Artificial Intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges_Journal of Biomedical Engineering

Authors：

HE Sunwei ¹ , LI Xiujuan ² , XIE Yuanzhong ² , HOU Jixue ³ , HAN Baosan ⁴ ,  NIE Shengdong ¹

1. Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Medical Imaging Center, Tai’an Central Hospital Affiliated with Qingdao University, Tai’an, Shandong 271000, P. R. China;
3. Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang 832000, P. R. China;
4. Department of Breast Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, P. R. China;

Corresponding?author：

Keywords：

Breast Cancer; Neoadjuvant Chemotherapy; Pathological Complete Response; Deep Learning; Treatment Response Prediction

DOI：

10.7507/1001-5515.202503075

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the rising incidence of breast cancer among women, Neoadjuvant Chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting Pathological Complete Response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.

1.	Shaikh K, Mooghal M, Ameen A, et al. Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review. J Pak Med Assoc, 2024, 74(4): S43-S48.
2.	Akram M, Iqbal M, Daniyal M, et al. Awareness and current knowledge of breast cancer. Biol Res, 2017, 50: 1-23.
3.	Corti C, Batra-Sharma H, Kelsten M, et al. Systemic therapy in breast cancer. Am Soc Clin Oncol Educ Book, 2024, 44(3): e432442.
4.	Mauri D, Pavlidis N, Ioannidis J P A. Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. J Natl Cancer Inst, 2005, 97(3): 188-194.
5.	Fu Y, Lei Y T, Huang Y H, et al. Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Eur Radiol, 2024, 34(11): 7080-7089.
6.	Xie L, Wang Y, Wan A, et al. Research trends of neoadjuvant therapy for breast cancer: a bibliometric analysis. Hum Vaccin Immunother, 2025, 21(1): 2460272.
7.	Li F, Yang Y, Wei Y, et al. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med, 2021, 19: 1-13.
8.	董春桐, 毛寧, 謝海柱, 等. 影像組學及深度學習在預測乳腺癌新輔助化療療效中的研究進展. 醫學影像學雜志, 2023, 33(4): 652-656.
9.	Pfob A, Heil J. Artificial intelligence to de-escalate loco-regional breast cancer treatment. Breast, 2023, 68: 201-204.
10.	Li M, Xiong X, Xu B. Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey. Front Digit Health, 2024, 6: 1371302.
11.	Zhou P, Qian H, Zhu P, et al. Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer. Front Oncol, 2025, 14: 1485681.
12.	龔俊峰, 王永杰, 丁宇, 等. 動態對比增強磁共振成像聯合人工智能預測乳腺癌新輔助化療療效的價值分析. 中國醫學裝備, 2024, 21(4): 55-59.
13.	王瑤, 聶芳. 基于超聲的人工智能在乳腺癌新輔助化療療效預測中的研究進展. 蘭州大學學報(醫學版), 2023, 49(9): 79-83.
14.	Shi Z, Huang X, Cheng Z, et al. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer. Radiology, 2023, 308(1): e222830.
15.	Zhang J, Wu Q, Yin W, et al. Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients. BMC Cancer, 2023, 23(1): 431.
16.	Zhou Z, Adrada B E, Candelaria R P, et al. Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs// 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney: IEEE, 2023: 1-4.
17.	Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14-14.
18.	Kazerouni A, Aghdam E K, Heidari M, et al. Diffusion models in medical imaging: a comprehensive survey. Med Image Anal, 2023, 88: 102846.
19.	Yin X X, Hadjiloucas S, Zhang Y, et al. MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. Comput Methods Programs Biomed, 2022, 214: 106510.
20.	Gao Y, Tan T, Wang X, et al. Multi-modal longitudinal representation learning for predicting neoadjuvant therapy response in breast cancer treatment. IEEE J Biomed Health Inform, 2025. DOI: 10.1109/JBHI.2025.3540574.
21.	趙曉暉, 傅騰超, 吳琍. 基于LASSO回歸構建乳腺癌患者新輔助化療療效的預測模型及列線圖. 臨床醫學進展, 2022, 12(11): 10290-10298.
22.	段艷莉, 楊光杰, 王洋洋, 等. 基于CT影像組學數據構建的綜合模型預測乳腺癌新輔助化療療效價值的研究. 精準醫學雜志, 2021, 36(6): 477-482.
23.	趙陽, 肖迎聰, 巨艷, 等. 基于自動乳腺超聲的列線圖模型早期預測HER-2陽性乳腺癌新輔助化療病理完全緩解的臨床價值. 中華臨床醫師雜志電子版, 2024, 18(4): 355-362.
24.	田婷婷, 李妮娜, 鐘征, 等. 臨床-影像聯合預測乳腺癌新輔助化療療效的價值. 中國普通外科雜志, 2024, 33(8): 1337-1342.
25.	Wang X, Chen B, Zhang H, et al. Integrative analysis identifies molecular features of fibroblast and the significance of fibrosis on neoadjuvant chemotherapy response in breast cancer. Int J Surg, 2024, 110(7): 4083-4095.
