Dataset Information

[Establishment and Verification of A Novel Predictive Model of Malignancy?for Non-solid Pulmonary Nodules].

ABSTRACT: BACKGROUND:Mathematical predictive model is an effective method for preliminarily identifying the malignant pulmonary nodules. As the epidemiological trend of lung cancer changes, the detection rate of ground-glass-opacity (GGO) like early stage lung cancer is increasing rapidly, timely and proper clinical management can effectively improve the patients' prognosis. Our study aims to establish a novel predictive model of malignancy for non-solid pulmonary nodules, which would provide an objective evidence for invasive procedure and avoid unnecessary operation and the consequences. METHODS:We retrospectively analyzed the basic demographics, serum tumor markers and imaging features of 362 cases of non-solid pulmonary nodule from January 2013 to April 2018. All nodules received biopsy or surgical resection, and got pathological diagnosis. Cases were randomly divided into two groups. The modeling group was used for univariate analysis and logistic regression to determine independent risk factors and establish the predictive model. Data of the validation group was used to validate the predictive value and make a comparison with other models. RESULTS:Of the 362 cases with non-solid pulmonary nodule, 313 (86.5%) cases were diagnosed as AAH/AIS, MIA or invasive adenocarcinoma, 49 cases were diagnosed as benign lesions. Age, serum tumor markers CEA and Cyfra21-1, consolidation tumor ratio value, lobulation and calcification were identified as independent risk factors. The AUC value of the ROC curve was 0.894, the predictive sensitivity and specificity were 87.6%, 69.7%, the positive and negative predictive value were 94.8%, 46.9%. The validated predictive value is significantly better than that of the VA, Brock and GMUFH models. CONCLUSIONS:Proved with high predictive sensitivity and positive predictive value, this novel model could help enable preliminarily screening of "high-risk" non-solid pulmonary nodules before biopsy or surgical excision, and minimize unnecessary invasive procedure. This model achieved preferable predictive value, might have great potential for clinical application.

SUBMITTER: Xiao F

PROVIDER: S-EPMC6348162 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

[Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules].

Xiao Fei F Yu Qiduo Q Zhang Zhenrong Z Liu Deruo D Liang Chaoyang C

Zhongguo fei ai za zhi = Chinese journal of lung cancer 20190101 1

<h4>Background</h4>Mathematical predictive model is an effective method for preliminarily identifying the malignant pulmonary nodules. As the epidemiological trend of lung cancer changes, the detection rate of ground-glass-opacity (GGO) like early stage lung cancer is increasing rapidly, timely and proper clinical management can effectively improve the patients' prognosis. Our study aims to establish a novel predictive model of malignancy for non-solid pulmonary nodules, which would provide an o ...[more]

PMID: 30674390

Similar Datasets

Project description:BackgroundDistinguishing benign from malignant sub-centimeter solid pulmonary nodules (SSPNs) continues to be challenging in clinical practice. Earlier diagnosis is crucial for improving patient survival and prognosis. This study aimed to investigate the risk factors of malignant SSPNs and establish and validate a prediction model based on computed tomography (CT) characteristics to assist in their early diagnosis.MethodsA total of 261 consecutive participants with 261 SSPNs were retrospectively recruited between January 2012 and July 2023 from National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Center 1), including 161 malignant lesions and 100 benign lesions. Patients were randomly assigned to the training set (n=183) and validation set (n=78) according to a 7:3 ratio. Malignant nodules were confirmed by pathology; and benign nodules were confirmed by follow-up or pathology. Clinical data and CT features were collected to estimate the independent predictors of malignancy of SSPN with multivariate logistic analysis. A clinical prediction model was subsequently established by logistic regression. Furthermore, an additional 69 consecutive patients with 69 SSPNs from The Fourth Hospital of Hebei Medical University (Center 2) between January 2022 and December 2022 were retrospectively included as an external cohort to validate the predictive efficacy of the model. The performance of the prediction model was assessed by sensitivity, specificity, and the area under the receiver operating characteristic curve.ResultsThere were 113 (61.7%), 48 (61.5%) and 28 (40.6%) malignant SSPNs in the training, internal and external validation sets, respectively. Multivariate logistic analysis revealed four independent predictors of malignant SSPNs: tumor-lung interface (P=0.002), spiculation (P=0.04), air bronchogram (P=0.047), and invisible at the mediastinal window (P=0.003). The area under the curve (AUC) for the prediction model in the training set was 0.875 [95% confidence interval (CI): 0.818, 0.933]; and the sensitivity and specificity were 94.7% and 68.6%, respectively. The AUCs in the internal and external validation set were (0.781; 95% CI: 0.664, 0.897) and (0.873; 95% CI: 0.791, 0.955), respectively; the sensitivity and specificity were 66.7% and 83.3% for the internal validation data, and 100.0% and 61.0% for the external validation data, respectively.ConclusionsThe prediction model based on CT characteristics could be helpful for distinguishing malignant SSPNs from benign ones.

Project description:ObjectiveTo develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB).MethodA total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People's Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models.ResultsThe combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively.ConclusionThe combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.

Dataset Information

[Establishment and Verification of A Novel Predictive Model of Malignancy?for Non-solid Pulmonary Nodules].

Publications

[Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules].

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

[Establishment and Verification of A Novel Predictive Model of Malignancy?for Non-solid Pulmonary Nodules].

Publications

[Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules].

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

[Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules].