新辅助治疗后乳腺癌病理完全缓解的评估困境与手术降阶梯策略研究进展
New Advances in the Assessment of Pathologic Complete Response and Surgical De-Escalation Strategies after Neoadjuvant Therapy for Breast Cancer
摘要: 本文旨在综述新辅助治疗后乳腺癌病理完全缓解(pCR)评估所面临的影像学、病理取样与肿瘤生物学异质性挑战,系统评析放射组学、深度学习、多模态影像、液体活检(ctDNA)与多组学融合在pCR预测及其向腋窝与乳房手术降阶转换中取得的进展与局限。并指出pCR与长期预后在分子亚型间差异显著,单一指标难以作为普适降阶依据,因而需依托标准化影像/病理流程、多源数据融合与可解释性模型构建风险分层,并通过分层化、前瞻性多中心随机试验验证降阶策略的肿瘤学安全性和长期结局,最终实现以证据为本的个体化手术减侵入化路径。
Abstract: This review aims to summarize the challenges in assessing pathologic complete response (pCR) after neoadjuvant therapy for breast cancer, including limitations in imaging, pathological sampling, and tumor biological heterogeneity. It systematically reviews the advances and limitations of radiomics, deep learning, multimodal imaging, liquid biopsy (e.g., ctDNA), and multi-omics integration in predicting pCR and its role in guiding de-escalation of axillary and breast surgery. The article highlights that the association between pCR and long-term prognosis varies significantly across molecular subtypes, and a single indicator is insufficient as a universal basis for de-escalation. Therefore, it is necessary to establish risk stratification through standardized imaging/pathology workflows, multi-source data fusion, and interpretable models. Furthermore, stratified, prospective, multicenter randomized trials are needed to validate the oncological safety and long-term outcomes of de-escalation strategies, ultimately paving the way for evidence-based, minimally invasive surgical approaches.
文章引用:王秋雨, 马泳之, 岳林皓, 陈雨佳, 贾中明. 新辅助治疗后乳腺癌病理完全缓解的评估困境与手术降阶梯策略研究进展[J]. 临床医学进展, 2026, 16(2): 1456-1467. https://doi.org/10.12677/acm.2026.162533

参考文献

[1] Gerber, B., Schneeweiss, A., Möbus, V., Golatta, M., Tesch, H., Krug, D., et al. (2022) Pathological Response in the Breast and Axillary Lymph Nodes after Neoadjuvant Systemic Treatment in Patients with Initially Node-Positive Breast Cancer Correlates with Disease Free Survival: An Exploratory Analysis of the Geparocto Trial. Cancers, 14, Article No. 521. [Google Scholar] [CrossRef] [PubMed]
[2] Pusztai, L., Denkert, C., O’Shaughnessy, J., Cortes, J., Dent, R., McArthur, H., et al. (2024) Event-Free Survival by Residual Cancer Burden with Pembrolizumab in Early-Stage TNBC: Exploratory Analysis from KEYNOTE-522. Annals of Oncology, 35, 429-436. [Google Scholar] [CrossRef] [PubMed]
[3] Aldrich, J., Canning, M. and Bhave, M. (2023) Monitoring of Triple Negative Breast Cancer after Neoadjuvant Chemotherapy. Clinical Breast Cancer, 23, 832-834. [Google Scholar] [CrossRef] [PubMed]
[4] Malhaire, C., Selhane, F., Saint-Martin, M., Cockenpot, V., Akl, P., Laas, E., et al. (2023) Exploring the Added Value of Pretherapeutic MR Descriptors in Predicting Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy. European Radiology, 33, 8142-8154. [Google Scholar] [CrossRef] [PubMed]
[5] Liang, Y., Xu, H., Lin, J., Tang, W., Liu, X., Gan, K., et al. (2025) Multi-Modal Radiomics Model Based on Four Imaging Modalities for Predicting Pathological Complete Response to Neoadjuvant Treatment in Breast Cancer. BMC Cancer, 25, Article No. 985. [Google Scholar] [CrossRef] [PubMed]
[6] Umutlu, L., Kirchner, J., Bruckmann, N., Morawitz, J., Antoch, G., Ting, S., et al. (2022) Multiparametric 18F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Cancers, 14, Article No. 1727. [Google Scholar] [CrossRef] [PubMed]
[7] Zeng, Q., Ke, M., Zhong, L., Zhou, Y., Zhu, X., He, C., et al. (2023) Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy. Academic Radiology, 30, 1638-1647. [Google Scholar] [CrossRef] [PubMed]
[8] Gu, J., Tong, T., Xu, D., Cheng, F., Fang, C., He, C., et al. (2022) Deep Learning Radiomics of Ultrasonography for Comprehensively Predicting Tumor and Axillary Lymph Node Status after Neoadjuvant Chemotherapy in Breast Cancer Patients: A Multicenter Study. Cancer, 129, 356-366. [Google Scholar] [CrossRef] [PubMed]
[9] Guo, J., Chen, B., Cao, H., Dai, Q., Qin, L., Zhang, J., et al. (2024) Cross-Modal Deep Learning Model for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. NPJ Precision Oncology, 8, Article No. 189. [Google Scholar] [CrossRef] [PubMed]
[10] Liu, Y., Chen, Z., Chen, J., Shi, Z. and Fang, G. (2023) Pathologic Complete Response Prediction in Breast Cancer Lesion Segmentation and Neoadjuvant Therapy. Frontiers in Medicine, 10, Article ID: 1188207. [Google Scholar] [CrossRef] [PubMed]
[11] Huang, J., Zhang, J., Ang, L., Li, M., Zhao, M., Wang, Y., et al. (2023) Proposing a Novel Molecular Subtyping Scheme for Predicting Distant Recurrence-Free Survival in Breast Cancer Post-Neoadjuvant Chemotherapy with Close Correlation to Metabolism and Senescence. Frontiers in Endocrinology, 14, Article ID: 1265520. [Google Scholar] [CrossRef] [PubMed]
[12] Zhang, J., Wu, Q., Yin, W., Yang, L., Xiao, B., Wang, J., et al. (2023) Development and Validation of a Radiopathomic Model for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. BMC Cancer, 23, Article No. 431. [Google Scholar] [CrossRef] [PubMed]
[13] Shi, Z., Huang, X., Cheng, Z., Xu, Z., Lin, H., Liu, C., et al. (2023) Erratum for: MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology, 308, e222830. [Google Scholar] [CrossRef] [PubMed]
[14] Gilad, M., Partridge, S.C., Iima, M., MD, R.R. and Freiman, M. (2025) Radiomics-Based Machine Learning Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Physiologically Decomposed Diffusion-Weighted MRI. Radiology: Imaging Cancer, 7, e240312. [Google Scholar] [CrossRef] [PubMed]
[15] Hatamikia, S., George, G., Schwarzhans, F., Mahbod, A. and Woitek, R. (2024) Breast MRI Radiomics and Machine Learning-Based Predictions of Response to Neoadjuvant Chemotherapy—How Are They Affected by Variations in Tumor Delineation? Computational and Structural Biotechnology Journal, 23, 52-63. [Google Scholar] [CrossRef] [PubMed]
[16] Ye, Z., Yuan, J., Hong, D., Xu, P. and Liu, W. (2025) Multimodal Diagnostic Models and Subtype Analysis for Neoadjuvant Therapy in Breast Cancer. Frontiers in Immunology, 16, Article ID: 1559200. [Google Scholar] [CrossRef] [PubMed]
[17] Huang, Y., Zhu, T., Zhang, X., Li, W., Zheng, X., Cheng, M., et al. (2023) Longitudinal MRI-Based Fusion Novel Model Predicts Pathological Complete Response in Breast Cancer Treated with Neoadjuvant Chemotherapy: A Multicenter, Retrospective Study. eClinicalMedicine, 58, Article ID: 101899. [Google Scholar] [CrossRef] [PubMed]
[18] Huang, Y., Shi, Z., Zhu, T., Zhou, T., Li, Y., Li, W., et al. (2025) Longitudinal MRI‐Driven Multi‐Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer. Advanced Science, 12, Article ID: 2413702. [Google Scholar] [CrossRef] [PubMed]
[19] Li, Y., Fan, Y., Xu, D., Li, Y., Zhong, Z., Pan, H., et al. (2023) Deep Learning Radiomic Analysis of DCE-MRI Combined with Clinical Characteristics Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Frontiers in Oncology, 12, Article ID: 1041142. [Google Scholar] [CrossRef] [PubMed]
[20] Zhao, J., Li, D., Xiao, X., Accorsi, F., Marshall, H., Cossetto, T., et al. (2021) United Adversarial Learning for Liver Tumor Segmentation and Detection of Multi-Modality Non-Contrast MRI. Medical Image Analysis, 73, Article ID: 102154. [Google Scholar] [CrossRef] [PubMed]
