Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters

Liang, Ling; Zhang, Haiyan; Lei, Haike; Zhou, Hong; Wu, Yongzhong; Shen, Jian

doi:10.1177/15330338221119748

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Beyond the first-order histogram features, the second-and higher-order texture features extracted by radiomics techniques were also integrated in the assessment of invasiveness among SSNs (30)(31)(32). Li et al obtained the CT texture features of 109 SSNs and found that the surface area feature and the extruded surface area feature could be predictors of IACs compared with MIAs (33).…”

Section: Discussionmentioning

confidence: 99%

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

Gao

et al. 2023

Front. Oncol.

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BackgroundTumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness.MethodsWe identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People’s Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves.ResultsIn total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004–1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058–3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004–1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259–27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057–9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399–0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750–7.717; p<0.001) were independent predictors for IAC. The areas under the curve (AUCs) in the training and validation cohorts indicated a better discriminative ability of the histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886).ConclusionWe developed an AI-based histogram model, which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.

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Section: Discussionmentioning

confidence: 99%

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

Gao

et al. 2023

Front. Oncol.

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The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study

Wang,

Wei,

Zhu

et al. 2024

Technol Cancer Res Treat

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Background Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. Methods A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. Results Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. Conclusion This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.

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Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

Bin,

Wu,

Huang

et al. 2024

BMC Med Imaging

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Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters

Cited by 6 publications

References 9 publications

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

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