{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T03:54:47Z","timestamp":1793332487556,"version":"4.5.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Cancer Hospital & Institute","award":["LC2020A15"],"award-info":[{"award-number":["LC2020A15"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>To construct effective prediction models for neoadjuvant radiotherapy (RT) and targeted therapy based on whole-tumor texture analysis of multisequence MRI for soft tissue sarcoma (STS) patients.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Thirty patients with STS of the extremities or trunk from a prospective phase II trial were enrolled for this analysis. All patients underwent pre- and post-neoadjuvant RT MRI examinations from which whole-tumor texture features were extracted, including T<jats:sub>1<\/jats:sub>-weighted with fat saturation and contrast enhancement (T<jats:sub>1<\/jats:sub>FSGd), T<jats:sub>2<\/jats:sub>-weighted with fat saturation (T<jats:sub>2<\/jats:sub>FS), and diffusion-weighted imaging (DWI) sequences and their corresponding apparent diffusion coefficient (ADC) maps. According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) and non-pCR (N-pCR) groups. pCR was defined as less than 5% of residual tumor cells by postoperative pathology. Delta features were defined as the percentage change in a texture feature from pre- to post-neoadjuvant RT MRI. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Five of 30 patients (16.7%) achieved pCR. The Delta_Model (AUC 0.92) had a better predictive ability than the Pre_Model (AUC 0.78) and Post_Model (AUC 0.76) and was better than AJCC staging (AUC 0.52) and RECIST 1.1 criteria (AUC 0.52). The Combined_Model (pre, post, and delta features) had the best predictive performance (AUC 0.95).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Whole-tumor texture analysis of multisequence MRI can well predict pCR status after neoadjuvant RT and targeted therapy in STS patients, with better performance than RECIST 1.1 and AJCC staging.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>MRI multisequence texture analysis could predict the efficacy of neoadjuvant RT and targeted therapy for STS patients.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>Texture features showed incremental value beyond routine clinical factors.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>The Combined_Model with features at multiple time points showed the best performance.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-022-09362-6","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T06:02:42Z","timestamp":1672293762000},"page":"3984-3994","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting pathological complete response of neoadjuvant radiotherapy and targeted therapy for soft tissue sarcoma by whole-tumor texture analysis of multisequence MRI imaging"],"prefix":"10.1007","volume":"33","author":[{"given":"Lei","family":"Miao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"LiJing","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"HongTu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ChangYuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhaoYang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JiuMing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ShuLian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YeXiong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YanMei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"LiZhi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4636-6035","authenticated-orcid":false,"given":"NingNing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"9362_CR1","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.6004\/jnccn.2020.0058","volume":"18","author":"M von Mehren","year":"2020","unstructured":"von Mehren M, Kane JM, Bui MM et al (2020) NCCN guidelines insights: soft tissue sarcoma, version 1.2021. 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The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"YanMei Wang kindly provided statistical advice for this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Informed consent was obtained. All methods were carried out in accordance with relevant guidelines and regulations.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The Ethical Committee at the Cancer Hospital, Chinese Academy of Medical Sciences, approved this prospective study (NCT05167994\/ChiCTR2000033377, NCT05235100).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 Prospective\u2022 Diagnostic or prognostic study\u2022 Performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}