou, J., Li, N., Sheng, D., Chang, C., Chen, J., & Li, J. (2023) · Sensors · doi:10.3390/s23115099 · Cited by 7 · see citing works
Guo, R., Yu, Y., Huang, Y., Lin, M., Liao, Y., Hu, Y., Li, Q., Peng, C., & Zhou, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e23383 · Cited by 6 · see citing works
Yang, L., Zhang, N., Jia, J., & Ma, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-83347-x · Cited by 6 · see citing works
Wang, B. C., Chen, M. H., Liu, B. J., Li, B. C., Jiao, B. W., Guo, B. Q., & Zhang, P. Q. (2023) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2023.230012 · Cited by 6 · see citing works
Wu, P., Jiang, Y., Xing, H., Song, W., Cui, X., Wu, X.-L., & Xu, G. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acec2d · Cited by 6 · see citing works
Zhang, H., Yi, H., Qin, S., Liu, X., & Liu, G. (2024) · PLoS ONE · doi:10.1371/journal.pone.0315339 · Cited by 5 · see citing works
Hathaway, Q. A., Abdeen, Y., Conte, J., Hass, R., Santer, M. J., Alyami, B., Avalon, J. C., & Patel, B. D. (2024) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-024-03101-2 · Cited by 5 · see citing works
Yang, Z., & Liu, C. (2024) · Medicine · doi:10.1097/md.0000000000039463 · Cited by 5 · see citing works
Song, Y., Liu, S., Liu, X., Jia, H., Shi, H., Liu, X., Hao, D., Wang, H., & Xing, X. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.08.014 · Cited by 5 · see citing works
Qin, Q., Gan, X.-Y., Lin, P., Pang, J., Gao, R., Wen, R., Liu, D., Tang, Q., Liu, C., He, Y., et al. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01237-0 · Cited by 5 · see citing works
Piotrzkowska‐Wróblewska, H. (2025) · Cancers · doi:10.3390/cancers17223676 · Cited by 4 · see citing works
Advancing ultrasonography with AI: recent innovations and future perspectives
Karako, K. (2025) · Holistic Integrative Oncology · doi:10.1007/s44178-025-00160-w · Cited by 4 · see citing works
Valizadeh, P., Jannatdoust, P., Moradi, N., Yaghoobpoor, S., Toofani, S., Rafiei, N., Moodi, F., Ghorani, H., & Arian, A. (2025) · Clinical Imaging · doi:10.1016/j.clinimag.2025.110574 · Cited by 4 · see citing works
Zhou, F., Xu, Y., Cui, Y., Zhang, S., Zhu, Y., He, W., Zhang, D., Wang, J., Wang, X., Chan, R. C., et al. (2025) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2025.100011 · Cited by 5 · see citing works
Liu, J., Leng, X., Yuan, Z., Li, Y., Luo, H., Chen, J., Li, R., Li, X., He, Z., Zou, Q., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-15148-y · Cited by 4 · see citing works
EPD: an integrated modeling technique to classify BC
Prusty, S., Dash, S. K., Patnaik, S., Prusty, S. G. P., & Tripathy, N. (2023) · International Conference in Advances in Power, Signal, and Information Technology (APSIT) · doi:10.1109/apsit58554.2023.10201778 · Cited by 4 · see citing works
Zhang, M., Liu, X., Du, Y. L., Zha, H., Zha, X., Wang, J., Liu, X., Wang, S.-J., Zou, Q., Zhang, J., et al. (2023) · British Journal of Radiology · doi:10.1093/bjr/tqad014 · Cited by 4 · see citing works
Zhang, J., Deng, J., Huang, J., Mei, L., Liao, N., Yao, F., Lei, C., Sun, S., & Zhang, Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1255618 · Cited by 4 · see citing works
Li, Z., Wu, S., Lin, Z., Jiang, M., Chen, C., Liang, R., Lin, W., & Xue, E. (2024) · Journal of Clinical Ultrasound · doi:10.1002/jcu.23666 · Cited by 4 · see citing works
Wu, K., Zhu, Z., Xu, D., Lu, Y., Zhang, T., Wang, X., Hu, C., Gu, W., & Yu, Y. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112487 · Cited by 3 · see citing works
Jia, Y., Zhou, X., Zhu, Y., Song, X., Duan, Y., Chen, K., & Nie, F. (2023) · Medical Ultrasonography · doi:10.11152/mu-3909 · Cited by 3 · see citing works
Zhou, Z., Adrada, B. E., Candelaria, R. P., Elshafeey, N. A., Böge, M., Mohamed, R. M. M., Pashapoor, S., Sun, J., Xu, Z., Panthi, B., et al. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340987 · Cited by 3 · see citing works
Yan, Y., Liu, Y., Yao, J., Sui, L., Chen, C., Jiang, T., Liu, X., Wang, Y., Ou, D., Chen, J., et al. (2024) · British Journal of Radiology · doi:10.1093/bjr/tqae147 · Cited by 3 · see citing works