[21] Matsuda, S., Irino, T., Kitagawa, Y., Okamura, A., Mayanagi, S., Booka, E., et al. (2025) Detection of Pathologic Complete Response Using Deep Neural Network-Based Endoscopic Evaluation in Patients with Esophageal Cancer Receiving Neoadjuvant Chemotherapy: A Nationwide Multicenter Retrospective Study from 46 Japanese Esophageal Centers. Esophagus, 22, 322-330. [Google Scholar] [CrossRef] [PubMed]
[22] Ramtohul, T., Lollivier, D., Spriet, J., Jin, M., Djerroudi, L., Gaillard, T., et al. (2025) Posttreatment MRI to Predict Pathologic Complete Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy. Radiology, 316, e243824. [Google Scholar] [CrossRef] [PubMed]
[23] Dong, M., Chen, J., Lu, N., Wang, S., Wei, W., Wang, Z., et al. (2025) Unraveling Breast Cancer Response to Neoadjuvant Chemotherapy through Integrated Genomic, Transcriptomic, and Circulating Tumor DNA Analysis. Breast Cancer Research, 27, Article No. 64. [Google Scholar] [CrossRef] [PubMed]
[24] Kim, M.H., Kim, G.M., Ahn, J.M., Ryu, W., Kim, S., Kim, J.H., et al. (2023) Copy Number Aberrations in Circulating Tumor DNA Enables Prognosis Prediction and Molecular Characterization of Breast Cancer. JNCI: Journal of the National Cancer Institute, 115, 1036-1049. [Google Scholar] [CrossRef] [PubMed]
[25] Liu, Z., Yu, B., Su, M., Yuan, C., Liu, C., Wang, X., et al. (2023) Construction of a Risk Stratification Model Integrating ctDNA to Predict Response and Survival in Neoadjuvant-Treated Breast Cancer. BMC Medicine, 21, Article No. 493. [Google Scholar] [CrossRef] [PubMed]
[26] Gonzalez-Ericsson, P.I., Wulfkhule, J.D., Gallagher, R.I., Sun, X., Axelrod, M.L., Sheng, Q., et al. (2021) Tumor-Specific Major Histocompatibility-II Expression Predicts Benefit to Anti-PD-1/l1 Therapy in Patients with Her2-Negative Primary Breast Cancer. Clinical Cancer Research, 27, 5299-5306. [Google Scholar] [CrossRef] [PubMed]
[27] Tang, Y., Xu, A., Xu, Z., Xie, J., Huang, W., Zhang, L., et al. (2025) Multi-Omics Analyses of the Heterogenous Immune Microenvironment in Triple-Negative Breast Cancer Implicate UQCRFS1 Potentiates Tumor Progression. Experimental Hematology & Oncology, 14, Article No. 85. [Google Scholar] [CrossRef] [PubMed]
[28] van Amstel, F.J.G., de Mooij, C.M., Simons, J.M., Mitea, C., van Diest, P.J., Nelemans, P.J., et al. (2024) Disease Extent According to Baseline [18f]fluorodeoxyglucose PET/CT and Molecular Subtype: Prediction of Axillary Treatment Response after Neoadjuvant Systemic Therapy for Breast Cancer. British Journal of Surgery, 111, znae203. [Google Scholar] [CrossRef] [PubMed]
[29] Han, L., Zhang, T., D’Angelo, A., van der Voort, A., Pinker-Domenig, K., Kok, M., et al. (2025) Exploring Personalized Neoadjuvant Therapy Selection Strategies in Breast Cancer: An Explainable Multi-Modal Response Model. eClinicalMedicine, 86, Article ID: 103356. [Google Scholar] [CrossRef] [PubMed]
[30] Mittendorf, E.A., Assaf, Z.J., Harbeck, N., Zhang, H., Saji, S., Jung, K.H., et al. (2025) Peri-Operative Atezolizumab in Early-Stage Triple-Negative Breast Cancer: Final Results and ctDNA Analyses from the Randomized Phase 3 Impassion031 Trial. Nature Medicine, 31, 2397-2404. [Google Scholar] [CrossRef] [PubMed]
[31] Capasso, K., Mitri, S., Roldan-Vasquez, E., Flores, R., Bhasin, S., Borgonovo, G., et al. (2024) Axillary De-Escalation after Neoadjuvant Chemotherapy for Advanced Lymph Node Involvement in Breast Cancer. The American Journal of Surgery, 236, Article ID: 115893. [Google Scholar] [CrossRef] [PubMed]
