Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules

Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wei YU [verfasserIn] Bo YE [verfasserIn] Liyun XU [verfasserIn] Zhaoyu WANG [verfasserIn] Hanbo LE [verfasserIn] Shanjun WANG [verfasserIn] Hanbo CAO [verfasserIn] Zhenda CHAI [verfasserIn] Zhijun CHEN [verfasserIn] Qingquan LUO [verfasserIn] Yongkui ZHANG [verfasserIn]

Format:	E-Artikel
Sprache:	Chinesisch

Erschienen:	2016

Schlagwörter:	Solitary pulmonary nodules (SPNs) Prediction model Independent predictors

Übergeordnetes Werk:	In: Chinese Journal of Lung Cancer - Chinese Anti-Cancer Association; Chinese Antituberculosis Association, 2008, 19(2016), 10, Seite page-page
Übergeordnetes Werk:	volume:19 ; year:2016 ; number:10 ; pages:page-page

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3779/j.issn.1009-3419.2016.10.12

Katalog-ID:	DOAJ013089218

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520			\|a Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.3779/j.issn.1009-3419.2016.10.12 doi (DE-627)DOAJ013089218 (DE-599)DOAJbd13d89f5f4b47598fba2c0205f71a33 DE-627 ger DE-627 rakwb chi RC254-282 Wei YU verfasserin aut Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs. Solitary pulmonary nodules (SPNs) Prediction model Independent predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bo YE verfasserin aut Liyun XU verfasserin aut Zhaoyu WANG verfasserin aut Hanbo LE verfasserin aut Shanjun WANG verfasserin aut Hanbo CAO verfasserin aut Zhenda CHAI verfasserin aut Zhijun CHEN verfasserin aut Qingquan LUO verfasserin aut Yongkui ZHANG verfasserin aut In Chinese Journal of Lung Cancer Chinese Anti-Cancer Association; Chinese Antituberculosis Association, 2008 19(2016), 10, Seite page-page (DE-627)572421125 (DE-600)2438672-8 19996187 nnns volume:19 year:2016 number:10 pages:page-page https://doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/article/bd13d89f5f4b47598fba2c0205f71a33 kostenfrei http://dx.doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/toc/1009-3419 Journal toc kostenfrei https://doaj.org/toc/1999-6187 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2016 10 page-page
spelling	10.3779/j.issn.1009-3419.2016.10.12 doi (DE-627)DOAJ013089218 (DE-599)DOAJbd13d89f5f4b47598fba2c0205f71a33 DE-627 ger DE-627 rakwb chi RC254-282 Wei YU verfasserin aut Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs. Solitary pulmonary nodules (SPNs) Prediction model Independent predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bo YE verfasserin aut Liyun XU verfasserin aut Zhaoyu WANG verfasserin aut Hanbo LE verfasserin aut Shanjun WANG verfasserin aut Hanbo CAO verfasserin aut Zhenda CHAI verfasserin aut Zhijun CHEN verfasserin aut Qingquan LUO verfasserin aut Yongkui ZHANG verfasserin aut In Chinese Journal of Lung Cancer Chinese Anti-Cancer Association; Chinese Antituberculosis Association, 2008 19(2016), 10, Seite page-page (DE-627)572421125 (DE-600)2438672-8 19996187 nnns volume:19 year:2016 number:10 pages:page-page https://doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/article/bd13d89f5f4b47598fba2c0205f71a33 kostenfrei http://dx.doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/toc/1009-3419 Journal toc kostenfrei https://doaj.org/toc/1999-6187 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2016 10 page-page
allfields_unstemmed	10.3779/j.issn.1009-3419.2016.10.12 doi (DE-627)DOAJ013089218 (DE-599)DOAJbd13d89f5f4b47598fba2c0205f71a33 DE-627 ger DE-627 rakwb chi RC254-282 Wei YU verfasserin aut Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs. Solitary pulmonary nodules (SPNs) Prediction model Independent predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bo YE verfasserin aut Liyun XU verfasserin aut Zhaoyu WANG verfasserin aut Hanbo LE verfasserin aut Shanjun WANG verfasserin aut Hanbo CAO verfasserin aut Zhenda CHAI verfasserin aut Zhijun CHEN verfasserin aut Qingquan LUO verfasserin aut Yongkui ZHANG verfasserin aut In Chinese Journal of Lung Cancer Chinese Anti-Cancer Association; Chinese Antituberculosis Association, 2008 19(2016), 10, Seite page-page (DE-627)572421125 (DE-600)2438672-8 19996187 nnns volume:19 year:2016 number:10 pages:page-page https://doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/article/bd13d89f5f4b47598fba2c0205f71a33 kostenfrei http://dx.doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/toc/1009-3419 Journal toc kostenfrei https://doaj.org/toc/1999-6187 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2016 10 page-page
allfieldsGer	10.3779/j.issn.1009-3419.2016.10.12 doi (DE-627)DOAJ013089218 (DE-599)DOAJbd13d89f5f4b47598fba2c0205f71a33 DE-627 ger DE-627 rakwb chi RC254-282 Wei YU verfasserin aut Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs. Solitary pulmonary nodules (SPNs) Prediction model Independent predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bo YE verfasserin aut Liyun XU verfasserin aut Zhaoyu WANG verfasserin aut Hanbo LE verfasserin aut Shanjun WANG verfasserin aut Hanbo CAO verfasserin aut Zhenda CHAI verfasserin aut Zhijun CHEN verfasserin aut Qingquan LUO verfasserin aut Yongkui ZHANG verfasserin aut In Chinese Journal of Lung Cancer Chinese Anti-Cancer Association; Chinese Antituberculosis Association, 2008 19(2016), 10, Seite page-page (DE-627)572421125 (DE-600)2438672-8 19996187 nnns volume:19 year:2016 number:10 pages:page-page https://doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/article/bd13d89f5f4b47598fba2c0205f71a33 kostenfrei http://dx.doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/toc/1009-3419 Journal toc kostenfrei https://doaj.org/toc/1999-6187 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2016 10 page-page
