CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer

Zhou, TaoHu; Tu, Wenting; Dong, Peng; Duan, Shaofeng; Zhou, Xiuxiu; Ma, Yanqing; Wang, Yun; Liu, Tian; Zhang, Hanxiao; Feng, Yan; Huang, WenJun; Ge, YanMing; Liu, Shiyuan; Li, Zhaobin; Fan, Li

doi:10.1016/j.acra.2023.03.021

Cited by 5 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We eventually identified 15 optimal radiomic features; notably, many features used in our model were high-order statistical features (online suppl. Table S4); these features can accurately reflect subtle changes in tissue biology and morphology [33, 34]. Our research demonstrated that MPE exhibits more pronounced heterogeneity and an abnormal distribution of grayscale levels compared to BPE.…”

Section: Discussionmentioning

confidence: 77%

An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion

Cai,

Cheng,

Liao

et al. 2024

Respiration

View full text Add to dashboard Cite

Introduction: Distinguishing between malignant pleural effusion (MPE) and benign pleural effusion (BPE) poses a challenge in clinical practice. We aimed to construct and validate a combined model integrating radiomic features and clinical factors using computerized tomography (CT) images to differentiate between MPE and BPE. Methods: A retrospective inclusion of 315 patients with pleural effusion (PE) was conducted in this study (training cohort: n = 220; test cohort: n = 95). Radiomic features were extracted from CT images, and the dimensionality reduction and selection processes were carried out to obtain the optimal radiomic features. Logistic regression (LR), support vector machine (SVM), and random forest were employed to construct radiomic models. LR analyses were utilized to identify independent clinical risk factors to develop a clinical model. The combined model was created by integrating the optimal radiomic features with the independent clinical predictive factors. The discriminative ability of each model was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results: Out of the total 1,834 radiomic features extracted, 15 optimal radiomic features explicitly related to MPE were picked to develop the radiomic model. Among the radiomic models, the SVM model demonstrated the highest predictive performance [area under the curve (AUC), training cohort: 0.876, test cohort: 0.774]. Six clinically independent predictive factors, including age, effusion laterality, procalcitonin, carcinoembryonic antigen, carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE), were selected for constructing the clinical model. The combined model (AUC: 0.932, 0.870) exhibited superior discriminative performance in the training and test cohorts compared to the clinical model (AUC: 0.850, 0.820) and the radiomic model (AUC: 0.876, 0.774). The calibration curves and DCA further confirmed the practicality of the combined model. Conclusion: This study presented the development and validation of a combined model for distinguishing MPE and BPE. The combined model was a powerful tool for assisting in the clinical diagnosis of PE patients.

show abstract

Section: Discussionmentioning

confidence: 77%

An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion

Cai,

Cheng,

Liao

et al. 2024

Respiration

View full text Add to dashboard Cite

show abstract

“…The advent of deep learning has revolutionized the data extraction process and alleviated observer variability concerns [56]. Studies that employed deep learning algorithms with CT data for COPD detection had achieved notable success with AUCs of 0.87, 0.90 [57], and 0.927 [58], which were relatively good. However, previous studies have predominantly focused on radiomics features.…”

Section: Discussionmentioning

confidence: 99%

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease

Zhu,

Zhao,

et al. 2024

Respir Res

View full text Add to dashboard Cite

Background Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. Methods We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). Results The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. Conclusion We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. Trial registration Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

show abstract

Computed tomography radiomics-based prognosis prediction of patients with chronic obstructive pulmonary disease

Zhao,

Qiu

2024

Journal of Radiation Research and Applied Sciences

View full text Add to dashboard Cite

CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer

Cited by 5 publications

References 25 publications

An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion

An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease

Computed tomography radiomics-based prognosis prediction of patients with chronic obstructive pulmonary disease

Contact Info

Product

Resources

About