{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T15:34:22Z","timestamp":1766504062979,"version":"3.40.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Chongqing Regional Key Disciplines","award":["zdxk202116"],"award-info":[{"award-number":["zdxk202116"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01631-2","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T12:16:26Z","timestamp":1744632986000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of neoadjuvant chemotherapy efficacy in breast cancer: integrating multimodal imaging and clinical features"],"prefix":"10.1186","volume":"25","author":[{"given":"Xianglong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangsheng","family":"Mou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbing","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"issue":"3","key":"1631_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209\u201349. https:\/\/doi.org\/10.3322\/caac.21660","journal-title":"Cancer J Clin"},{"key":"1631_CR2","doi-asserted-by":"publisher","unstructured":"Maccoll CE, Guillaume Par\u00e9, Salehi A, et al. Postneoadjuvant pure and predominantly pure intralymphatic breast carcinoma: case series and literature review. Am J Surg Pathol. 2020. https:\/\/doi.org\/10.1097\/PAS.0000000000001610","DOI":"10.1097\/PAS.0000000000001610"},{"key":"1631_CR3","doi-asserted-by":"publisher","unstructured":"Hussein H, Abbas E, Keshavarzi S, et al. Supplemental breast cancer screening in women with dense breasts and negative mammography: a systematic review and meta-analysis[J\/OL]. Radiology. 2023;306(3). https:\/\/doi.org\/10.1148\/radiol.221785","DOI":"10.1148\/radiol.221785"},{"key":"1631_CR4","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ejrad.2019.03.010","volume":"114","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Li M, Wang J, et al. Role of dynamic contrast-enhanced magnetic resonance imaging in assessing breast cancer: evaluation of the morphological characteristics and biological behavior. Eur J Radiol. 2019;114:38\u201345. https:\/\/doi.org\/10.1016\/j.ejrad.2019.03.010","journal-title":"Eur J Radiol"},{"issue":"1","key":"1631_CR5","doi-asserted-by":"publisher","first-page":"46","DOI":"10.2214\/AJR.20.23211","volume":"215","author":"J Cai","year":"2020","unstructured":"Cai J, Zhang L, Yu L. Clinical applications of diffusion kurtosis imaging in oncology. Am J Roentgenol. 2020;215(1):46\u201355. https:\/\/doi.org\/10.2214\/AJR.20.23211","journal-title":"Am J Roentgenol"},{"key":"1631_CR6","doi-asserted-by":"publisher","unstructured":"Comparison of the pre-treatment functional MRI metrics\u2019 efficacy in predicting locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy. Cancer Imaging. 2021;21(1):1\u201312.https:\/\/doi.org\/10.1186\/s40644-021-00428-0","DOI":"10.1186\/s40644-021-00428-0"},{"key":"1631_CR7","doi-asserted-by":"publisher","unstructured":"Fusco R, Sansone M. Granata V, et al. Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among standardized index of shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters. Abdom Radiol. 2019;44(11):3683\u2013700. https:\/\/doi.org\/10.1007\/s00261-018-1801-z","DOI":"10.1007\/s00261-018-1801-z"},{"key":"1631_CR8","doi-asserted-by":"publisher","unstructured":"Granata V, Fusco R, Sansone M, et al. Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging. Therapeutic Adv Gastroenterol. 2019;175628481988505. https:\/\/doi.org\/10.1177\/1756284819885052","DOI":"10.1177\/1756284819885052"},{"key":"1631_CR9","doi-asserted-by":"publisher","unstructured":"Li H, Zhao S, Hai F. The effect of histogram analysis of DCE-MRI parameters on differentiating renal tumors. Clin Lab. 2023;69(11). https:\/\/doi.org\/10.7754\/Clin.Lab.2023.221126","DOI":"10.7754\/Clin.Lab.2023.221126"},{"key":"1631_CR10","doi-asserted-by":"publisher","unstructured":"Li Q, Xiao Q, Yang M, et al. Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer. Eur J Radiol. 2021;(3):109697. https:\/\/doi.org\/10.1016\/j.ejrad.2021.109697","DOI":"10.1016\/j.ejrad.2021.109697"},{"issue":"4","key":"1631_CR11","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s12035-018-1413-6","volume":"7","author":"R Smith","year":"2018","unstructured":"Smith R, Williams P. Role of imaging in evaluating neoadjuvant chemotherapy response in breast cancer. Med Imaging Diagnosis. 