allfieldsSound	10.3779/j.issn.1009-3419.2016.10.12 doi (DE-627)DOAJ013089218 (DE-599)DOAJbd13d89f5f4b47598fba2c0205f71a33 DE-627 ger DE-627 rakwb chi RC254-282 Wei YU verfasserin aut Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs. Solitary pulmonary nodules (SPNs) Prediction model Independent predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bo YE verfasserin aut Liyun XU verfasserin aut Zhaoyu WANG verfasserin aut Hanbo LE verfasserin aut Shanjun WANG verfasserin aut Hanbo CAO verfasserin aut Zhenda CHAI verfasserin aut Zhijun CHEN verfasserin aut Qingquan LUO verfasserin aut Yongkui ZHANG verfasserin aut In Chinese Journal of Lung Cancer Chinese Anti-Cancer Association; Chinese Antituberculosis Association, 2008 19(2016), 10, Seite page-page (DE-627)572421125 (DE-600)2438672-8 19996187 nnns volume:19 year:2016 number:10 pages:page-page https://doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/article/bd13d89f5f4b47598fba2c0205f71a33 kostenfrei http://dx.doi.org/10.3779/j.issn.1009-3419.2016.10.12 kostenfrei https://doaj.org/toc/1009-3419 Journal toc kostenfrei https://doaj.org/toc/1999-6187 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2016 10 page-page
language	Chinese
source	In Chinese Journal of Lung Cancer 19(2016), 10, Seite page-page volume:19 year:2016 number:10 pages:page-page
sourceStr	In Chinese Journal of Lung Cancer 19(2016), 10, Seite page-page volume:19 year:2016 number:10 pages:page-page
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Solitary pulmonary nodules (SPNs) Prediction model Independent predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Chinese Journal of Lung Cancer
authorswithroles_txt_mv	Wei YU @@aut@@ Bo YE @@aut@@ Liyun XU @@aut@@ Zhaoyu WANG @@aut@@ Hanbo LE @@aut@@ Shanjun WANG @@aut@@ Hanbo CAO @@aut@@ Zhenda CHAI @@aut@@ Zhijun CHEN @@aut@@ Qingquan LUO @@aut@@ Yongkui ZHANG @@aut@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	572421125
id	DOAJ013089218
language_de	chinesisch
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The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. 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callnumber-first	R - Medicine
author	Wei YU
spellingShingle	Wei YU misc RC254-282 misc Solitary pulmonary nodules (SPNs) misc Prediction model misc Independent predictors misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules
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topic_title	RC254-282 Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules Solitary pulmonary nodules (SPNs) Prediction model Independent predictors
topic	misc RC254-282 misc Solitary pulmonary nodules (SPNs) misc Prediction model misc Independent predictors misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc Solitary pulmonary nodules (SPNs) misc Prediction model misc Independent predictors misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc Solitary pulmonary nodules (SPNs) misc Prediction model misc Independent predictors misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules
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title_full	Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules
author_sort	Wei YU
journal	Chinese Journal of Lung Cancer
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author_browse	Wei YU Bo YE Liyun XU Zhaoyu WANG Hanbo LE Shanjun WANG Hanbo CAO Zhenda CHAI Zhijun CHEN Qingquan LUO Yongkui ZHANG
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title_sort	establishment of a clinical prediction model of solid solitary pulmonary nodules
callnumber	RC254-282
title_auth	Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules
abstract	Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.
abstractGer	Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.
abstract_unstemmed	Background and objective The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs. Methods We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model. Results Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%. Conclusion Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.
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title_short	Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules
url	https://doi.org/10.3779/j.issn.1009-3419.2016.10.12 https://doaj.org/article/bd13d89f5f4b47598fba2c0205f71a33 http://dx.doi.org/10.3779/j.issn.1009-3419.2016.10.12 https://doaj.org/toc/1009-3419 https://doaj.org/toc/1999-6187
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up_date	2024-07-03T15:42:53.338Z
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