Value of radiomics based on CE-MRI for predicting the efficacy of neoadjuvant chemotherapy in invasive breast cancer

OBJECTIVETo establish and validate a radiomics nomogram based on contrast-enhanced (CE)-MRI for predicting the efficacy of neoadjuvant chemotherapy (NAC) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer with non-mass enhancement (NME). METHODSA cohort comprising 117 HER2-pos...

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Veröffentlicht in:British journal of radiology 2022-10, Vol.95 (1139), p.20220186-20220186
Hauptverfasser: Li, Qin, Huang, Yan, Xiao, Qin, Duan, Shaofeng, Wang, Simin, Li, Jianwei, Niu, Qingliang, Gu, Yajia
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container_end_page 20220186
container_issue 1139
container_start_page 20220186
container_title British journal of radiology
container_volume 95
creator Li, Qin
Huang, Yan
Xiao, Qin
Duan, Shaofeng
Wang, Simin
Li, Jianwei
Niu, Qingliang
Gu, Yajia
description OBJECTIVETo establish and validate a radiomics nomogram based on contrast-enhanced (CE)-MRI for predicting the efficacy of neoadjuvant chemotherapy (NAC) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer with non-mass enhancement (NME). METHODSA cohort comprising 117 HER2-positive breast cancer patients showing NME on CE-MRI between January 2012 and December 2019 were retrospectively analysed in our study. Patients were classified as pathological complete respone (pCR) according to surgical specimens and axillary lymph nodes without invasive tumour cells. Clinicopathological data were recorded, and images were assessed by two radiologists. A total of 1130 radiomics features were extracted from the primary tumour and six radiomics features were selected by the maximal relevance and minimal redundancy and least absolute shrinkage and selection operator algorithms. Univariate logistic regression was used to screen meaningful clinical and imaging features. The rad-score and independent risk factors were incorporated to build a nomogram model. Calibration and receiver operator characteristic curves were used to confirm the performance of the nomogram in the training and testing cohorts. The clinical usefulness of the nomogram was evaluated by decision curve analysis. RESULTSThe difference in the rad-score between the pCR and non-pCR groups was significant in the training and testing cohorts (p < 0.01). The nomogram model showed good calibration and discrimination, with AUCs of 0.900 and 0.810 in the training and testing cohorts. Decision curve analysis indicated that the radiomics-based model was superior in terms of patient clinical benefit. CONCLUSIONThe MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer patients showing NME. ADVANCES IN KNOWLEDGEHER2-positive breast cancer showing segmental enhancement on CE-MRI was more likely to achieve pCR after NAC than regional enhancement and diffuse enhancement.The MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer that showed NME.
doi_str_mv 10.1259/bjr.20220186
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METHODSA cohort comprising 117 HER2-positive breast cancer patients showing NME on CE-MRI between January 2012 and December 2019 were retrospectively analysed in our study. Patients were classified as pathological complete respone (pCR) according to surgical specimens and axillary lymph nodes without invasive tumour cells. Clinicopathological data were recorded, and images were assessed by two radiologists. A total of 1130 radiomics features were extracted from the primary tumour and six radiomics features were selected by the maximal relevance and minimal redundancy and least absolute shrinkage and selection operator algorithms. Univariate logistic regression was used to screen meaningful clinical and imaging features. The rad-score and independent risk factors were incorporated to build a nomogram model. Calibration and receiver operator characteristic curves were used to confirm the performance of the nomogram in the training and testing cohorts. The clinical usefulness of the nomogram was evaluated by decision curve analysis. RESULTSThe difference in the rad-score between the pCR and non-pCR groups was significant in the training and testing cohorts (p &lt; 0.01). The nomogram model showed good calibration and discrimination, with AUCs of 0.900 and 0.810 in the training and testing cohorts. Decision curve analysis indicated that the radiomics-based model was superior in terms of patient clinical benefit. CONCLUSIONThe MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer patients showing NME. ADVANCES IN KNOWLEDGEHER2-positive breast cancer showing segmental enhancement on CE-MRI was more likely to achieve pCR after NAC than regional enhancement and diffuse enhancement.The MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer that showed NME.