Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept
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Objectives: In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. Methods: Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. Results: A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. Conclusions: Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. Key Points: • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.
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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 [27]
The Materials and methods section (Neural networks; page unavailable) describes this paper’s detection and classification networks, but does not assess how many networks most previous studies used. The Discussion (AI and treatment response evaluation; page unavailable) compares prior studies’ results without saying they overlooked complementary advantages of multiple networks. Searches for single-network training and combinations of networks found no passage addressing that claim. (LLM, conf=0.73)
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.11.008 · Cited by 17,893 · see citing works
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010 · Cited by 17,796 · see citing works
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
Cortazar, P., Zhang, L., Untch, M., Mehta, K. M., Costantino, J. P., Wolmark, N., Bonnefoi, H. R., Cameron, D., Gianni, L., Valagussa, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62422-8 · Cited by 4,777 · see citing works
von Minckwitz, G., Untch, M., Blohmer, J.-U., Costa, S. D., Eidtmann, H., Fasching, P. A., Gerber, B., Eiermann, W., Hilfrich, J., Huober, J., et al. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2011.38.8595 · Cited by 2,659 · see citing works
Trastuzumab Emtansine for Residual Invasive HER2-Positive Breast Cancer
von Minckwitz, G., Huang, C., Mano, M. S., Loibl, S., Mamounas, E. P., Untch, M., Wolmark, N., Rastogi, P., Schneeweiß, A., Redondo, A., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1814017 · Cited by 2,647 · see citing works
Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
Cardoso, F., Kyriakides, S., Ohno, S., Penault-Llorca, F., Poortmans, P., Rubio, I. T., Zackrisson, S., & Senkus, E. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz173 · Cited by 2,214 · see citing works
Denkert, C., von Minckwitz, G., Darb-Esfahani, S., Lederer, B., Heppner, B. I., Weber, K. E., Budczies, J., Huober, J., Klauschen, F., Furlanetto, J., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30904-x · Cited by 2,157 · see citing works
Adjuvant Capecitabine for Breast Cancer after Preoperative Chemotherapy
Masuda, N., Lee, S.-J., Ohtani, S., Im, Y., Lee, E. S., Yokota, I., Kuroi, K., Im, S., Park, B., Kim, S., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1612645 · Cited by 1,900 · see citing works
Measurement of Residual Breast Cancer Burden to Predict Survival After Neoadjuvant Chemotherapy
Symmans, W. F., Peintinger, F., Hatzis, C., Rajan, R., Kuerer, H. M., Valero, V., Assad, L., Poniecka, A. W., Hennessy, B. T., Green, M. C., et al. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2007.10.6823 · Cited by 1,655 · see citing works
Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline
Korde, L. A., Somerfield, M. R., Carey, L., Crews, J. R., Denduluri, N., Hwang, E. S., Khan, S. A., Loibl, S., Morris, E. A., Perez, A., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.20.03399 · Cited by 1,056 · see citing works
A curated mammography data set for use in computer-aided detection and diagnosis research
Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data · doi:10.1038/sdata.2017.177 · Cited by 859 · see citing works
Burstein, H. J., Curigliano, G., Loibl, S., Dubsky, P. C., Gnant, M., Poortmans, P. M., Colleoni, M. A., Denkert, C., Piccart, M. J., Regan, M. M., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz235 · Cited by 672 · see citing works
Houssami, N., Macaskill, P., von Minckwitz, G., Marinovich, M. L., & Mamounas, E. P. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2012.05.023 · Cited by 554 · see citing works
Hylton, 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
