{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T09:09:29Z","timestamp":1788944969126,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"First Clinical College of Chongqing Medical University","award":["CYYY-BSYJSKYCXXM202412"],"award-info":[{"award-number":["CYYY-BSYJSKYCXXM202412"]}]},{"award":["CYYY-BSYJSKYCXXM202412"],"award-info":[{"award-number":["CYYY-BSYJSKYCXXM202412"]}],"id":[{"id":"https:\/\/ror.org\/017z00e58","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Curr. Oncol.","Current Oncology"],"abstract":"<jats:p>(1) Background: Neoadjuvant chemotherapy (NAC) is important for breast cancer, but prognosis varies widely. Ki67 and apparent diffusion coefficient (ADC) reflect proliferation and cellularity, respectively. This study evaluated the prognostic value of the Ki67\/ADC ratio (KA) and post-treatment ADC change (\u03b4ADC) in breast cancer patients receiving NAC, and developed a survival prediction model incorporating these indicators. (2) Methods: Two cohorts of breast cancer patients treated with NAC were collected. Pre- and post-treatment breast MRI with diffusion-weighted imaging were obtained; ADC values were measured by two blinded radiologists. KA was calculated as pre-treatment Ki67 divided by pre-treatment ADC, and \u03b4ADC as post-ADC minus pre-ADC. Disease-free survival (DFS) was the primary outcome. Cox regression and a predictive Cox model were used. (3) Results: A total of 419 patients were analyzed. Both KA and \u03b4ADC were associated with survival. In multivariable analysis, KA remained an independent prognostic factor (HR 0.40, 95% CI 0.19\u20130.84, p = 0.015). High KA was associated with worse prognosis, particularly in patients without pathological complete response. The model incorporating KA showed better predictive performance than clinicopathological variables alone and effectively stratified high- vs. low-risk patients. (4) Conclusion: KA is a promising complementary biomarker for prognosis in breast cancer patients undergoing NAC. Its integration into a prognostic model improved survival risk prediction and may aid individualized post-treatment management.<\/jats:p>","DOI":"10.3390\/curroncol33090534","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T15:39:47Z","timestamp":1788363587000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pretreatment Ki67-to-ADC Ratio Predicts Prognosis in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy: A Retrospective Cohort Study"],"prefix":"10.3390","volume":"33","author":[{"given":"Jun","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8221-4055","authenticated-orcid":false,"given":"Lin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjia","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudi","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6484-9738","authenticated-orcid":false,"given":"Fajin","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"477","article-title":"Breast cancer statistics 2024","volume":"74","author":"Giaquinto","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1200\/JCO.20.03399","article-title":"Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline","volume":"39","author":"Korde","year":"2021","journal-title":"J. Clin. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1093\/annonc\/mdx308","article-title":"De-escalating and escalating treatments for early-stage breast cancer: The St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017","volume":"28","author":"Curigliano","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/S1470-2045(09)70262-1","article-title":"Ki67 in breast cancer: Prognostic and predictive potential","volume":"11","author":"Yerushalmi","year":"2010","journal-title":"Lancet Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1097\/RLI.0000000000000465","article-title":"Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping for Breast Cancer Detection as a Stand-Alone Parameter: Comparison with Dynamic Contrast-Enhanced and Multiparametric Magnetic Resonance Imaging","volume":"53","author":"Pinker","year":"2018","journal-title":"Investig. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.clinimag.2023.03.016","article-title":"A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker","volume":"98","author":"Yao","year":"2023","journal-title":"Clin. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1007\/s00330-019-06510-3","article-title":"Diffusion-weighted imaging of the breast\u2014A consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group","volume":"30","author":"Baltzer","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1200\/JCO.19.02309","article-title":"Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO\/CAP Guideline Update","volume":"38","author":"Allison","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1002\/cac2.12191","article-title":"Estrogen receptor-low breast cancer: Biology chaos and treatment paradox","volume":"41","author":"Yu","year":"2021","journal-title":"Cancer Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1200\/JCO.2018.77.8738","article-title":"Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology\/College of American Pathologists Clinical Practice Guideline Focused Update","volume":"36","author":"Wolff","year":"2018","journal-title":"J. Clin. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1200\/JCO.19.02488","article-title":"HER2-Low Breast Cancer: Pathological and Clinical Landscape","volume":"38","author":"Tarantino","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1093\/jnci\/djp082","article-title":"Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer","volume":"101","author":"Cheang","year":"2009","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102615","DOI":"10.1016\/j.xcrm.2026.102615","article-title":"Spatial single-cell analysis reveals tumor microenvironment signatures predictive of oral cavity cancer outcome","volume":"7","author":"Nissi","year":"2026","journal-title":"Cell Rep. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1038\/s41598-023-27787-x","article-title":"Assessment of diffusion-weighted MRI in predicting response to neoadjuvant chemotherapy in breast cancer patients","volume":"13","author":"Hottat","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e240288","DOI":"10.1148\/radiol.240288","article-title":"Time-Dependent Diffusion MRI Helps Predict Molecular Subtypes and Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer","volume":"313","author":"Wang","year":"2024","journal-title":"Radiology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4067","DOI":"10.1007\/s00330-021-08462-z","article-title":"Value of diffusion-weighted MRI in predicting early response to neoadjuvant chemotherapy of breast cancer: Comparison between ROI-ADC and whole-lesion-ADC measurements","volume":"32","author":"Hottat","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5400","DOI":"10.1007\/s00330-023-09675-0","article-title":"Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database","volume":"33","author":"Bickel","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1148\/radiol.12111368","article-title":"In Vivo Assessment of Ductal Carcinoma in Situ Grade: A Model Incorporating Dynamic Contrast-enhanced and Diffusion-weighted Breast MR Imaging Parameters","volume":"263","author":"Rahbar","year":"2012","journal-title":"Radiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1148\/radiol.2018180273","article-title":"Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial","volume":"289","author":"Partridge","year":"2018","journal-title":"Radiology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1186\/s12967-021-02886-3","article-title":"Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: Evaluation with mono-, bi-, and stretched-exponential models","volume":"19","author":"Suo","year":"2021","journal-title":"J. Transl. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16372","DOI":"10.1038\/s41598-019-52785-3","article-title":"Diffusion-Weighted Magnetic Resonance Imaging of Patients with Breast Cancer Following Neoadjuvant Chemotherapy Provides Early Prediction of Pathological Response\u2014A Prospective Study","volume":"9","author":"Pereira","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Joint Head and Neck Radiotherapy-MRI Development Cooperative, Mohamed, A.S.R., Abusaif, A., He, R., Wahid, K.A., Salama, V., Youssef, S., McDonald, B.A., Naser, M., and Ding, Y. (2023). Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study. Radiother. Oncol., 183, 109641.","DOI":"10.1016\/j.radonc.2023.109641"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nrclinonc.2015.73","article-title":"Clinical management of breast cancer heterogeneity","volume":"12","author":"Zardavas","year":"2015","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1148\/radiol.2016151149","article-title":"Prediction of Low versus High Recurrence Scores in Estrogen Receptor\u2013Positive, Lymph Node\u2013Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores","volume":"280","author":"Dialani","year":"2016","journal-title":"Radiology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1148\/radiol.2019182510","article-title":"A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization","volume":"291","author":"Baxter","year":"2019","journal-title":"Radiology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s11547-015-0527-z","article-title":"MR mammography using diffusion-weighted imaging in evaluating breast cancer: A correlation with proliferation index","volume":"120","author":"Molinari","year":"2015","journal-title":"Radiol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1038\/s41523-024-00700-z","article-title":"A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk","volume":"10","author":"Goyal","year":"2024","journal-title":"npj Breast Cancer"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1148\/radiol.2017170143","article-title":"Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy","volume":"286","author":"Ueno","year":"2018","journal-title":"Radiology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"110220","DOI":"10.1016\/j.ejrad.2022.110220","article-title":"A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer","volume":"149","author":"Jimenez","year":"2022","journal-title":"Eur. J. Radiol."}],"container-title":["Current Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1718-7729\/33\/9\/534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T08:50:38Z","timestamp":1788943838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1718-7729\/33\/9\/534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,2]]},"references-count":31,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["curroncol33090534"],"URL":"https:\/\/doi.org\/10.3390\/curroncol33090534","relation":{},"ISSN":["1718-7729"],"issn-type":[{"value":"1718-7729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,2]]}}}