[32] Alamoodi, M., Wazir, U., Mokbel, K., Patani, N., Varghese, J. and Mokbel, K. (2023) Omitting Sentinel Lymph Node Biopsy after Neoadjuvant Systemic Therapy for Clinically Node Negative HER2 Positive and Triple Negative Breast Cancer: A Pooled Analysis. Cancers, 15, Article No. 3325. [Google Scholar] [CrossRef] [PubMed]
[33] Grašič Kuhar, C., Geiger, J., Schwab, F.D., Heinzelmann-Schwartz, V., Vetter, M., Weber, W.P., et al. (2024) Prognostic Importance of Axillary Lymph Node Response to Neoadjuvant Systemic Therapy on Axillary Surgery in Breast Cancer—A Single Center Experience. Cancers, 16, Article No. 1306. [Google Scholar] [CrossRef] [PubMed]
[34] Tinterri, C., Barbieri, E., Sagona, A., Di Maria Grimaldi, S. and Gentile, D. (2024) De-Escalation of Axillary Surgery in Clinically Node-Positive Breast Cancer Patients Treated with Neoadjuvant Therapy: Comparative Long-Term Outcomes of Sentinel Lymph Node Biopsy versus Axillary Lymph Node Dissection. Cancers, 16, Article No. 3168. [Google Scholar] [CrossRef] [PubMed]
[35] Banys-Paluchowski, M., Gasparri, M., de Boniface, J., Gentilini, O., Stickeler, E., Hartmann, S., et al. (2021) Surgical Management of the Axilla in Clinically Node-Positive Breast Cancer Patients Converting to Clinical Node Negativity through Neoadjuvant Chemotherapy: Current Status, Knowledge Gaps, and Rationale for the EUBREAST-03 AXSANA Study. Cancers, 13, Article No. 1565. [Google Scholar] [CrossRef] [PubMed]
[36] Shin, E., Yoo, T., Kim, J., Chung, I.Y., Ko, B.S., Kim, H.J., et al. (2025) Evaluating the Survival Outcomes in Clinical Node Stage 2 and 3 Breast Cancer Patients with Negative Sentinel Lymph Node Biopsy after Neoadjuvant Chemotherapy: Sentinel Lymph Node Biopsy Alone vs. Axillary Lymph Node Dissection. Frontiers in Oncology, 15, Article ID: 1563586. [Google Scholar] [CrossRef] [PubMed]
[37] Bhardwaj, P.V., Wang, Y., Brunk, E., Spanheimer, P.M. and Abdou, Y.G. (2023) Advances in the Management of Early-Stage Triple-Negative Breast Cancer. International Journal of Molecular Sciences, 24, Article No. 12478. [Google Scholar] [CrossRef] [PubMed]
[38] Aktas, A., Gunay-Gurleyik, M., Aker, F., Kaan-Akgok, Y. and Atag, E. (2023) La quimioterapia neoadyuvante proporciona algún beneficio para la desescalada quirúrgica en el cáncer de mama HER2 (Ó?) luminal B? Cirugía y Cirujanos, 91, Article No. 9382. [Google Scholar] [CrossRef] [PubMed]
[39] Tinterri, C., Barbieri, E., Sagona, A., Bottini, A., Canavese, G. and Gentile, D. (2024) De-Escalation Surgery in cT3-4 Breast Cancer Patients after Neoadjuvant Therapy: Predictors of Breast Conservation and Comparison of Long-Term Oncological Outcomes with Mastectomy. Cancers, 16, Article No. 1169. [Google Scholar] [CrossRef] [PubMed]
[40] Connors, C., Valente, S.A., ElSherif, A., Escobar, P., Chichura, A., Kopicky, L., et al. (2024) Real-World Outcomes with the KEYNOTE-522 Regimen in Early-Stage Triple-Negative Breast Cancer. Annals of Surgical Oncology, 32, 912-921. [Google Scholar] [CrossRef] [PubMed]
[41] Park, W.K., Nam, S.J., Kim, S.W., Yu, J., Lee, S.K., Ryu, J.M., et al. (2025) Real-World Evidence of the Efficacy of Neoadjuvant Pembrolizumab in Triple-Negative Breast Cancer: A Surgeon’s Point of View. European Journal of Surgical Oncology, 51, Article ID: 110011. [Google Scholar] [CrossRef] [PubMed]
[42] He, M., Hao, S., Ma, L., Xiu, B., Yang, B., Wang, Z., et al. (2024) Neoadjuvant Anthracycline Followed by Toripalimab Combined with Nab-Paclitaxel in Patients with Early Triple-Negative Breast Cancer (NeoTENNIS): A Single-Arm, Phase II Study. eClinicalMedicine, 74, Article ID: 102700. [Google Scholar] [CrossRef] [PubMed]
