Evaluation and management of pulmonary nodules: state-of-the-art and future perspectives

Sayyouh, Mohamed; Vummidi, Dharshan; Kazerooni, Ella A.

doi:10.1517/17530059.2013.858117

Cited by 11 publications

(5 citation statements)

References 127 publications

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“…The differential diagnosis between benign and malignant pulmonary nodules is based on the medical history, volume, or growth rate of the nodule in a long time [9] and imaging study for the characteristic morphologic nodule [10].…”

Section: Discussionmentioning

confidence: 99%

Multiple Pulmonary Calssified Nodules and Cavitation Filled With the Dense Body on Chest X-Ray: A Case Report

Agin

Tehrani

Dadpour

et al. 2019

Int J Med Toxicol Forensic Med

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Imaging in the clinical study has a significant value in medical diagnosing and the following diseases. The characteristic signs of imaging are a clue in approach to differential diagnosis. A conventional chest x-ray radiography is a more prevalent image used in the initial clinical assessment of pulmonary diseases. Iran is one of the 9 countries with a 95% burden of tuberculosis in the Eastern Mediterranian region. Chest radiography is the initial imaging for the evaluation of pulmonary tuberculosis.

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Section: Discussionmentioning

confidence: 99%

Multiple Pulmonary Calssified Nodules and Cavitation Filled With the Dense Body on Chest X-Ray: A Case Report

Agin

Tehrani

Dadpour

et al. 2019

Int J Med Toxicol Forensic Med

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“…Nowadays, with the advancement of people's living standards, the continuous improvement of health awareness, and the widespread use of low-dose computed tomography (LDCT), pulmonary nodules are most discovered during physical examination [1]. By chest radiography or chest CT examination, most pulmonary nodules are difcult to distinguish between benign and malignant, and such patients usually require outpatient follow-up for more than 2 years to observe changes in the nodules and determine whether further treatment is required [2,3]. Most pulmonary nodules do not progress to lung cancer, but because of the frst discovery and long-term follow-up, patients may encounter signifcant stress, adverse emotional changes, and distress.…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of a Risk Warning Model of Depression in Patients with Pulmonary Nodule

Sun

Zhou

Wang

2023

Perspectives in Psychiatric Care

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Background. To establish a depression risk warning model for patients with pulmonary nodules and to provide a theoretical basis for medical staff to identify high-risk patients early and quickly and take timely intervention measures. Methods. A total of 535 hospitalized patients with pulmonary nodules were selected, and the relevant data were analyzed by single-factor analysis. Binary logistic regression analysis was used to determine the independent risk factors for depression in patients with pulmonary nodules and to establish a risk warning model. The Hosmer−Lemeshow test and receiver operating characteristic (ROC) curve were used to evaluate the goodness of fit and prediction effect of the model, and the cross-validation method was used to verify the efficacy of the model. Results. The prevalence of depression in patients with pulmonary nodules was 47.29%. Univariate analysis showed that CRP, albumin, creatinine, phosphorus, calcium, triglyceride, cholesterol, low-density lipoprotein, high-density lipoprotein, β2-microglobulin, objective support, social support, and education level were related to depression in patients with pulmonary nodules P < 0.05 . Binary logistic regression analysis showed that serum CRP, calcium, social support, and education level were independent risk factors for depression in patients with pulmonary nodules. The area under the ROC curve/sensitivity/specificity of serum CRP, calcium, social support, and education level was 0.78/86.88%/80.65%, 0.79/84.40%/75.59%, 0.83/89.91%/80.22%, and 0.81/85.96%/79.19%, and the accuracy of cross-checked risk warning model was 84.97%. In the Hosmer−Lemeshow test, P = 0.926, area under the ROC curve was 0.98, sensitivity was 98.17%, and specificity was 93.55%. The accuracy of the cross-checked risk warning model was 84.97%, indicating that the prediction effect of the model was good. Conclusions. Serum CRP, calcium, social support, and education level are the independent risk factors of depression in patients with pulmonary nodules, and the risk warning model based on them has a good early warning effect on depression in patients with pulmonary nodules. The risk warning model established in this study has a good predictive effect on depression in patients with pulmonary nodules.

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“…In the Early Lung Cancer Action Project performed by Henschke et al [5], the detection rate of SPN was as high as 23%, which increased to 39.5% in patients received lung operation [6]. For indeterminate solid and ground-glass nodules, they should be followed with CT at least 2 and 3 years, respectively, according to the international guidelines for the management of SPN [7,8].…”

Section: Introductionmentioning

confidence: 99%

The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma

et al. 2020

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Background: Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN. Methods: This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrastenhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated. Results: The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735-0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707-0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723-0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power. Conclusion: The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.

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Evaluation and management of pulmonary nodules: state-of-the-art and future perspectives

Cited by 11 publications

References 127 publications

Multiple Pulmonary Calssified Nodules and Cavitation Filled With the Dense Body on Chest X-Ray: A Case Report

Multiple Pulmonary Calssified Nodules and Cavitation Filled With the Dense Body on Chest X-Ray: A Case Report

Construction and Validation of a Risk Warning Model of Depression in Patients with Pulmonary Nodule

The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma

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