Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules
Objectives The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules. Methods A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were...
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| Veröffentlicht in: | European radiology 2019-03, Vol.29 (3), p.1518-1526 |
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| creator | Guo, Bao-liang Ouyang, Fu-sheng Ouyang, Li-zhu Liu, Zi-wei Lin, Shao-jia Meng, Wei Huang, Xi-yi Chen, Hai-xiong Yang, Shao-ming Hu, Qiu-gen |
| description | Objectives
The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules.
Methods
A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI).
Results
The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918–0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915–0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776–0.789).
Conclusions
This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules.
Key Points
• Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules.
• However, ultrasound accuracy relies heavily on examiner experience.
• A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration. |
| doi_str_mv | 10.1007/s00330-018-5715-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2103672233</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2103362564</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-57ec1a4c9087660092240a20c2b44c6f5f647caf046b8414a4ffa4ad9e2e288e3</originalsourceid><addsrcrecordid>eNp1kctqHDEQRUVIiCe2P8CbIMgmm05Kj34tjfOwwZBNshY16tK4Tbc0ltQDQ34-mowdgyErQenUqSouYxcCPgmA9nMCUAoqEF1Vt6Ku6ldsJbSSlYBOv2Yr6FVXtX2vT9i7lO4BoBe6fctOFEjo616u2O8vtKMpbGfymaMf-A6nccA8Bs-DKxW-TDliCosfqjUmGrgPc9hEnHkOfJy3MeyI5zviw4gbH1IeLUdrl4h2z12IfC7Gjcfiz3f7GMaDYVgmSmfsjcMp0fnje8p-ffv68-q6uv3x_ebq8rayqpW5nEZWoLY9dG3TlBuk1IASrFxrbRtXu0a3Fh3oZt1poVE7hxqHniTJriN1yj4evWXXh4VSNvOYLE0TegpLMlKAaloplSrohxfofViiL9v9pVQj60YXShwpG0NKkZzZxnHGuDcCzCEZc0zGlGTMIRlTl573j-ZlPdPwr-MpigLII5DKl99QfB79f-sfpJKaPQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2103362564</pqid></control><display><type>article</type><title>Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><creator>Guo, Bao-liang ; Ouyang, Fu-sheng ; Ouyang, Li-zhu ; Liu, Zi-wei ; Lin, Shao-jia ; Meng, Wei ; Huang, Xi-yi ; Chen, Hai-xiong ; Yang, Shao-ming ; Hu, Qiu-gen</creator><creatorcontrib>Guo, Bao-liang ; Ouyang, Fu-sheng ; Ouyang, Li-zhu ; Liu, Zi-wei ; Lin, Shao-jia ; Meng, Wei ; Huang, Xi-yi ; Chen, Hai-xiong ; Yang, Shao-ming ; Hu, Qiu-gen</creatorcontrib><description>Objectives
The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules.
Methods
A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI).
Results
The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918–0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915–0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776–0.789).
Conclusions
This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules.
Key Points
• Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules.
• However, ultrasound accuracy relies heavily on examiner experience.
• A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-018-5715-5</identifier><identifier>PMID: 30209592</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Aspect ratio ; Benign ; Calcification ; Calibration ; Confidence intervals ; Datasets ; Diagnosis, Differential ; Diagnostic Radiology ; Diagnostic systems ; Differential diagnosis ; Female ; Humans ; Identification methods ; Imaging ; Internal Medicine ; Interventional Radiology ; Male ; Medical diagnosis ; Medicine ; Medicine & Public Health ; Middle Aged ; Model testing ; Neuroradiology ; Nodules ; Nomograms ; Radiology ; Reproducibility of Results ; Retrospective Studies ; ROC Curve ; Statistical analysis ; Thyroid ; Thyroid gland ; Thyroid Gland - diagnostic imaging ; Thyroid Gland - pathology ; Thyroid Nodule - diagnostic imaging ; Thyroid Nodule - pathology ; Training ; Ultrasonic imaging ; Ultrasonography - methods ; Ultrasound ; Young Adult</subject><ispartof>European radiology, 2019-03, Vol.29 (3), p.1518-1526</ispartof><rights>European Society of Radiology 2018</rights><rights>European Radiology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-57ec1a4c9087660092240a20c2b44c6f5f647caf046b8414a4ffa4ad9e2e288e3</citedby><cites>FETCH-LOGICAL-c372t-57ec1a4c9087660092240a20c2b44c6f5f647caf046b8414a4ffa4ad9e2e288e3</cites><orcidid>0000-0003-1099-0098</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-018-5715-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-018-5715-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30209592$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Guo, Bao-liang</creatorcontrib><creatorcontrib>Ouyang, Fu-sheng</creatorcontrib><creatorcontrib>Ouyang, Li-zhu</creatorcontrib><creatorcontrib>Liu, Zi-wei</creatorcontrib><creatorcontrib>Lin, Shao-jia</creatorcontrib><creatorcontrib>Meng, Wei</creatorcontrib><creatorcontrib>Huang, Xi-yi</creatorcontrib><creatorcontrib>Chen, Hai-xiong</creatorcontrib><creatorcontrib>Yang, Shao-ming</creatorcontrib><creatorcontrib>Hu, Qiu-gen</creatorcontrib><title>Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules.
Methods
A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI).
Results
The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918–0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915–0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776–0.789).
Conclusions
This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules.
Key Points
• Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules.
• However, ultrasound accuracy relies heavily on examiner experience.
