{"title":"[基于临床可视化参数的肺磨玻璃结节生长预测模型]。","authors":"Ying-Ying Zhou, Zhi-Jun Chen","doi":"10.3881/j.issn.1000-503X.15618","DOIUrl":null,"url":null,"abstract":"<p><p>Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%<i>CI</i>=1.010-1.092,<i>P</i>=0.015) and mass (95%<i>CI</i>=1.002-1.067,<i>P</i>=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CT<sub>mean</sub>+1000)×0.001 (where V is the volume,V=3/4πR<sup>3</sup>,R:radius).Therefore,the logit prediction model was established as ln[<i>P</i>/(1-<i>P</i>)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CT<sub>mean</sub>.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (<i>χ<sup>2</sup></i>=4.515,<i>P</i>=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.</p>","PeriodicalId":6919,"journal":{"name":"中国医学科学院学报","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters].\",\"authors\":\"Ying-Ying Zhou, Zhi-Jun Chen\",\"doi\":\"10.3881/j.issn.1000-503X.15618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%<i>CI</i>=1.010-1.092,<i>P</i>=0.015) and mass (95%<i>CI</i>=1.002-1.067,<i>P</i>=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CT<sub>mean</sub>+1000)×0.001 (where V is the volume,V=3/4πR<sup>3</sup>,R:radius).Therefore,the logit prediction model was established as ln[<i>P</i>/(1-<i>P</i>)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CT<sub>mean</sub>.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (<i>χ<sup>2</sup></i>=4.515,<i>P</i>=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.</p>\",\"PeriodicalId\":6919,\"journal\":{\"name\":\"中国医学科学院学报\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国医学科学院学报\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.3881/j.issn.1000-503X.15618\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国医学科学院学报","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.3881/j.issn.1000-503X.15618","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
[A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters].
Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.
期刊介绍：
Acta Academiae Medicinae Sinicae was founded in February 1979. It is a comprehensive medical academic journal published in China and abroad, supervised by the Ministry of Health of the People's Republic of China and sponsored by the Chinese Academy of Medical Sciences and Peking Union Medical College.
The journal mainly reports the latest research results, work progress and dynamics in the fields of basic medicine, clinical medicine, pharmacy, preventive medicine, biomedicine, medical teaching and research, aiming to promote the exchange of medical information and improve the academic level of medicine. At present, the journal has been included in 10 famous foreign retrieval systems and their databases [Medline (PubMed online version), Elsevier, EMBASE, CA, WPRIM, ExtraMED, IC, JST, UPD and EBSCO-ASP]; and has been included in important domestic retrieval systems and databases [China Science Citation Database (Documentation and Information Center of the Chinese Academy of Sciences), China Core Journals Overview (Peking University Library), China Science and Technology Paper Statistical Source Database (China Science and Technology Core Journals) (China Institute of Scientific and Technological Information), China Science and Technology Journal Paper and Citation Database (China Institute of Scientific and Technological Information)].
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