2018;7(4):58\u201365. https:\/\/doi.org\/10.1007\/s12035-018-1413-6","journal-title":"Med Imaging Diagnosis"},{"key":"1631_CR12","doi-asserted-by":"publisher","first-page":"153677","DOI":"10.1016\/j.prp.2021.153677","volume":"228","author":"D Zhao","year":"2021","unstructured":"Zhao D, Fu X, Rohr J, et al. Poor histologic tumor response after adjuvant therapy in basal-like HER2-positive breast carcinoma[J]. Pathol Res Pract. 2021;228:153677. https:\/\/doi.org\/10.1016\/j.prp.2021.153677","journal-title":"Pathol - Res Pract"},{"key":"1631_CR13","doi-asserted-by":"publisher","unstructured":"Huang Y, Le J, Miao A, et al. Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound. AME publishing company. 2021;(4). https:\/\/doi.org\/10.21037\/GS-20-836","DOI":"10.21037\/GS-20-836"},{"issue":"4","key":"1631_CR14","first-page":"1242","volume":"12","author":"J Li","year":"2023","unstructured":"Li J, et al. Clinical response and pathological assessment in breast cancer after neoadjuvant chemotherapy: a retrospective study of 3,000 patients. Cancer Med. 2023;12(4):1242\u201352.","journal-title":"Cancer Med"},{"key":"1631_CR15","first-page":"799346","volume":"12","author":"L Wang","year":"2022","unstructured":"Wang L, et al. Pathological response to neoadjuvant chemotherapy in HER2-positive breast cancer and its impact on prognosis. Front Oncol. 2022;12:799346.","journal-title":"Front Oncol"},{"issue":"1","key":"1631_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-022-00289-7","volume":"6","author":"F Galati","year":"2022","unstructured":"Galati F, Rizzo V, Moffa G, et al. Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes?[J]. Eur Radiol Experimental. 2022;6(1):1\u201313. https:\/\/doi.org\/10.1186\/s41747-022-00289-7","journal-title":"Eur Radiol Experimental"},{"issue":"2","key":"1631_CR17","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10549-021-06116-0","volume":"189","author":"L Zhang","year":"2021","unstructured":"Zhang L, Wang X, Li Y, et al. Evaluation of tumor shrinkage patterns and clinical-pathological factors associated with neoadjuvant chemotherapy in breast cancer. Breast Cancer Res Treat. 2021;189(2):249\u201359. https:\/\/doi.org\/10.1007\/s10549-021-06116-0","journal-title":"Breast Cancer Res Treat"},{"key":"1631_CR18","doi-asserted-by":"publisher","unstructured":"Ramtohul T, Tescher C, Vaflard P, et al. Prospective evaluation of ultrafast breast MRI for predicting pathologic response afterneoadjuvant therapies. Radiology. 2022;305(3):565\u201374. https:\/\/doi.org\/10.1148\/radiol.220389","DOI":"10.1148\/radiol.220389"},{"key":"1631_CR19","doi-asserted-by":"publisher","unstructured":"Dou H, Li F, Wang Y, et al. Estrogen receptor-negative\/progesterone receptor-positive breast cancer has distinct characteristics and pathologic complete response rate after neoadjuvant chemotherapy. Diagn Pathol. 2024;19(1). https:\/\/doi.org\/10.1186\/s13000-023-01433-6","DOI":"10.1186\/s13000-023-01433-6"},{"issue":"4","key":"1631_CR20","doi-asserted-by":"publisher","first-page":"2111","DOI":"10.1245\/s10434-020-09480-9","volume":"28","author":"RA Leon-Ferre","year":"2021","unstructured":"Leon-Ferre RA, Hieken TJ, Boughey JC. The landmark series: neoadjuvant chemotherapy for triple-negative and HER2-positive breast Cancer. Ann Surg Oncol. 2021;28(4):2111\u20139. https:\/\/doi.org\/10.1245\/s10434-020-09480-9","journal-title":"Ann Surg Oncol"},{"key":"1631_CR21","doi-asserted-by":"publisher","unstructured":"Teruya N, Inoue H, Horii R, et al. Intratumoral heterogeneity, treatment response, and survival outcome of ER-positive HER2\u2010positive breast cancer. Cancer Med. 2023;12(9):10526\u201335. https:\/\/doi.org\/10.1002\/cam4.5788","DOI":"10.1002\/cam4.5788"},{"key":"1631_CR22","doi-asserted-by":"publisher","unstructured":"Peng JH, Zhang X, Song JL, et al. Neoadjuvant chemotherapy reduces the expression rates of ER, PR, HER2, Ki67, and P53 of invasive ductal carcinoma. Medicine. 2019;98(2). https:\/\/doi.org\/10.1097\/MD.0000000000013554","DOI":"10.1097\/MD.0000000000013554"},{"key":"1631_CR23","doi-asserted-by":"publisher","unstructured":"Zhang H, Wang Z, Liu W, et al. Breast-conserving surgery in triple-negative breast cancer: a retrospective cohort Study. Evid Based Complement Alternat Med. 2023;2023:1\u20138. https:\/\/doi.org\/10.1155\/2023\/5431563","DOI":"10.1155\/2023\/5431563"},{"key":"1631_CR24","doi-asserted-by":"publisher","unstructured":"Chen W, Li FX, Lu DL, et al. Differences between the efficacy of HER2(2+)\/FISH-positive and HER2(3+) in breast cancer during dual-target neoadjuvant therapy. Breast. 