A nomogram for individual prediction of vascular invasion in primary breast cancer

•Vascular invasion has been reported as a strong prognostic factor in patients with breast cancer.•We identified 10 clinicopathologic and radiological features associated with vascular invasion in breast cancer.•The nomogram for individual risk prediction for vascular invasion showed excellent discr...

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Veröffentlicht in:European journal of radiology 2019-01, Vol.110, p.30-38
Hauptverfasser: Ouyang, Fu-sheng, Guo, Bao-liang, Huang, Xi-yi, Ouyang, Li-zhu, Zhou, Cui-ru, Zhang, Rong, Wu, Mei-lian, Yang, Zun-shuai, Wu, Shang-kun, Guo, Tian-di, Yang, Shao-ming, Hu, Qiu-gen
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container_title European journal of radiology
container_volume 110
creator Ouyang, Fu-sheng
Guo, Bao-liang
Huang, Xi-yi
Ouyang, Li-zhu
Zhou, Cui-ru
Zhang, Rong
Wu, Mei-lian
Yang, Zun-shuai
Wu, Shang-kun
Guo, Tian-di
Yang, Shao-ming
Hu, Qiu-gen
description •Vascular invasion has been reported as a strong prognostic factor in patients with breast cancer.•We identified 10 clinicopathologic and radiological features associated with vascular invasion in breast cancer.•The nomogram for individual risk prediction for vascular invasion showed excellent discrimination and calibration. To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89–0.99, the sensitivity was 78.9% (95%CI: 72.4%–89.1%), the specificity was 95.3% (95%CI: 89.1%–100.0%), the accuracy was 86.0% (95%CI: 82.0%–92.0%), the PPV was 95.7% (95%CI: 90.0%–100.0%), and the NPV was 77.4% (95%CI: 67.8%–87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83–0.95), the sensitivity was 70.3% (95%CI: 60.7%–79.2%), the specificity was 88.9% (95%CI: 80.0%–97.1%), the accuracy was 77.0% (95%CI: 70.0%–83.0%), the PPV was 91.8% (95%CI: 85.3%–98.0%), and the NPV was 62.7% (95%CI: 51.7%–74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.
doi_str_mv 10.1016/j.ejrad.2018.11.013
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To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89–0.99, the sensitivity was 78.9% (95%CI: 72.4%–89.1%), the specificity was 95.3% (95%CI: 89.1%–100.0%), the accuracy was 86.0% (95%CI: 82.0%–92.0%), the PPV was 95.7% (95%CI: 90.0%–100.0%), and the NPV was 77.4% (95%CI: 67.8%–87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83–0.95), the sensitivity was 70.3% (95%CI: 60.7%–79.2%), the specificity was 88.9% (95%CI: 80.0%–97.1%), the accuracy was 77.0% (95%CI: 70.0%–83.0%), the PPV was 91.8% (95%CI: 85.3%–98.0%), and the NPV was 62.7% (95%CI: 51.7%–74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. 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All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-99a239bf32218fc0821e92f940702c51f1b440414886ee971cc69d32ec3e99b13</citedby><cites>FETCH-LOGICAL-c359t-99a239bf32218fc0821e92f940702c51f1b440414886ee971cc69d32ec3e99b13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejrad.2018.11.013$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30599870$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ouyang, Fu-sheng</creatorcontrib><creatorcontrib>Guo, Bao-liang</creatorcontrib><creatorcontrib>Huang, Xi-yi</creatorcontrib><creatorcontrib>Ouyang, Li-zhu</creatorcontrib><creatorcontrib>Zhou, Cui-ru</creatorcontrib><creatorcontrib>Zhang, Rong</creatorcontrib><creatorcontrib>Wu, Mei-lian</creatorcontrib><creatorcontrib>Yang, Zun-shuai</creatorcontrib><creatorcontrib>Wu, Shang-kun</creatorcontrib><creatorcontrib>Guo, Tian-di</creatorcontrib><creatorcontrib>Yang, Shao-ming</creatorcontrib><creatorcontrib>Hu, Qiu-gen</creatorcontrib><title>A nomogram for individual prediction of vascular invasion in primary breast cancer</title><title>European journal of radiology</title><addtitle>Eur J Radiol</addtitle><description>•Vascular invasion has been reported as a strong prognostic factor in patients with breast cancer.