Self-citationSingh, S., & Healy, N. A. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01869-4 · Cited by 3 · see citing works
Oladimeji, O. O., Imran, A.-A.-Z., Wang, X., & Unnikrishnan, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105601 · Cited by 4 · see citing works
Tong, Y., Wei, Y., Sun, P., & Chang, C. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01818-7 · Cited by 3 · see citing works
Pawłowska, A., Żołek, N., Leśniak-Plewińska, B., Dobruch‐Sobczak, K., Klimonda, Z., Piotrzkowska‐Wróblewska, H., & Litniewski, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104393 · Cited by 3 · see citing works
Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer
Gong, X., Liu, X., Xie, X., & Wang, Y. (2023) · Cancer Innovation · doi:10.1002/cai2.85 · Cited by 2 · see citing works
Zhang, M., Zha, H., Pan, J., Liu, X., Zong, M., Du, L., & Du, Y. L. (2024) · Clinical Breast Cancer · doi:10.1016/j.clbc.2024.03.012 · Cited by 2 · see citing works
Wu, J., Guo, Y., Wu, C., Wang, Z., Sun, Y., & Xu, D. (2024) · Journal of Investigative Surgery · doi:10.1080/08941939.2024.2436050 · Cited by 2 · see citing works
Sun, Y., Liao, Q., Fan, Y., Cui, C., Wang, Y., Yang, C., Hou, Y., & Zhao, D. (2025) · BMC Cancer · doi:10.1186/s12885-025-14004-3 · Cited by 2 · see citing works
Radiomics in clinical trials: perspectives on standardization
Nie, K., & Xiao, Y. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aca388 · Cited by 2 · see citing works
Ge, L., Wu, J., Jin, Y., Xu, D. M., & Wang, Z. (2024) · Technology in Cancer Research & Treatment · doi:10.1177/15330338241257424 · Cited by 2 · see citing works
Chen, L., Su, Y., Huang, Y., Tao, J., Huang, X., Li, K., Liu, D., & Zhang, J. (2025) · European Radiology · doi:10.1007/s00330-025-11593-2 · Cited by 1 · see citing works
Hu, M., Zhang, L., Wang, X., & Xiao, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-12825-7 · Cited by 1 · see citing works
Shen, J.-T., Chen, G.-Q., Lun, H., Huang, H.-F., Zhang, L., Li, L.-L., Deng, Y., Wu, H.-H., & Hu, Q. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1587879 · Cited by 1 · see citing works
Ultrasound-based radiomics for the evaluation of breast cancer
Sun, F.-Y., Meng, D.-L., Liu, L., Cao, X.-Q., Fu, L., Meng, L., Cui, X., & Pan, X.-F. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2025.1710405 · Cited by 1 · see citing works
Cai, Q., Gao, T., Zhou, L., Zhang, S., Chen, Y., Xu, X., Jiang, T., Zhang, J., Zhang, X., Feng, Z., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-48693-y · Cited by 1 · see citing works
Self-citation
- Authors
- Gu, J., Tong, T., He, C., Xu, M., Yang, X., Tian, J., Jiang, T., & Wang, K.
- Affiliations
- Chinese Academy of Sciences (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Corresponding author
- Jiang, Tianan
- Wang, Kun
- Venue
- European Radiology
- Published
- 2021-10-15
- Volume
- 32
- Issue
- 3
- Pages
- 2099-2109
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W3206781867
- Cited by
- 126
- References
- 37
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 28 · 2023: 41 · 2022: 17 · 2021: 2
- FWCI
- 11.60
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- European Radiology (publishedVersion)
- PubMed (publishedVersion)
- European Society of Radiology
- Funders
- Ministry of Science and Technology (2017YFA0205200)
- Key Technologies Research and Development Program (2018YFC0114900)
- National Natural Science Foundation of China (62027901, 81930053, 81227901, 82027803, 81971623)
- Chinese Academy of Sciences (YJKYYQ20180048, QYZDJ-SSW-JSC005)
- Youth Innovation Promotion Association CAS
- Project of High-Level Talents Team Introduction in Zhuhai City
- 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, neoadjuvant chemotherapy, treatment response prediction, deep learning radiomics, breast ultrasound, early treatment response prediction, prognostic imaging biomarkers, treatment cost reduction, treatment time reduction
- MeSH terms
- Deep Learning, Breast Neoplasms, Female, Humans, Prospective Studies, Retrospective Studies, Ultrasonography, Neoadjuvant Therapy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