Response-Guided Neoadjuvant Chemotherapy for Breast Cancer
von Minckwitz, G., Blohmer, J., Costa, S. D., Denkert, C., Eidtmann, H., EIERMANN, W., Gerber, B., Hanusch, C. A., Hilfrich, J., Huober, J., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.45.0940 · Cited by 415 · see citing works
Bidard, F., Michiels, S., Riethdorf, S., Müller, V., Esserman, L. J., Lucci, A., Naume, B., Horiguchi, J., Gisbert‐Criado, R., Sleijfer, S., et al. (2018) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djy018 · Cited by 278 · see citing works
Tahmassebi, A., Wengert, G., Helbich, T. H., Bagó-Horváth, Z., Alaei, S., Bartsch, R., Dubsky, P. C., Baltzer, P., Clauser, P., Kapetas, P., et al. (2018) · Investigative Radiology · doi:10.1097/rli.0000000000000518 · Cited by 274 · see citing works
Assessing Tumor Response to Therapy
Weber, W. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.057174 · Cited by 269 · see citing works
Kong, X., Moran, M. S., Zhang, N., Haffty, B. G., & Yang, Q. (2011) · European Journal of Cancer · doi:10.1016/j.ejca.2011.06.014 · Cited by 265 · see citing works
Bossuyt, V., Provenzano, E., Symmans, W. F., Boughey, J. C., Coles, C. E., Curigliano, G., Dixon, J. M., Esserman, L. J., Fastner, G., Kuehn, T., et al. (2015) · Annals of Oncology · doi:10.1093/annonc/mdv161 · Cited by 243 · see citing works
Cain, E. H., Saha, A., Harowicz, M. R., Marks, J. R., Marcom, P. K., & Mazurowski, M. A. (2018) · Breast Cancer Research and Treatment · doi:10.1007/s10549-018-4990-9 · Cited by 202 · see citing works
Mammographic density—a review on the current understanding of its association with breast cancer
Huo, C., Chew, G. L., Britt, K. L., Ingman, W. V., Henderson, M. A., Hopper, J. L., & Thompson, E. W. (2014) · Breast Cancer Research and Treatment · doi:10.1007/s10549-014-2901-2 · Cited by 199 · see citing works
Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome
Park, Y. H., Lal, S., Lee, J. E., Choi, Y., Wen, J., Ram, S., Ding, Y., Lee, S. H., Powell, E. L., Lee, S. K., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19933-0 · Cited by 190 · see citing works
Denduluri, N., Chávez‐MacGregor, M., Telli, M. L., Eisen, A. F., Graff, S. L., Hassett, M. J., Holloway, J. N., Hurria, A., King, T. A., Lyman, G. H., et al. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2018.78.8604 · Cited by 147 · see citing works
Ha, R. S., Chin, C., Karcich, J., Liu, M. Z., Chang, P., Mutasa, S., Sant, E. P. V., Wynn, R. T., Connolly, E. P., & Jambawalikar, S. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0144-1 · Cited by 132 · see citing works
Sutton, E. J., Onishi, N., Fehr, D., Dashevsky, B. Z., Sadinski, M., Pinker, K., Martinez, D. F., Brogi, E., Braunstein, L. Z., Razavi, P., et al. (2020) · Breast Cancer Research · doi:10.1186/s13058-020-01291-w · Cited by 102 · see citing works
Qu, Y., Zhu, H., Cao, K., Li, X., Ye, M., & Sun, Y. (2020) · Thoracic Cancer · doi:10.1111/1759-7714.13309 · Cited by 96 · see citing works
ACR Appropriateness Criteria ® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer
Slanetz, P. J., Moy, L. P., Baron, P. L., diFlorio‐Alexander, R. M., Green, E. D., Heller, S. L., Holbrook, A. I., Lee, S., Lewin, A. A., Lourenço, A. P., et al. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.037 · Cited by 83 · see citing works
Han, S., & Choi, J. Y. (2020) · Breast Cancer Research · doi:10.1186/s13058-020-01350-2 · Cited by 73 · see citing works
Liu, M. Z., Mutasa, S., Chang, P., Siddique, M., Jambawalikar, S., & Ha, R. S. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.08.021 · Cited by 50 · see citing works
Meti, N., Saednia, K., Lagree, A., Tabbarah, S., Mohebpour, M., Kiss, A., Lu, F., Slodkowska, E. A., Gandhi, S., Jerzak, K. J., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00078 · Cited by 49 · see citing works
Cardoso, F., Kyriakides, S., Ohno, S., Penault-Llorca, F. M., Poortmans, P. M., Rubio, I. T., Zackrisson, S., & Senkus, E. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.08.2158 · Cited by 48 · see citing works
Sinn, B. V., Loibl, S., Hanusch, C. A., Zahm, D.-M., Sinn, H., Untch, M., Weber, K. E., Karn, T., Becker, C., Marmé, F., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-3113 · Cited by 45 · see citing works
