Advances in imaging assessment of response to neoadjuvant therapy in breast cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

RONG Yan ,  XING Hua

Department of Breast Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, P. R. China;

Corresponding?author：

XING Hua, Email: xingh@jlu.edu.cn

Keywords：

breast cancer; neoadjuvant therapy; imaging assessment; pathological complete response; multimodal fusion

DOI：

10.7507/1007-9424.202604011

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To summarize the current research progress of imaging in evaluating the efficacy of neoadjuvant therapy for breast cancer, analyze the clinical application value and limitations of conventional imaging modalities, and explore the application prospects of advanced technologies such as multimodal fusion, radiomics, and artificial intelligence in efficacy prediction. Method By searching recent domestic and international literature on imaging evaluation of neoadjuvant therapy for breast cancer, this review summarizes traditional imaging evaluation methods and the applications of cutting-edge technologies such as multimodal fusion, radiomics, and deep learning. Results Traditional imaging methods play an important role in the evaluation of post-treatment efficacy, but there are inherent limitations. The morphology-based evaluation paradigm struggles to meet the clinical demand for early prediction and exhibits a significant gap compared with the histopathological gold standard. In recent years, the development of multimodal fusion, radiomics, and deep learning technologies has demonstrated promising efficacy in predicting pathologic complete response. However, most studies are single-center retrospective designs, and their external validation and interpretability still need to be strengthened. Conclusions Imaging enables both retrospective assessment and prospective prediction in evaluating the efficacy of neoadjuvant therapy for breast cancer. In the future, the establishment of standardized imaging acquisition and validation databases, the development of hybrid models that combine predictive performance with interpretability, and the promotion of the deep integration of imaging with multi-omics data will be important directions for clinical transformation.

Citation： RONG Yan, XING Hua. Advances in imaging assessment of response to neoadjuvant therapy in breast cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(7): 985-994. doi: 10.7507/1007-9424.202604011 Copy

1.
2.
3.
4.
5.
6.
7.	Albasini S, Rossetti C, Brancaccio R, et al. Post-neoadjuvant chemotherapy axillary ultrasound in cN+ breast cancer: can it reliably support de-escalation of axillary surgery? Cancer Med, 2026, 15(4): e71759. doi: 10.1002/cam4.71759.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.	Kim EY, Do SI, Yun JS, et al. Preoperative evaluation of mammographic microcalcifications after neoadjuvant chemotherapy for breast cancer. Clin Radiol, 2020, 75(8): 641.e19-641.e27.
37.
38.
39.	Thompson BM, Chala LF, Shimizu C, et al. Pre-treatment MRI tumor features and post-treatment mammographic findings: may they contribute to refining the prediction of pathologic complete response in post-neoadjuvant breast cancer patients with radiologic complete response on MRI? Eur Radiol, 2022, 32(3): 1663-1675.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.	Zhang J, Li Y, Li Z, et al. Deep-learning-based HER2 status assessment from multimodal breast cancer data predicts neoadjuvant therapy response. Nat Biomed Engm, 2025. doi: 10.1038/s41551-025-01495-5.
51.
52.
53.
54.
55.
56.
57.
58.
59.	Paquier Z, Chao SL, Acquisto A, et al. Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values. Biomed Phys Eng Express, 2022, 8(6): 065008. doi: 10.1088/2057-1976/ac8e6f.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.

1.
2.
3.
4.
5.
6.
7. Albasini S, Rossetti C, Brancaccio R, et al. Post-neoadjuvant chemotherapy axillary ultrasound in cN+ breast cancer: can it reliably support de-escalation of axillary surgery? Cancer Med, 2026, 15(4): e71759. doi: 10.1002/cam4.71759.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36. Kim EY, Do SI, Yun JS, et al. Preoperative evaluation of mammographic microcalcifications after neoadjuvant chemotherapy for breast cancer. Clin Radiol, 2020, 75(8): 641.e19-641.e27.
37.
38.
39. Thompson BM, Chala LF, Shimizu C, et al. Pre-treatment MRI tumor features and post-treatment mammographic findings: may they contribute to refining the prediction of pathologic complete response in post-neoadjuvant breast cancer patients with radiologic complete response on MRI? Eur Radiol, 2022, 32(3): 1663-1675.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50. Zhang J, Li Y, Li Z, et al. Deep-learning-based HER2 status assessment from multimodal breast cancer data predicts neoadjuvant therapy response. Nat Biomed Engm, 2025. doi: 10.1038/s41551-025-01495-5.
51.
52.
53.
54.
55.
56.
57.
58.
59. Paquier Z, Chao SL, Acquisto A, et al. Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values. Biomed Phys Eng Express, 2022, 8(6): 065008. doi: 10.1088/2057-1976/ac8e6f.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Advances in imaging assessment of response to neoadjuvant therapy in breast cancer

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content