26.	H?berle L, Erber R, Gass P, et al. Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers. Breast Cancer Res, 2025, 27(1): 13.
27.	Liu H Q, Lin S Y, Song Y D, et al. Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy. Eur Radiol, 2023, 33(4): 2965-2974.
28.	Gullo R L, Eskreis-Winkler S, Morris E A, et al. Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy. Breast, 2020, 49: 115-122.
29.	Meti N, Saednia K, Lagree A, et al. Machine learning frameworks to predict neoadjuvant chemotherapy response in breast cancer using clinical and pathological features. JCO Clin Cancer Inform, 2021, 5: 66-80.
30.	Sammut S J, Crispin-Ortuzar M, Chin S F, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature, 2022, 601(7894): 623-629.
31.	Wang Y, Wang M, Yu K, et al. A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity. Cancer Biol Med, 2023, 20(3): 218-228.
32.	Chtouki K, Rhanoui M, Mikram M, et al. Supervised machine learning for breast cancer risk factors analysis and survival prediction// International Conference On Big Data and Internet of Things. Cham: Springer International Publishing, 2022: 59-71.
33.	Bulut G, Atilgan H I, ??narer G, et al. Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT. PLoS One, 2023, 18(9): e0290543.
34.	Jiang M, Li C L, Luo X M, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. Eur J Cancer, 2021, 147: 95-105.
35.	F?rnvik D, Borgquist S, Larsson M, et al. Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy. Eur J Radiol, 2024, 178: 111624.
36.	Yu F H, Miao S M, Li C Y, et al. Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Eur Radiol, 2023, 33(8): 5634-5644.
37.	El Adoui M, Drisis S, Benjelloun M. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. Int J Comput Assist Radiol Surg, 2020, 15: 1491-1500.
38.	Arledge C A, Zhao A H, Topaloglu U, et al. Dynamic contrast enhanced MRI mapping of vascular permeability for evaluation of breast cancer neoadjuvant chemotherapy response using image-to-image conditional generative adversarial networks. medRxiv, 2024: 2024.09.04.24313070.
39.	Lv M, Zhao B X, Mao Y, et al. Deep learning model for the early prediction of pathologic response following neoadjuvant chemotherapy in breast cancer patients using dynamic contrast-enhanced MRI. Front Oncol, 2025, 15: 1491843.
40.	Tang W, Jin C, Kong Q, et al. Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study. eClinicalMedicine, 2025, 85: 103298.
41.	Comes M C, Fanizzi A, Bove S, et al. Monitoring over time of pathological complete response to neoadjuvant chemotherapy in breast cancer patients through an ensemble vision transformers-based model. Cancer Med, 2024, 13(24): e70482.
42.	Byra M, Dobruch-Sobczak K, Piotrzkowska-Wroblewska H, et al. Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals. Phys Med Biol, 2022, 67(18): 185007.
43.	Jing B, Wang K, Schmitz E, et al. Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification. Med Phys, 2024, 51(12): 9385-9393.
44.	Mao N, Dai Y, Zhou H, et al. A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. Sci Adv, 2025, 11(18): eadr1576.
45.	Joo S, Ko E S, Kwon S, et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci Rep, 2021, 11(1): 18800.
46.	Gao Y, Ventura-Diaz S, Wang X, et al. An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer. Nat Commun, 2024, 15(1): 9613.
47.	Nishizawa T, Maldjian T, Jiao Z, et al. Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer. J Transl Med, 2025, 23(1): 774.
48.	Verma M, Abdelrahman L, Collado-Mesa F, et al. Multimodal spatiotemporal deep learning framework to predict response of breast cancer to neoadjuvant systemic therapy. Diagnostics, 2023, 13(13): 2251.
49.	Huang Z, Shao W, Han Z, et al. Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images. NPJ Precis Oncol, 2023, 7(1): 14.
50.	Velarde O M, Lin C, Eskreis-Winkler S, et al. Robustness of deep networks for mammography: replication across public datasets. J Imaging Inform Med, 2024, 37(2): 536-546.
51.	Rezaeian O, Asan O, Bayrak A E. The impact of AI explanations on clinicians’ trust and diagnostic accuracy in breast cancer. Appl Ergon, 2025, 129: 104577.

