Bai-Qiang Qu, Yun Wang, Yue-Peng Pan, Pei-Wei Cao, Xue-Ying Deng
{"title":"评分系统结合放射组学和影像学特征,预测偶发的不确定小(<20 毫米)实性肺结节的恶性可能性。","authors":"Bai-Qiang Qu, Yun Wang, Yue-Peng Pan, Pei-Wei Cao, Xue-Ying Deng","doi":"10.1186/s12880-024-01413-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm.</p><p><strong>Methods: </strong>A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed.</p><p><strong>Results: </strong>Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%.</p><p><strong>Conclusion: </strong>The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380408/pdf/","citationCount":"0","resultStr":"{\"title\":\"The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.\",\"authors\":\"Bai-Qiang Qu, Yun Wang, Yue-Peng Pan, Pei-Wei Cao, Xue-Ying Deng\",\"doi\":\"10.1186/s12880-024-01413-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm.</p><p><strong>Methods: </strong>A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed.</p><p><strong>Results: </strong>Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%.</p><p><strong>Conclusion: </strong>The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.</p>\",\"PeriodicalId\":9020,\"journal\":{\"name\":\"BMC Medical Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380408/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12880-024-01413-2\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-024-01413-2","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.
Objective: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm.
Methods: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed.
Results: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%.
Conclusion: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.
期刊介绍:
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.