Challenges and research opportunities for lung cancer screening in China

Wang, Zixing; Wang, Yuyan; Huang, Yao; Xue, Fang; Han, Wei; Hu, Yimin; Wang, Lei; Song, Wei; Jiang, Jingmei

doi:10.1186/s40880-018-0305-0

Cited by 12 publications

(7 citation statements)

References 41 publications

(34 reference statements)

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“…A multidimensional model incorporating radiomics score and liquid biopsy for the prediction of malignant and benign pulmonary nodules advanced stages by diagnosis, resulting in a low 5-year overall survival rate (1,2), whereas patients with stage I lung cancer who undergo surgery have a 10-year survival rate of 92% (3). Therefore, the early screening and treatment of people at high risk for lung cancer can improve patient prognosis and reduce the risk of death from lung cancer (4,5). The benefits demonstrated in the National Lung Screening Trial have led to the widespread implementation of lung cancer screening worldwide (6).…”

Section: Original Articlementioning

confidence: 99%

A multidimensional model incorporating radiomics score and liquid biopsy for the prediction of malignant and benign pulmonary nodules

Xu¹,

Zhou²,

Wang³

et al. 2022

Transl Cancer Res TCR

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Background: As the lesions in pulmonary nodules (PNs) are small and the clinical manifestations lack specificity, the etiology of PNs is complex, predisposing them to misdiagnoses missed diagnoses. Thus, the diagnosis and treatment of PNs remains challenging and an important clinical problem. Methods: This study prospectively enrolled 156 patients with computed tomography (CT)-diagnosed PNs who underwent circulating genetically abnormal cell (CAC) testing between January 2020 and December 2021. We collected data on clinical features closely related to the nature of PNs, such as age, smoking history, and type of nodule. All internal regions of interest (ROIs) of PNs in this study were segmented. Radiomic feature extraction was performed on the ROIs, and a radiomics model was constructed using least absolute shrinkage and selection operator (LASSO) regression to obtain a radiomics score (Rad-score). A comprehensive model combining clinical features, Rad-score, and liquid biopsy was constructed using logistic regression analysis. The diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curves. Results: In this study, 5 radiomics features were screened for model construction. The area under the ROC curve (AUC) of the radiomics model was 0.844 [95% confidence interval (CI): 0.766-0.915] in the training set. The Rad-score, clinical features, and CAC were further combined to construct a multidimensional analysis model. The AUC of the synthesized model was 0.943 (95% CI: 0.881-0.978) in the training set. Conclusions: A multidimensional model is an effective tool for the noninvasive diagnosis of malignant PNs. The validation and combination of multiple diagnostic methods is a productive avenue of research trend for the identification of malignant PNs.

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Section: Original Articlementioning

confidence: 99%

A multidimensional model incorporating radiomics score and liquid biopsy for the prediction of malignant and benign pulmonary nodules

Xu¹,

Zhou²,

Wang³

et al. 2022

Transl Cancer Res TCR

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“…1,2 The key to lowering lung cancer mortality is early detection and treatment. [3][4][5] However, because of the lack of typical symptoms of early lung cancer, patients frequently miss the best time for diagnosis and treatment. 6 Previous studies have reported that a low-dose computed tomography (CT) examination can detect early lung cancer and significantly reduce mortality.…”

Section: Introductionmentioning

confidence: 99%

“…Lung cancer continues to be the leading cause of cancer‐related death worldwide 1,2 . The key to lowering lung cancer mortality is early detection and treatment 3–5 . However, because of the lack of typical symptoms of early lung cancer, patients frequently miss the best time for diagnosis and treatment 6 .…”

Section: Introductionmentioning

confidence: 99%

A combined non‐enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub‐centimeter pulmonary solid nodules

Lin

Zheng

et al. 2023

Medical Physics

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Background Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground‐glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub‐centimeter solid nodules, is rare. Purpose This study aims to develop a radiomics model based on non‐enhanced CT images that can distinguish between benign and malignant sub‐centimeter pulmonary solid nodules (SPSNs, <1 cm). Methods The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non‐enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non‐enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver‐operating characteristic curve (AUC). Results The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862–0.954) in the training set and an AUC of 0.877 (95% CI, 0.817–0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906–0.969) in the training set and an AUC of 0.903 (95% CI, 0.857–0.944) in the testing set. Conclusions Radiomics features based on non‐enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.

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“…Risk-based or personalized screening approaches are being studied to overcome these challenges [ 6 ]. We believe that a deeper understanding of the spectrum of risk factors for LC and applying technologies such as machine learning and knowledge graphs (KGs) will generate more cost-effective screening solutions.…”

Section: Introductionmentioning

confidence: 99%

The Generation of a Lung Cancer Health Factor Distribution Using Patient Graphs Constructed From Electronic Medical Records: Retrospective Study

Chen¹,

Huang²,

Wu³

et al. 2022

J Med Internet Res

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Background Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC. Objective We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients. Methods The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor’s relationship to LC. Results The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient’s health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature. Conclusions By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

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Challenges and research opportunities for lung cancer screening in China

Cited by 12 publications

References 41 publications

A multidimensional model incorporating radiomics score and liquid biopsy for the prediction of malignant and benign pulmonary nodules

A multidimensional model incorporating radiomics score and liquid biopsy for the prediction of malignant and benign pulmonary nodules

A combined non‐enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub‐centimeter pulmonary solid nodules

The Generation of a Lung Cancer Health Factor Distribution Using Patient Graphs Constructed From Electronic Medical Records: Retrospective Study

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