2023;71:69\u201373. https:\/\/doi.org\/10.1016\/j.breast.2023.07.008","DOI":"10.1016\/j.breast.2023.07.008"},{"issue":"1","key":"1631_CR25","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1186\/s12885-022-10315-x","volume":"22","author":"X Liang","year":"2022","unstructured":"Liang X, Chen X, Yang Z, et al. Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast cancer. BMC Cancer. 2022;22(1):1250. https:\/\/doi.org\/10.1186\/s12885-022-10315-x","journal-title":"Cancer[J] BMC cancer"},{"issue":"1115","key":"1631_CR26","doi-asserted-by":"publisher","first-page":"20200751","DOI":"10.1259\/bjr.20200751","volume":"93","author":"W Guo","year":"2020","unstructured":"Guo W, Zhang Y, Luo D, et al. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pretreatment prediction of neoadjuvant chemotherapy response in locally advanced hypopharyngeal cancer. Br J Radiol. 2020;93(1115):20200751. https:\/\/doi.org\/10.1259\/bjr.20200751","journal-title":"Br J Radiol"},{"key":"1631_CR27","doi-asserted-by":"publisher","unstructured":"Zhang D, Geng X, Suo S, et al. The predictive value of DKI in breast cancer: does tumour subtype affect pathological response evaluations? Magn Reson Imaging. 2021;85:28\u201334. https:\/\/doi.org\/10.1016\/j.mri.2021.10.013","DOI":"10.1016\/j.mri.2021.10.013"},{"key":"1631_CR28","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.ejrad.2019.06.008","volume":"117","author":"W Liu","year":"2019","unstructured":"Liu W, Wei C, Bai J, et al. Histogram analysis of diffusion kurtosis imaging in the differentiation of malignant from benign breast lesions. Eur J Radiol. 2019;117:156\u201363. https:\/\/doi.org\/10.1016\/j.ejrad.2019.06.008","journal-title":"Eur J Radiol"},{"key":"1631_CR29","doi-asserted-by":"publisher","unstructured":"Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging. Acta Radiol. 2020;61(9):1228\u201339. https:\/\/doi.org\/10.1177\/0284185119898656","DOI":"10.1177\/0284185119898656"},{"key":"1631_CR30","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26164","author":"D Zheng","year":"2018","unstructured":"Zheng D, Lai G, Chen Y, et al. Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma. J Magn Reson Imaging. 2018. https:\/\/doi.org\/10.1002\/jmri.26164","journal-title":"J Magn Reson Imaging"},{"key":"1631_CR31","doi-asserted-by":"publisher","unstructured":"Ai Z, Han Q, Huang Z, et al. The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions. Ann Transl Med. 2020;(18). https:\/\/doi.org\/10.21037\/ATM-20-5109","DOI":"10.21037\/ATM-20-5109"},{"key":"1631_CR32","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.mri.2017.12.018","volume":"48","author":"Y Kim","year":"2018","unstructured":"Kim Y, Kim SH, Lee HW, et al. Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer. Magn Reson Imaging. 2018;48:27\u201333. https:\/\/doi.org\/10.1016\/j.mri.2017.12.018","journal-title":"Magn Reson Imaging"},{"key":"1631_CR33","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.ejrad.2019.04.022","volume":"116","author":"Y Gao","year":"2019","unstructured":"Gao Y, Wu W, Zhang X, et al. The role of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in the prediction of chemotherapy response in breast cancer. Eur J Radiol. 2019;116:159\u201365. https:\/\/doi.org\/10.1016\/j.ejrad.2019.04.022","journal-title":"Eur J Radiol"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01631-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01631-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01631-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T12:16:27Z","timestamp":1744632987000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01631-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1631"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01631-2","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,14]]},"assertion":[{"value":"29 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The studies involving humans were approved by Ethics Committee of Chongging University Three Gorges Hospital. The studies were conducted in accordance with the local legislation and institutional requirements (MR-50-23-026780). This study is a retrospective analysis and has received an exemption from the informed consent requirement.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All data have been anonymized to adhere to ethical standards, with explicit consent obtained from the participants, and all authors have provided their consent for this manuscript\u2019s publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"118"}}