1. Shaikh K, Mooghal M, Ameen A, et al. Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review. J Pak Med Assoc, 2024, 74(4): S43-S48.
2. Akram M, Iqbal M, Daniyal M, et al. Awareness and current knowledge of breast cancer. Biol Res, 2017, 50: 1-23.
3. Corti C, Batra-Sharma H, Kelsten M, et al. Systemic therapy in breast cancer. Am Soc Clin Oncol Educ Book, 2024, 44(3): e432442.
4. Mauri D, Pavlidis N, Ioannidis J P A. Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. J Natl Cancer Inst, 2005, 97(3): 188-194.
5. Fu Y, Lei Y T, Huang Y H, et al. Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Eur Radiol, 2024, 34(11): 7080-7089.
6. Xie L, Wang Y, Wan A, et al. Research trends of neoadjuvant therapy for breast cancer: a bibliometric analysis. Hum Vaccin Immunother, 2025, 21(1): 2460272.
7. Li F, Yang Y, Wei Y, et al. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med, 2021, 19: 1-13.
8. 董春桐, 毛寧, 謝海柱, 等. 影像組學及深度學習在預測乳腺癌新輔助化療療效中的研究進展. 醫學影像學雜志, 2023, 33(4): 652-656.
9. Pfob A, Heil J. Artificial intelligence to de-escalate loco-regional breast cancer treatment. Breast, 2023, 68: 201-204.
10. Li M, Xiong X, Xu B. Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey. Front Digit Health, 2024, 6: 1371302.
11. Zhou P, Qian H, Zhu P, et al. Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer. Front Oncol, 2025, 14: 1485681.
12. 龔俊峰, 王永杰, 丁宇, 等. 動態對比增強磁共振成像聯合人工智能預測乳腺癌新輔助化療療效的價值分析. 中國醫學裝備, 2024, 21(4): 55-59.
13. 王瑤, 聶芳. 基于超聲的人工智能在乳腺癌新輔助化療療效預測中的研究進展. 蘭州大學學報(醫學版), 2023, 49(9): 79-83.
14. Shi Z, Huang X, Cheng Z, et al. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer. Radiology, 2023, 308(1): e222830.
15. Zhang J, Wu Q, Yin W, et al. Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients. BMC Cancer, 2023, 23(1): 431.
16. Zhou Z, Adrada B E, Candelaria R P, et al. Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs// 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney: IEEE, 2023: 1-4.
17. Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14-14.
18. Kazerouni A, Aghdam E K, Heidari M, et al. Diffusion models in medical imaging: a comprehensive survey. Med Image Anal, 2023, 88: 102846.
19. Yin X X, Hadjiloucas S, Zhang Y, et al. MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. Comput Methods Programs Biomed, 2022, 214: 106510.
20. Gao Y, Tan T, Wang X, et al. Multi-modal longitudinal representation learning for predicting neoadjuvant therapy response in breast cancer treatment. IEEE J Biomed Health Inform, 2025. DOI: 10.1109/JBHI.2025.3540574.
21. 趙曉暉, 傅騰超, 吳琍. 基于LASSO回歸構建乳腺癌患者新輔助化療療效的預測模型及列線圖. 臨床醫學進展, 2022, 12(11): 10290-10298.
22. 段艷莉, 楊光杰, 王洋洋, 等. 基于CT影像組學數據構建的綜合模型預測乳腺癌新輔助化療療效價值的研究. 精準醫學雜志, 2021, 36(6): 477-482.
23. 趙陽, 肖迎聰, 巨艷, 等. 基于自動乳腺超聲的列線圖模型早期預測HER-2陽性乳腺癌新輔助化療病理完全緩解的臨床價值. 中華臨床醫師雜志電子版, 2024, 18(4): 355-362.
24. 田婷婷, 李妮娜, 鐘征, 等. 臨床-影像聯合預測乳腺癌新輔助化療療效的價值. 中國普通外科雜志, 2024, 33(8): 1337-1342.
25. Wang X, Chen B, Zhang H, et al. Integrative analysis identifies molecular features of fibroblast and the significance of fibrosis on neoadjuvant chemotherapy response in breast cancer. Int J Surg, 2024, 110(7): 4083-4095.
26. H?berle L, Erber R, Gass P, et al. Prediction of pathological complete response after neoadjuvant chemotherapy for HER2-negative breast cancer patients with routine immunohistochemical markers. Breast Cancer Res, 2025, 27(1): 13.
27. Liu H Q, Lin S Y, Song Y D, et al. Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy. Eur Radiol, 2023, 33(4): 2965-2974.
28. Gullo R L, Eskreis-Winkler S, Morris E A, et al. Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy. Breast, 2020, 49: 115-122.
29. Meti N, Saednia K, Lagree A, et al. Machine learning frameworks to predict neoadjuvant chemotherapy response in breast cancer using clinical and pathological features. JCO Clin Cancer Inform, 2021, 5: 66-80.