•We identified 10 clinicopathologic and radiological features associated with vascular invasion in breast cancer.•The nomogram for individual risk prediction for vascular invasion showed excellent discrimination and calibration. To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89–0.99, the sensitivity was 78.9% (95%CI: 72.4%–89.1%), the specificity was 95.3% (95%CI: 89.1%–100.0%), the accuracy was 86.0% (95%CI: 82.0%–92.0%), the PPV was 95.7% (95%CI: 90.0%–100.0%), and the NPV was 77.4% (95%CI: 67.8%–87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83–0.95), the sensitivity was 70.3% (95%CI: 60.7%–79.2%), the specificity was 88.9% (95%CI: 80.0%–97.1%), the accuracy was 77.0% (95%CI: 70.0%–83.0%), the PPV was 91.8% (95%CI: 85.3%–98.0%), and the NPV was 62.7% (95%CI: 51.7%–74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.</description><subject>Adult</subject><subject>Aged</subject><subject>Area Under Curve</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - blood supply</subject><subject>Breast Neoplasms - pathology</subject><subject>Feasibility Studies</subject><subject>Female</subject><subject>Humans</subject><subject>Magnetic Resonance Angiography</subject><subject>Magnetic Resonance Imaging</subject><subject>Middle Aged</subject><subject>Neoplasm Invasiveness</subject><subject>Nomogram</subject><subject>Nomograms</subject><subject>Preoperative Care - methods</subject><subject>Probability</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Sensitivity and Specificity</subject><subject>Vascular invasion</subject><subject>Vascular Neoplasms - pathology</subject><issn>0720-048X</issn><issn>1872-7727</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtLxDAQgIMouj5-gSA9emmdSbqb5OBBxBcIgih4C2k6lSxto0m74L-3664ePc0wfPP6GDtFKBBwcbEsaBltXXBAVSAWgGKHzVBJnkvJ5S6bgeSQQ6neDthhSksAmJea77MDAXOtlYQZe77K-tCF92i7rAkx833tV74ebZt9RKq9G3zos9BkK5vc2No1MaXrou8nxHc2fmVVJJuGzNneUTxme41tE51s4xF7vb15ub7PH5_uHq6vHnMn5nrItbZc6KoRnKNqHCiOpHmjS5DA3RwbrMoSSiyVWhBpic4tdC04OUFaVyiO2Plm7kcMnyOlwXQ-OWpb21MYk-G44FKWGtSEig3qYkgpUmO2lxsEs5ZpluZHplnLNIhmkjl1nW0XjFVH9V_Pr70JuNwANL258hRNcp4mB7WP5AZTB__vgm9TYIYm</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>Ouyang, Fu-sheng</creator><creator>Guo, Bao-liang</creator><creator>Huang, Xi-yi</creator><creator>Ouyang, Li-zhu</creator><creator>Zhou, Cui-ru</creator><creator>Zhang, Rong</creator><creator>Wu, Mei-lian</creator><creator>Yang, Zun-shuai</creator><creator>Wu, Shang-kun</creator><creator>Guo, Tian-di</creator><creator>Yang, Shao-ming</creator><creator>Hu, Qiu-gen</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201901</creationdate><title>A nomogram for individual prediction of vascular invasion in primary breast cancer</title><author>Ouyang, Fu-sheng ; Guo, Bao-liang ; Huang, Xi-yi ; Ouyang, Li-zhu ; Zhou, Cui-ru ; Zhang, Rong ; Wu, Mei-lian ; Yang, Zun-shuai ; Wu, Shang-kun ; Guo, Tian-di ; Yang, Shao-ming ; Hu, Qiu-gen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-99a239bf32218fc0821e92f940702c51f1b440414886ee971cc69d32ec3e99b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Area Under