[43] Zhang, Q., Wang, M., Li, Y., Zhang, H., Wang, Y., Chen, X., et al. (2025) Efficacy, Safety and Exploratory Analysis of Neoadjuvant Tislelizumab (a PD-1 Inhibitor) Plus Nab-Paclitaxel Followed by Epirubicin/Cyclophosphamide for Triple-Negative Breast Cancer: A Phase 2 TREND Trial. Signal Transduction and Targeted Therapy, 10, Article No. 169. [Google Scholar] [CrossRef] [PubMed]
[44] Loibl, S., Schneeweiss, A., Huober, J., Braun, M., Rey, J., Blohmer, J.-., et al. (2022) Neoadjuvant Durvalumab Improves Survival in Early Triple-Negative Breast Cancer Independent of Pathological Complete Response. Annals of Oncology, 33, 1149-1158. [Google Scholar] [CrossRef] [PubMed]
[45] Spring, L.M., Tolaney, S.M., Fell, G., Bossuyt, V., Abelman, R.O., Wu, B., et al. (2024) Response-Guided Neoadjuvant Sacituzumab Govitecan for Localized Triple-Negative Breast Cancer: Results from the NeoSTAR Trial. Annals of Oncology, 35, 293-301. [Google Scholar] [CrossRef] [PubMed]
[46] Ocean, A.J., Starodub, A.N., Bardia, A., Vahdat, L.T., Isakoff, S.J., Guarino, M., et al. (2017) Sacituzumab Govitecan (IMMU‐132), an Anti‐Trop‐2‐SN‐38 Antibody‐Drug Conjugate for the Treatment of Diverse Epithelial Cancers: Safety and Pharmacokinetics. Cancer, 123, 3843-3854. [Google Scholar] [CrossRef] [PubMed]
[47] Zhang, L., Yang, L., Ge, Y., Zhu, Z., Chen, B., Yang, C., et al. (2025) Neoadjuvant Anlotinib/Sintilimab plus Chemotherapy in Triple-Negative Breast Cancer (NeoSACT): Phase 2 Trial. Cell Reports Medicine, 6, Article ID: 102193. [Google Scholar] [CrossRef] [PubMed]
[48] Gandhi, S., Slomba, R.T., Janes, C., Fitzpatrick, V., Miller, J., Attwood, K., et al. (2024) Systemic Chemokine-Modulatory Regimen Combined with Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Journal for ImmunoTherapy of Cancer, 12, e010058. [Google Scholar] [CrossRef] [PubMed]
[49] Prat, A., Pascual, T., De Angelis, C., Gutierrez, C., Llombart-Cussac, A., Wang, T., et al. (2019) Her2-Enriched Subtype and ERBB2 Expression in Her2-Positive Breast Cancer Treated with Dual HER2 Blockade. JNCI: Journal of the National Cancer Institute, 112, 46-54. [Google Scholar] [CrossRef] [PubMed]
[50] Wu, S., Bian, L., Wang, H., Zhang, S., Wang, T., Yu, Z., et al. (2024) De-Escalation of Neoadjuvant Taxane and Carboplatin Therapy in Her2-Positive Breast Cancer with Dual HER2 Blockade: A Multicenter Real-World Experience in China. World Journal of Surgical Oncology, 22, Article No. 214. [Google Scholar] [CrossRef] [PubMed]
[51] Waks, A.G., Desai, N.V., Li, T., Poorvu, P.D., Partridge, A.H., Sinclair, N., et al. (2022) A Prospective Trial of Treatment De-Escalation Following Neoadjuvant Paclitaxel/Trastuzumab/Pertuzumab in Her2-Positive Breast Cancer. NPJ Breast Cancer, 8, Article No. 63. [Google Scholar] [CrossRef] [PubMed]
[52] Sharma, P., Stecklein, S.R., Yoder, R., Staley, J.M., Schwensen, K., O’Dea, A., et al. (2024) Clinical and Biomarker Findings of Neoadjuvant Pembrolizumab and Carboplatin plus Docetaxel in Triple-Negative Breast Cancer: NeoPACT Phase 2 Clinical Trial. JAMA Oncology, 10, Article No. 227. [Google Scholar] [CrossRef] [PubMed]
[53] Deng, H., Wang, L., Wang, N., Zhang, K., Zhao, Y., Qiu, P., et al. (2023) Neoadjuvant Checkpoint Blockade in Combination with Chemotherapy in Patients with Tripe-Negative Breast Cancer: Exploratory Analysis of Real-World, Multicenter Data. BMC Cancer, 23, Article No. 29. [Google Scholar] [CrossRef] [PubMed]