Chou, H., Kuo, W., Yu, C., Tsai, H.-P., Shen, S.-C., Chu, C., Yu, M., Lo, Y., Dabora, M. A., Chang, H., et al. (2019) · Biomedical Journal · doi:10.1016/j.bj.2018.10.007 · Cited by 44 · see citing works
Pu, S., Wang, K., Liu, Y., Liao, X., Chen, H., He, J., & Zhang, J. (2020) · BMC Cancer · doi:10.1186/s12885-020-07621-7 · Cited by 38 · see citing works
Gu, J., Polley, E. C., Denis, M. F., Carter, J. M., Pruthi, S., Gregory, A., Boughey, J. C., Fazzio, R. T., Fatemi, M., & Alizad, A. (2021) · Breast Cancer Research · doi:10.1186/s13058-021-01429-4 · Cited by 38 · see citing works
Wang, H., Zhang, S., Yee, D., Basu, S., Beckwith, H. C., Potter, D. A., & Blaes, A. H. (2021) · Breast Cancer · doi:10.1007/s12282-020-01194-w · Cited by 37 · see citing works
Skarping, I. D., Förnvik, D., Sartor, H., Heide‐Jørgensen, U., Zackrisson, S., & Borgquist, S. (2019) · BMC Cancer · doi:10.1186/s12885-019-6485-4 · Cited by 26 · see citing works
Self-citationForgia, D. L., Vestito, A., Lasciarrea, M., Comes, M. C., Diotaiuti, S., Giotta, F., Latorre, A. C., Lorusso, V., Massafra, R., Palmiotti, G., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11040256 · Cited by 26 · see citing works
Soliman, H. H., Wagner, S., Flake, D. D., Robson, M. E., Schwartzberg, L. S., Sharma, P., Magliocco, A. M., Kronenwett, R., Lancaster, J. M., Lanchbury, J. S. S., et al. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-019-08039-7 · Cited by 25 · see citing works
Skarping, I. D., Förnvik, D., Heide‐Jørgensen, U., Sartor, H., Hall, P. N., Zackrisson, S., & Borgquist, S. (2020) · The Breast · doi:10.1016/j.breast.2020.05.013 · Cited by 24 · see citing works
Self-citationSkarping, I. D., Förnvik, D., Heide‐Jørgensen, U., Sartor, H., Hall, P. N., Zackrisson, S., & Borgquist, S. (2020) · Cancer Causes & Control · doi:10.1007/s10552-020-01379-w · Cited by 20 · see citing works
Self-citationSkarping, I. D., Förnvik, D., Heide‐Jørgensen, U., Rydén, L., Zackrisson, S., & Borgquist, S. (2020) · Acta Oncologica · doi:10.1080/0284186x.2020.1830167 · Cited by 18 · see citing works
Self-citationTribukait, B., Bergh, J., & Hatschek, T. (2020) · BMC Cancer · doi:10.1186/s12885-020-06925-y · Cited by 14 · see citing works
How to avoid describing your radiological research study incorrectly
Halligan, S., Kenis, S. F., Abeyakoon, O., Plumb, A., & Mallett, S. (2020) · European Radiology · doi:10.1007/s00330-020-06720-0 · Cited by 8 · see citing works
Mohiuddin, J. J., Deal, A. M., Lund, J. L., Carey, L., Baker, B. R., Zagar, T. M., Jones, E. L., Marks, L. B., & Chen, R. C. (2017) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkx004 · Cited by 5 · see citing works
“Association of mammographic features with molecular breast tumor profiles”
Sartor, H., Zackrisson, S., Hegardt, C., & Larsson, C. (2021) · Cancer Treatment and Research Communications · doi:10.1016/j.ctarc.2021.100387 · Cited by 3 · see citing works
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Huang, Y., Zhu, T., Zhang, X., Li, W., Zheng, X., Cheng, M., Ji, F., Zhang, L., Yang, C., Wu, Z., et al. (2023) · eClinicalMedicine · doi:10.1016/j.eclinm.2023.101899 · Cited by 175 · see citing works
Gao, Y., Ventura‐Díaz, S., Wang, X., He, M., Xu, Z., Weir, A., Zhou, H.-Y., Zhang, T., van Duijnhoven, F. H., Han, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53450-8 · Cited by 93 · see citing works
Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions
Taylor, C. R., Monga, N., Johnson, C., Hawley, J. R., & Patel, M. J. (2023) · Diagnostics · doi:10.3390/diagnostics13122041 · Cited by 94 · see citing works
Cè, M., Caloro, E., Pellegrino, M. E., Basile, M., Sorce, A., Fazzini, D., Oliva, G., & Cellina, M. I. (2022) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2022.00113 · Cited by 57 · 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
Uchikov, P. A., Khalid, U., Dedaj-Salad, G. H., Ghale, D., Rajadurai, H., Kraeva, M. I., Kraev, K. I., Hristov, B. K., Doykov, M., Mitova, V., et al. (2024) · Life · doi:10.3390/life14111451 · Cited by 50 · 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 ).”
