{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T10:44:53Z","timestamp":1782729893823,"version":"3.54.5"},"reference-count":27,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: This study aimed to evaluate the diagnostic performance of post-neoadjuvant chemotherapy (NAC) contrast-enhanced MRI in breast cancer treatment response assessment and to determine whether apparent diffusion coefficient (ADC) parameters contribute to predicting and confirming pathologic complete response (pCR). Methods: This retrospective cohort study enrolled patients with histopathologically confirmed invasive breast cancer who underwent breast MRI before and after NAC, followed by surgical resection. Post-NAC MRI response was classified into four categories and subsequently dichotomised into complete response versus residual disease. On diffusion-weighted imaging (DWI), pre-treatment minimum and maximum ADC values, post-treatment ADC, and the percentage change in ADC (\u0394ADC) were calculated. Diagnostic performance was assessed by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curve analysis, and Cohen\u2019s kappa coefficient. Results: Of 188 patients, 19.7% achieved pCR. Post-NAC MRI complete response predicted pCR with 100% sensitivity and 90.1% specificity, with a Cohen\u2019s kappa of 0.781 and 15 false-positive cases. Post-treatment ADC achieved the highest predictive performance, with an area under the ROC curve (AUC) of 0.967 (optimism-corrected 0.958); at the derived threshold, sensitivity was 100.0% and specificity 96.7%. \u0394ADC was likewise a statistically significant predictor of pCR, and post-treatment ADC remained independent after adjustment. Pre-treatment ADC parameters carried no meaningful predictive value in the full-cohort analysis. Conclusions: Post-treatment ADC and \u0394ADC help identify true complete responders among cases that contrast-enhanced MRI alone misclassifies as positive. Incorporating quantitative diffusion parameters into standard post-NAC MRI assessment may support pCR confirmation and warrants prospective external validation.<\/jats:p>","DOI":"10.3390\/jcm15135026","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T08:45:20Z","timestamp":1782722720000},"page":"5026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Accuracy of Post-Treatment ADC and MRI Response Assessment for Predicting Pathological Complete Response in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5039-9070","authenticated-orcid":false,"given":"Ela","family":"Kaplan","sequence":"first","affiliation":[{"name":"Department of Radiology, Faculty of Medicine, Adiyaman University, Ad\u0131yaman 02040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00fcseyin","family":"Alakus","sequence":"additional","affiliation":[{"name":"Departmant of Surgery Oncology, Faculty of Medicine, Adiyaman University, Ad\u0131yaman 02040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selcuk","family":"Kaplan","sequence":"additional","affiliation":[{"name":"Departmant of Gynecology and Obstetrics, Faculty of Medicine, Adiyaman University, Ad\u0131yaman 02040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12631","DOI":"10.21037\/qims-2025-1167","article-title":"Percentage apparent diffusion coefficient change for early prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis, and a retrospective study","volume":"15","author":"Ren","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cheng, Q., Huang, J., Liang, J., Ma, M., Ye, K., Shi, C., and Luo, L. (2020). The diagnostic performance of DCE-MRI in evaluating the pathological response to neoadjuvant chemotherapy in breast cancer: A meta-analysis. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.00093"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.clbc.2016.12.010","article-title":"Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: A meta-analysis","volume":"17","author":"Gu","year":"2017","journal-title":"Clin. Breast Cancer"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8217","DOI":"10.1007\/s00330-025-11640-y","article-title":"Comparison of diffusion-weighted and contrast-enhanced MRI for monitoring response to neoadjuvant therapy in breast cancer","volume":"35","author":"Eom","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Marinovich, M.L., Macaskill, P., Irwig, L., Sardanelli, F., Mamounas, E., von Minckwitz, G., Guarneri, V., Partridge, S.C., Wright, F.C., and Choi, J.H. (2015). Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: Individual patient data meta-analysis. BMC Cancer, 15.","DOI":"10.1186\/s12885-015-1664-4"},{"key":"ref_6","first-page":"011011","article-title":"Dynamic contrast-enhanced MRI and diffusion-weighted MRI for predicting the response of locally advanced breast cancer to neoadjuvant therapy: A meta-analysis","volume":"5","author":"Virostko","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_7","first-page":"e73469","article-title":"Ki-67 with MRI in predicting the complete pathological response post-neoadjuvant chemotherapy","volume":"16","author":"Kolli","year":"2024","journal-title":"Cureus"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"4089","DOI":"10.21037\/qims-22-869","article-title":"Nomogram based on quantitative dynamic contrast-enhanced magnetic resonance imaging, apparent diffusion coefficient, and clinicopathological features for early prediction of pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy","volume":"13","author":"He","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1038\/modpathol.2015.74","article-title":"Standardization of pathologic evaluation and reporting of postneoadjuvant specimens in clinical trials of breast cancer: Recommendations from an international working group","volume":"28","author":"Provenzano","year":"2015","journal-title":"Mod. Pathol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1007\/s00330-023-09547-7","article-title":"Imaging findings for response evaluation of ductal carcinoma in situ in breast cancer patients treated with neoadjuvant systemic therapy: A systematic review and meta-analysis","volume":"33","author":"Ploumen","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"197","DOI":"10.21037\/qims.2019.11.16","article-title":"The diagnostic accuracy of magnetic resonance imaging in predicting pathologic complete response after neoadjuvant chemotherapy in patients with different molecular subtypes of breast cancer","volume":"10","author":"Zhang","year":"2020","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1016\/j.acra.2025.12.004","article-title":"Discordance of Radiologic and Pathologic Complete Response in Patients with Triple-negative Breast Cancer Undergoing Neoadjuvant Chemotherapy","volume":"33","author":"Dhungana","year":"2025","journal-title":"Acad. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3394","DOI":"10.1158\/0008-5472.CAN-22-1329","article-title":"MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer","volume":"82","author":"Wu","year":"2022","journal-title":"Cancer Res."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Andreassen, M.M.S., Loubrie, S., Tong, M.W., Fang, L., Seibert, T.M., Wallace, A.M., Zare, S., Ojeda-Fournier, H., Kuperman, J., and Hahn, M. (2023). Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1237720"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Liang, X., Chen, X., Yang, Z., Liao, Y., Wang, M., Li, Y., Fan, W., Dai, Z., and Zhang, Y. (2022). Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast cancer. BMC Cancer, 22.","DOI":"10.1186\/s12885-022-10315-x"},{"key":"ref_20","first-page":"351","article-title":"Clinicopathological factors predicting pathological complete response to neoadjuvant anti-HER2 therapy in HER2-positive breast cancer","volume":"103","author":"Jung","year":"2025","journal-title":"Oncology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1007\/s12094-024-03440-5","article-title":"Real-world experience with pertuzumab and trastuzumab combined with chemotherapy in neoadjuvant treatment for patients with early-stage HER2-positive breast cancer: The NEOPERSUR study","volume":"26","author":"Jurado","year":"2024","journal-title":"Clin. Transl. Oncol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fang, S., Xia, W., Zhang, H., Ni, C., Wu, J., Mo, Q., Jiang, M., Guan, D., Yuan, H., and Chen, W. (2024). A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1323226"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"858","DOI":"10.18621\/eurj.1704067","article-title":"Evaluating the role of HER2-low versus HER2-0 status in predicting response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer","volume":"11","author":"Topal","year":"2025","journal-title":"Eur. Res. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1148\/radiol.211509","article-title":"Microcalcifications and peritumoral edema predict survival outcome in luminal breast cancer treated with neoadjuvant chemotherapy","volume":"304","author":"Kwon","year":"2022","journal-title":"Radiology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e87","DOI":"10.1016\/j.acra.2019.07.011","article-title":"Dynamic contrast-enhanced MRI evaluation of pathologic complete response in human epidermal growth factor receptor 2 (HER2)-positive breast cancer after HER2-targeted therapy","volume":"27","author":"Heacock","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1002\/jmri.28219","article-title":"Quantitative apparent diffusion coefficients from peritumoral regions as early predictors of response to neoadjuvant systemic therapy in triple-negative breast cancer","volume":"56","author":"Musall","year":"2022","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103737","DOI":"10.1016\/j.breast.2024.103737","article-title":"Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models","volume":"76","author":"Lin","year":"2024","journal-title":"Breast"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/13\/5026\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T09:52:23Z","timestamp":1782726743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/13\/5026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":27,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["jcm15135026"],"URL":"https:\/\/doi.org\/10.3390\/jcm15135026","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]}}}