30. Sammut S J, Crispin-Ortuzar M, Chin S F, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature, 2022, 601(7894): 623-629.
31. Wang Y, Wang M, Yu K, et al. A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity. Cancer Biol Med, 2023, 20(3): 218-228.
32. Chtouki K, Rhanoui M, Mikram M, et al. Supervised machine learning for breast cancer risk factors analysis and survival prediction// International Conference On Big Data and Internet of Things. Cham: Springer International Publishing, 2022: 59-71.
33. Bulut G, Atilgan H I, ??narer G, et al. Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT. PLoS One, 2023, 18(9): e0290543.
34. Jiang M, Li C L, Luo X M, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. Eur J Cancer, 2021, 147: 95-105.
35. F?rnvik D, Borgquist S, Larsson M, et al. Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy. Eur J Radiol, 2024, 178: 111624.
36. Yu F H, Miao S M, Li C Y, et al. Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Eur Radiol, 2023, 33(8): 5634-5644.
37. El Adoui M, Drisis S, Benjelloun M. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. Int J Comput Assist Radiol Surg, 2020, 15: 1491-1500.
38. Arledge C A, Zhao A H, Topaloglu U, et al. Dynamic contrast enhanced MRI mapping of vascular permeability for evaluation of breast cancer neoadjuvant chemotherapy response using image-to-image conditional generative adversarial networks. medRxiv, 2024: 2024.09.04.24313070.
39. Lv M, Zhao B X, Mao Y, et al. Deep learning model for the early prediction of pathologic response following neoadjuvant chemotherapy in breast cancer patients using dynamic contrast-enhanced MRI. Front Oncol, 2025, 15: 1491843.
40. Tang W, Jin C, Kong Q, et al. Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study. eClinicalMedicine, 2025, 85: 103298.
41. Comes M C, Fanizzi A, Bove S, et al. Monitoring over time of pathological complete response to neoadjuvant chemotherapy in breast cancer patients through an ensemble vision transformers-based model. Cancer Med, 2024, 13(24): e70482.
42. Byra M, Dobruch-Sobczak K, Piotrzkowska-Wroblewska H, et al. Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals. Phys Med Biol, 2022, 67(18): 185007.
43. Jing B, Wang K, Schmitz E, et al. Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification. Med Phys, 2024, 51(12): 9385-9393.
44. Mao N, Dai Y, Zhou H, et al. A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer. Sci Adv, 2025, 11(18): eadr1576.
45. Joo S, Ko E S, Kwon S, et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci Rep, 2021, 11(1): 18800.
46. Gao Y, Ventura-Diaz S, Wang X, et al. An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer. Nat Commun, 2024, 15(1): 9613.
47. Nishizawa T, Maldjian T, Jiao Z, et al. Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer. J Transl Med, 2025, 23(1): 774.
48. Verma M, Abdelrahman L, Collado-Mesa F, et al. Multimodal spatiotemporal deep learning framework to predict response of breast cancer to neoadjuvant systemic therapy. Diagnostics, 2023, 13(13): 2251.
49. Huang Z, Shao W, Han Z, et al. Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images. NPJ Precis Oncol, 2023, 7(1): 14.
50. Velarde O M, Lin C, Eskreis-Winkler S, et al. Robustness of deep networks for mammography: replication across public datasets. J Imaging Inform Med, 2024, 37(2): 536-546.
51. Rezaeian O, Asan O, Bayrak A E. The impact of AI explanations on clinicians’ trust and diagnostic accuracy in breast cancer. Appl Ergon, 2025, 129: 104577.

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Latest ArticlesArtificial Intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges

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