Uwimana, A., Gnecco, G., & Riccaboni, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109391 · Cited by 46 · see citing works
Artificial intelligence for breast cancer: Implications for diagnosis and management
AlSamhori, J. F., AlSamhori, A. R. F., Duncan, L. A., Qalajo, A., Alshahwan, H. F., Al-abbadi, M., Soudi, M. A., Zakraoui, R., AlSamhori, A. F., AlRyalat, S. A., et al. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100120 · Cited by 44 · see citing works
Weakly Supervised Deep Learning in Radiology
Misera, L., Müller‐Franzes, G. A., Truhn, D., & Kather, J. N. (2024) · Radiology · doi:10.1148/radiol.232085 · Cited by 39 · see citing works
Brahmareddy, A., & Selvan, M. P. (2025) · Scientific Reports · doi:10.1038/s41598-025-19173-6 · Cited by 37 · see citing works
Duwe, 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
Gamal, A., Sharafeldeen, A., Alnaghy, E., Alghandour, R., Alghamdi, N. S., Ali, K. M., Shamaa, S. S. A., Aboueleneen, A., Tolba, A. E., Elmougy, S., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3432459 · Cited by 21 · 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
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
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
A Pictorial Exploration of Mammary Paget Disease: Insights and Perspectives
Mariano, L., Nicosia, L., Pupo, D., Olivieri, A. M., Scolari, S., Pesapane, F., Latronico, A., Bozzini, A. C., Fusco, N., Blanco, M. C., et al. (2023) · Cancers · doi:10.3390/cancers15215276 · Cited by 14 · see citing works
Förnvik, D., Borgquist, S., Larsson, M., Zackrisson, S., & Skarping, I. D. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111624 · Cited by 12 · see citing works
Self-citationChen, Y., Hua, Z., Lin, F., Zheng, T., Zhou, H., Zhang, S., Gao, J., Wang, Z., Shao, H., Li, W., et al. (2023) · Chinese Journal of Cancer Research · doi:10.21147/j.issn.1000-9604.2023.04.07 · Cited by 12 · see citing works
Solodkiy, V. А., Каприн, А. Д., Нуднов, Н. В., Kharchenko, N. V., Ходорович, О. С., Магомедович, З. Г., Sherstneva, T. V., Dibirova, S. M., & Kanakhina, L. B. (2023) · Journal of radiology and nuclear medicine · doi:10.20862/0042-4676-2023-104-2-151-162 · Cited by 10 · see citing works
Lyu, P., Wang, Y., Meng, Q.-X., Fan, P.-M., Ma, K., Xiao, S., Cao, X.-C., Lin, G., & Dong, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.955668 · Cited by 8 · see citing works
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
Current status and prospects of breast cancer imaging-based diagnosis using artificial intelligence
Sekine, C., & Horiguchi, J. (2024) · International Journal of Clinical Oncology · doi:10.1007/s10147-024-02594-0 · Cited by 6 · see citing works
Kim, H., Choi, J. S., Ah, S., Ryu, J. M., Lee, J. E., Kim, M. K., Lee, J., Ko, E. S., Ko, E. Y., & Han, B. (2025) · European Radiology · doi:10.1007/s00330-025-11390-x · Cited by 6 · see citing works
Liu, J., Zhao, X., Hua, H., Lv, J., Lin, X., Fu, M., Sun, L., & Chen, J. (2026) · Cancer Imaging · doi:10.1186/s40644-025-00986-7 · Cited by 5 · see citing works
Liang, Y., Xu, H., Lin, J., Tang, W., Liu, X., Gan, K., Wan, Q., & Du, X. (2025) · BMC Cancer · doi:10.1186/s12885-025-14407-2 · Cited by 4 · see citing works
Corines, M. J., Christianson, B., Comstock, C., Drotman, M. B., & Dodelzon, K. (2026) · British Journal of Radiology · doi:10.1093/bjr/tqag053 · Cited by 3 · see citing works
Mao, K., Li, Z., Li, J., Xie, Y., Long, M., Ai, J., Zhang, L., Gao, D., Shen, D., Li, P., et al. (2026) · European Radiology · doi:10.1007/s00330-026-12526-3 · Cited by 3 · see citing works
Wang, X., Zhang, Y., Yang, M., Wu, N., Wang, S., Chen, H., Zhou, T., Zhang, Y., Wang, X., Jin, Z., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-80409-y · Cited by 3 · see citing works