Curve</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - blood supply</topic><topic>Breast Neoplasms - pathology</topic><topic>Feasibility Studies</topic><topic>Female</topic><topic>Humans</topic><topic>Magnetic Resonance Angiography</topic><topic>Magnetic Resonance Imaging</topic><topic>Middle Aged</topic><topic>Neoplasm Invasiveness</topic><topic>Nomogram</topic><topic>Nomograms</topic><topic>Preoperative Care - methods</topic><topic>Probability</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Sensitivity and Specificity</topic><topic>Vascular invasion</topic><topic>Vascular Neoplasms - pathology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ouyang, Fu-sheng</creatorcontrib><creatorcontrib>Guo, Bao-liang</creatorcontrib><creatorcontrib>Huang, Xi-yi</creatorcontrib><creatorcontrib>Ouyang, Li-zhu</creatorcontrib><creatorcontrib>Zhou, Cui-ru</creatorcontrib><creatorcontrib>Zhang, Rong</creatorcontrib><creatorcontrib>Wu, Mei-lian</creatorcontrib><creatorcontrib>Yang, Zun-shuai</creatorcontrib><creatorcontrib>Wu, Shang-kun</creatorcontrib><creatorcontrib>Guo, Tian-di</creatorcontrib><creatorcontrib>Yang, Shao-ming</creatorcontrib><creatorcontrib>Hu, Qiu-gen</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ouyang, Fu-sheng</au><au>Guo, Bao-liang</au><au>Huang, Xi-yi</au><au>Ouyang, Li-zhu</au><au>Zhou, Cui-ru</au><au>Zhang, Rong</au><au>Wu, Mei-lian</au><au>Yang, Zun-shuai</au><au>Wu, Shang-kun</au><au>Guo, Tian-di</au><au>Yang, Shao-ming</au><au>Hu, Qiu-gen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A nomogram for individual prediction of vascular invasion in primary breast cancer</atitle><jtitle>European journal of radiology</jtitle><addtitle>Eur J Radiol</addtitle><date>2019-01</date><risdate>2019</risdate><volume>110</volume><spage>30</spage><epage>38</epage><pages>30-38</pages><issn>0720-048X</issn><eissn>1872-7727</eissn><abstract>•Vascular invasion has been reported as a strong prognostic factor in patients with breast cancer.•We identified 10 clinicopathologic and radiological features associated with vascular invasion in breast cancer.•The nomogram for individual risk prediction for vascular invasion showed excellent discrimination and calibration. To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89–0.99, the sensitivity was 78.9% (95%CI: 72.4%–89.1%), the specificity was 95.3% (95%CI: 89.1%–100.0%), the accuracy was 86.0% (95%CI: 82.0%–92.0%), the PPV was 95.7% (95%CI: 90.0%–100.0%), and the NPV was 77.4% (95%CI: 67.8%–87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83–0.95), the sensitivity was 70.3% (95%CI: 60.7%–79.2%), the specificity was 88.9% (95%CI: 80.0%–97.1%), the accuracy was 77.0% (95%CI: 70.0%–83.0%), the PPV was 91.8% (95%CI: 85.3%–98.0%), and the NPV was 62.7% (95%CI: 51.7%–74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>30599870</pmid><doi>10.1016/j.ejrad.2018.11.013</doi><tpages>9</tpages></addata></record>
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source MEDLINE; Elsevier ScienceDirect Journals
subjects Adult
Aged
Area Under Curve
Breast cancer
Breast Neoplasms - blood supply
Breast Neoplasms - pathology
Feasibility Studies
Female
Humans
Magnetic Resonance Angiography
Magnetic Resonance Imaging
Middle Aged
Neoplasm Invasiveness
Nomogram
Nomograms
Preoperative Care - methods
Probability
Retrospective Studies
ROC Curve
Sensitivity and Specificity
Vascular invasion
Vascular Neoplasms - pathology
title A nomogram for individual prediction of vascular invasion in primary breast cancer
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