</description><identifier>ISSN: 0007-1285</identifier><identifier>EISSN: 1748-880X</identifier><identifier>DOI: 10.1259/bjr.20220186</identifier><identifier>PMID: 36005646</identifier><language>eng</language><publisher>The British Institute of Radiology</publisher><ispartof>British journal of radiology, 2022-10, Vol.95 (1139), p.20220186-20220186</ispartof><rights>2022 The Authors. Published by the British Institute of Radiology 2022 The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-53824f49b349248d6b48dc9c92d23be54ce123152c88d4458890c7008b704e9f3</citedby><cites>FETCH-LOGICAL-c361t-53824f49b349248d6b48dc9c92d23be54ce123152c88d4458890c7008b704e9f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids></links><search><creatorcontrib>Li, Qin</creatorcontrib><creatorcontrib>Huang, Yan</creatorcontrib><creatorcontrib>Xiao, Qin</creatorcontrib><creatorcontrib>Duan, Shaofeng</creatorcontrib><creatorcontrib>Wang, Simin</creatorcontrib><creatorcontrib>Li, Jianwei</creatorcontrib><creatorcontrib>Niu, Qingliang</creatorcontrib><creatorcontrib>Gu, Yajia</creatorcontrib><title>Value of radiomics based on CE-MRI for predicting the efficacy of neoadjuvant chemotherapy in invasive breast cancer</title><title>British journal of radiology</title><description>OBJECTIVETo establish and validate a radiomics nomogram based on contrast-enhanced (CE)-MRI for predicting the efficacy of neoadjuvant chemotherapy (NAC) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer with non-mass enhancement (NME). METHODSA cohort comprising 117 HER2-positive breast cancer patients showing NME on CE-MRI between January 2012 and December 2019 were retrospectively analysed in our study. Patients were classified as pathological complete respone (pCR) according to surgical specimens and axillary lymph nodes without invasive tumour cells. Clinicopathological data were recorded, and images were assessed by two radiologists. A total of 1130 radiomics features were extracted from the primary tumour and six radiomics features were selected by the maximal relevance and minimal redundancy and least absolute shrinkage and selection operator algorithms. Univariate logistic regression was used to screen meaningful clinical and imaging features. The rad-score and independent risk factors were incorporated to build a nomogram model. Calibration and receiver operator characteristic curves were used to confirm the performance of the nomogram in the training and testing cohorts. The clinical usefulness of the nomogram was evaluated by decision curve analysis. RESULTSThe difference in the rad-score between the pCR and non-pCR groups was significant in the training and testing cohorts (p &lt; 0.01). The nomogram model showed good calibration and discrimination, with AUCs of 0.900 and 0.810 in the training and testing cohorts. Decision curve analysis indicated that the radiomics-based model was superior in terms of patient clinical benefit. CONCLUSIONThe MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer patients showing NME. ADVANCES IN KNOWLEDGEHER2-positive breast cancer showing segmental enhancement on CE-MRI was more likely to achieve pCR after NAC than regional enhancement and diffuse enhancement.The MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer that showed NME.