• A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration.</description><subject>Accuracy</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aspect ratio</subject><subject>Benign</subject><subject>Calcification</subject><subject>Calibration</subject><subject>Confidence intervals</subject><subject>Datasets</subject><subject>Diagnosis, Differential</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Differential diagnosis</subject><subject>Female</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Model testing</subject><subject>Neuroradiology</subject><subject>Nodules</subject><subject>Nomograms</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Statistical analysis</subject><subject>Thyroid</subject><subject>Thyroid gland</subject><subject>Thyroid Gland - diagnostic imaging</subject><subject>Thyroid Gland - pathology</subject><subject>Thyroid Nodule - diagnostic imaging</subject><subject>Thyroid Nodule - pathology</subject><subject>Training</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonography - methods</subject><subject>Ultrasound</subject><subject>Young Adult</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kctqHDEQRUVIiCe2P8CbIMgmm05Kj34tjfOwwZBNshY16tK4Tbc0ltQDQ34-mowdgyErQenUqSouYxcCPgmA9nMCUAoqEF1Vt6Ku6ldsJbSSlYBOv2Yr6FVXtX2vT9i7lO4BoBe6fctOFEjo616u2O8vtKMpbGfymaMf-A6nccA8Bs-DKxW-TDliCosfqjUmGrgPc9hEnHkOfJy3MeyI5zviw4gbH1IeLUdrl4h2z12IfC7Gjcfiz3f7GMaDYVgmSmfsjcMp0fnje8p-ffv68-q6uv3x_ebq8rayqpW5nEZWoLY9dG3TlBuk1IASrFxrbRtXu0a3Fh3oZt1poVE7hxqHniTJriN1yj4evWXXh4VSNvOYLE0TegpLMlKAaloplSrohxfofViiL9v9pVQj60YXShwpG0NKkZzZxnHGuDcCzCEZc0zGlGTMIRlTl573j-ZlPdPwr-MpigLII5DKl99QfB79f-sfpJKaPQ</recordid><startdate>20190301</startdate><enddate>20190301</enddate><creator>Guo, Bao-liang</creator><creator>Ouyang, Fu-sheng</creator><creator>Ouyang, Li-zhu</creator><creator>Liu, Zi-wei</creator><creator>Lin, Shao-jia</creator><creator>Meng, Wei</creator><creator>Huang, Xi-yi</creator><creator>Chen, Hai-xiong</creator><creator>Yang, Shao-ming</creator><creator>Hu, Qiu-gen</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature 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>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1099-0098</orcidid></search><sort><creationdate>20190301</creationdate><title>Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules</title><author>Guo, Bao-liang ; Ouyang, Fu-sheng ; Ouyang, Li-zhu ; Liu, Zi-wei ; Lin, Shao-jia ; Meng, Wei ; Huang, Xi-yi ; Chen, Hai-xiong ; Yang, Shao-ming ; Hu, Qiu-gen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-57ec1a4c9087660092240a20c2b44c6f5f647caf046b8414a4ffa4ad9e2e288e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aspect ratio</topic><topic>Benign</topic><topic>Calcification</topic><topic>Calibration</topic><topic>Confidence intervals</topic><topic>Datasets</topic><topic>Diagnosis, Differential</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Differential diagnosis</topic><topic>Female</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Model testing</topic><topic>Neuroradiology</topic><topic>Nodules</topic><topic>Nomograms</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Statistical analysis</topic><topic>Thyroid</topic><topic>Thyroid gland</topic><topic>Thyroid Gland - diagnostic imaging</topic><topic>Thyroid Gland - pathology</topic><topic>Thyroid Nodule - diagnostic imaging</topic><topic>Thyroid Nodule - pathology</topic><topic>Training</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonography - methods</topic><topic>Ultrasound</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Bao-liang</creatorcontrib><creatorcontrib>Ouyang, Fu-sheng</creatorcontrib><creatorcontrib>Ouyang, Li-zhu</creatorcontrib><creatorcontrib>Liu, Zi-wei</creatorcontrib><creatorcontrib>Lin, Shao-jia</creatorcontrib><creatorcontrib>Meng, Wei</creatorcontrib><creatorcontrib>Huang, Xi-yi</creatorcontrib><creatorcontrib>Chen, Hai-xiong</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>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Bao-liang</au><au>Ouyang, Fu-sheng</au><au>Ouyang, Li-zhu</au><au>Liu, Zi-wei</au><au>Lin, Shao-jia</au><au>Meng, Wei</au><au>Huang, Xi-yi</au><au>Chen, Hai-xiong</au><au>Yang, Shao-ming</au><au>Hu, Qiu-gen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2019-03-01</date><risdate>2019</risdate><volume>29</volume><issue>3</issue><spage>1518</spage><epage>1526</epage><pages>1518-1526</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules.
Methods
A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI).
Results
The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918–0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915–0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776–0.789).
Conclusions
This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules.
Key Points
• Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules.
• However, ultrasound accuracy relies heavily on examiner experience.
• A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>30209592</pmid><doi>10.1007/s00330-018-5715-5</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1099-0098</orcidid></addata></record> |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Accuracy Adolescent Adult Aged Aged, 80 and over Aspect ratio Benign Calcification Calibration Confidence intervals Datasets Diagnosis, Differential Diagnostic Radiology Diagnostic systems Differential diagnosis Female Humans Identification methods Imaging Internal Medicine Interventional Radiology Male Medical diagnosis Medicine Medicine & Public Health Middle Aged Model testing Neuroradiology Nodules Nomograms Radiology Reproducibility of Results Retrospective Studies ROC Curve Statistical analysis Thyroid Thyroid gland Thyroid Gland - diagnostic imaging Thyroid Gland - pathology Thyroid Nodule - diagnostic imaging Thyroid Nodule - pathology Training Ultrasonic imaging Ultrasonography - methods Ultrasound Young Adult |
| title | Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules |
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