Fan, M., Zhu, Z., Yu, Z., Du, J., Xie, S., Pan, X., Chen, S., & Li, L. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae077c · Cited by 2 · see citing works
Wang, K., Luo, Y., Zhang, P., Yang, B., & Tao, Y. (2025) · Diagnostics · doi:10.3390/diagnostics15212763 · Cited by 2 · see citing works
Kumar, U., Mishra, A., Singh, K. R., Parihar, A., Raja, N., Raam, M., Rahalkar, A., & Ramakant, P. (2025) · World Journal of Surgery · doi:10.1002/wjs.12502 · Cited by 2 · see citing works
Fu, M., Zhao, H., Pang, J., Hua, H., Lin, X., Liu, J., He, Y., Ming, X., Meng, X., & Chen, J. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109130 · Cited by 1 · see citing works
Suleymanova, M. M., Karmazanovsky, G. G., Kondratyev, E. V., Popov, A. Y., Nechaev, V. A., Ermoshchenkova, M. S., & Kuzmina, E. S. (2025) · Digital Diagnostics · doi:10.17816/dd634972 · Cited by 1 · see citing works
The role of artificial intelligence (AI) in the early detection of women-specific cancers
Patra, S., Shand, H., & Ghorai, S. (2026) · Discover Medicine · doi:10.1007/s44337-026-00594-9 · Cited by 1 · see citing works
A review of application of Artificial Intelligence in breast cancer detection and treatment
Siddiqui, A., Khobragade, K., Kautish, P., Siddiqui, M., & Marak, Z. R. (2026) · Discover Oncology · doi:10.1007/s12672-026-05010-3 · Cited by 1 · see citing works
A Method of Cell Counting Based on Computer Vision
Ding, S., Zhao, H., Han, Z. J., & Tang, Y. (2022) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber55403.2022.9907162
Duwe, G., Mercier, D., Wiesmann, C., Junker, M., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Springer eBooks · doi:10.1007/978-3-658-43860-9_36
Use of T1-weighted MRI in the Quantitative Feature Analysis of Breast Lesions
Sun, H., Wang, X., & Li, H. (2024) · International Conference on Signal Processing and Intelligent Computing (SPIC) · doi:10.1109/spic62469.2024.10691510
Park, G. E., Kang, B. J., Kim, S. H., & Mun, H. S. (2024) · Life · doi:10.3390/life14111449
Artificial Intelligence in Breast Imaging: Basic Principles for Radiologists
Rana, P., & Rangarajan, K. (2025) · Springer eBooks · doi:10.1007/978-981-97-9847-6_20
Gündoğdu, A., Ozdemir, N. B. U., Wetherilt, C. S., Aktaş, M., Gentile, D., Çomçalı, B., & Yilmaz, O. C. (2026) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-026-04116-0
Role of Gen AI for Invasive Breast Cancer Diagnosis
Chellathurai, A. S., & Narayanan, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-23626-5_10
- Authors
- Skarping, I., Larsson, M., & Förnvik, D.
- Affiliations
- Lund University (SE)
- Corresponding author
- Skarping, I.
- Venue
- European Radiology
- Published
- 2021-10-15
- Volume
- 32
- Issue
- 5
- Pages
- 3131-3141
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC9038782
- OpenAlex
- W3205757396
- Cited by
- 44
- References
- 51
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 14 · 2023: 8 · 2022: 3
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Radiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Swedish Breast Cancer Group
- Allmänna Sjukhusets i Malmö Stiftelse för Bekämpande av Cancer
- the Governmental Funding of Clinical Research within the National Health Services
- Lund University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- breast cancer, neoadjuvant chemotherapy, treatment response prediction, digital mammograms, pathological complete response, deep learning, computer-aided diagnosis, tumor response assessment, clinical decision support
- MeSH terms
- Artificial Intelligence, Breast Neoplasms, Female, Humans, Mammography, Middle Aged, Premenopause, Neoadjuvant Therapy
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