</description><issn>0007-1285</issn><issn>1748-880X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNpVUV1r20AQPEpK7Tp9yw-4xzxE7t6HpLuXQDBpa0gplCb07TidVvEZSefcSQb_-8jYCQSWXZaZnWEZQq4YLBnP9fdqG5ccOAemik9kzkqpMqXg_wWZA0CZMa7yGfma0va45hq-kJkoAPJCFnMyPNl2RBoaGm3tQ-ddopVNWNPQ09V99vvvmjYh0l3E2rvB98902CDFpvHOusPxsMdg6-24t_1A3Qa7MBGi3R2o76fa2-T3SKuINk247R3GS_K5sW3Cb-e5II8_7v-tfmUPf36uV3cPmRMFG7JcKC4bqSshNZeqLqqpOe00r7moMJcOGRcs506pWspcKQ2uBFBVCRJ1Ixbk9qS7G6sOa4f9EG1rdtF3Nh5MsN58RHq_Mc9hb3SphVR8Erg-C8TwMmIaTOeTw7a109NjMryEouRQMJioNyeqiyGliM27DQNzDMpMQZm3oMQrHdSGAQ</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Li, Qin</creator><creator>Huang, Yan</creator><creator>Xiao, Qin</creator><creator>Duan, Shaofeng</creator><creator>Wang, Simin</creator><creator>Li, Jianwei</creator><creator>Niu, Qingliang</creator><creator>Gu, Yajia</creator><general>The British Institute of Radiology</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20221001</creationdate><title>Value of radiomics based on CE-MRI for predicting the efficacy of neoadjuvant chemotherapy in invasive breast cancer</title><author>Li, Qin ; Huang, Yan ; Xiao, Qin ; Duan, Shaofeng ; Wang, Simin ; Li, Jianwei ; Niu, Qingliang ; Gu, Yajia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-53824f49b349248d6b48dc9c92d23be54ce123152c88d4458890c7008b704e9f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Qin</creatorcontrib><creatorcontrib>Huang, Yan</creatorcontrib><creatorcontrib>Xiao, Qin</creatorcontrib><creatorcontrib>Duan, Shaofeng</creatorcontrib><creatorcontrib>Wang, Simin</creatorcontrib><creatorcontrib>Li, Jianwei</creatorcontrib><creatorcontrib>Niu, Qingliang</creatorcontrib><creatorcontrib>Gu, Yajia</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>British journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Qin</au><au>Huang, Yan</au><au>Xiao, Qin</au><au>Duan, Shaofeng</au><au>Wang, Simin</au><au>Li, Jianwei</au><au>Niu, Qingliang</au><au>Gu, Yajia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Value of radiomics based on CE-MRI for predicting the efficacy of neoadjuvant chemotherapy in invasive breast cancer</atitle><jtitle>British journal of radiology</jtitle><date>2022-10-01</date><risdate>2022</risdate><volume>95</volume><issue>1139</issue><spage>20220186</spage><epage>20220186</epage><pages>20220186-20220186</pages><issn>0007-1285</issn><eissn>1748-880X</eissn><abstract>OBJECTIVETo establish and validate a radiomics nomogram based on contrast-enhanced (CE)-MRI for predicting the efficacy of neoadjuvant chemotherapy (NAC) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer with non-mass enhancement (NME). METHODSA cohort comprising 117 HER2-positive breast cancer patients showing NME on CE-MRI between January 2012 and December 2019 were retrospectively analysed in our study. Patients were classified as pathological complete respone (pCR) according to surgical specimens and axillary lymph nodes without invasive tumour cells. Clinicopathological data were recorded, and images were assessed by two radiologists. A total of 1130 radiomics features were extracted from the primary tumour and six radiomics features were selected by the maximal relevance and minimal redundancy and least absolute shrinkage and selection operator algorithms. Univariate logistic regression was used to screen meaningful clinical and imaging features. The rad-score and independent risk factors were incorporated to build a nomogram model. Calibration and receiver operator characteristic curves were used to confirm the performance of the nomogram in the training and testing cohorts. The clinical usefulness of the nomogram was evaluated by decision curve analysis. RESULTSThe difference in the rad-score between the pCR and non-pCR groups was significant in the training and testing cohorts (p &lt; 0.01). The nomogram model showed good calibration and discrimination, with AUCs of 0.900 and 0.810 in the training and testing cohorts. Decision curve analysis indicated that the radiomics-based model was superior in terms of patient clinical benefit. CONCLUSIONThe MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer patients showing NME. ADVANCES IN KNOWLEDGEHER2-positive breast cancer showing segmental enhancement on CE-MRI was more likely to achieve pCR after NAC than regional enhancement and diffuse enhancement.The MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer that showed NME.</abstract><pub>The British Institute of Radiology</pub><pmid>36005646</pmid><doi>10.1259/bjr.20220186</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
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title Value of radiomics based on CE-MRI for predicting the efficacy of neoadjuvant chemotherapy in invasive breast cancer
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