Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

Guo, Haoyue; Lin, Jun‐Tao; Huang, Huilian; Gao, Yuan; Yan, Mei-Ru; Sun, Ming Hua; Xu, Wei; Yan, Hong‐Hong; Zhong, Wen‐Zhao; Yang, Xiaozhong

doi:10.1016/j.cllc.2019.07.014

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…We noticed that the clinical characteristics could be as important as radiomics features in lung cancer risk prediction for solid nodules. Sixty percent of the top ten selected features were clinical variables, which have been identified in previous studies (7,(20)(21)(22). The clinical variables were age, spiculation, sex, shape, smoking, and history of malignancy.…”

Section: Discussionmentioning

confidence: 99%

“…The clinical variables were age, spiculation, sex, shape, smoking, and history of malignancy. When predicting malignancy of solid nodules, the clinical-based models exhibited an AUC of 0.81 to 0.89 (7,(20)(21)(22), and one study pointed out that a VDT of 25-400 days was highly suggestive of malignancy (7). On the other hand, quantitative radiomics models have also demonstrated potential for diagnosing solid nodules, especially radiomics models created from gross tumor volume instead of peritumoral volumes (23).…”

Section: Discussionmentioning

confidence: 99%

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Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

Zhang¹,

Sun²,

Chen³

et al. 2021

J Thorac Dis

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Background: Accurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.Methods: This study enrolled 5-20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model.Results: Lung cancer risk models were developed among four nodule groups: all nodules (410 benign vs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

Zhang¹,

Sun²,

Chen³

et al. 2021

J Thorac Dis

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“…30 Besides, another three models were also established for solid nodules based on a Chinese population (AUC=0.85~0.87). [31][32][33] Compared to these four models, the current solid-nodule models shared some similar risk factors, such as age, sex, history of malignancy, morphology, and serum CEA, but some novel markers like V25, RV/TLC, serum lymphocyte, CYFRA21-1, and NSE were also identified. As for model performance, these four were a little better than our models (AUC=0.82), which can be associated with some valuable predictors they enrolled, such as enhancement, VDT, as well as maximum uptake value of nodules.…”

Section: Discussionmentioning

confidence: 96%

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

Zhang¹,

Tian²,

Chen³

et al. 2020

CMAR

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Objective: Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size. Materials and Methods: This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models. Results: The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size. Conclusion: We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.

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“…Therefore, some identified margin characteristics can increase the probability of malignancy, such as spiculation (a linear shadow of varying length extending from the nodule margin to the surrounding lung tissue) and lobulation (the edge of the lesion characterized by an uneven irregular notch), while benign nodules are usually smooth and roundlike with an obvious boundary. 11 In addition, there are some special structural changes in the surrounding structure of malignant pulmonary nodules, including vessel convergence (the thickening of blood vessels near the nodules and the accumulation of blood into the focal areas contributing to the growth of malignant tumors) and pleural indentation (indentation due to subpleural fibrosis or the invasion of malignant tumors to the visceral pleura). 12 However, CT imaging has several limitations in the diagnosis of malignant lung nodules as follows.…”

Section: Margin Characteristicsmentioning

confidence: 99%

Liquid biopsies to distinguish malignant from benign pulmonary nodules

et al. 2021

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Over the past decades, low-dose computed tomography (LD-CT) screening has been widely used for the early detection of lung cancer. Increasing numbers of indeterminate pulmonary nodules are now being discovered. However, it remains challenging to distinguish malignant from benign pulmonary nodules, especially those considered to be small or ground-glass (GGN) nodules. Liquid biopsies have been successfully applied in the diagnosis of advanced lung cancer, and the potential value for early detection of lung cancer has made great progress. Recent studies have demonstrated the value of various blood-based tumor biomarkers in determining the nature of pulmonary nodules, including cell-free DNA (cfDNA), microRNAs (miRNAs), circulating tumor cells (CTCs) and tumor-associated autoantibodies (AAbs). In this review, we summarize the latest progress of liquid biopsies, and their potential applications and challenges in the diagnosis of malignant pulmonary nodules. K E Y W O R D Sbiomarker, cell-free DNA, liquid biopsy, miRNA, pulmonary nodule diagnosis Rui Tao, Wei Cao, and Feng Zhu contributed equally to this study.

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Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy

Cited by 15 publications

References 31 publications

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features

<p>Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes</p>

Liquid biopsies to distinguish malignant from benign pulmonary nodules

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