Molecular Imaging Visualization of Tumor Heterogeneity in Non-small Cell Lung Cancer
试验速览
- 阶段
- 不适用
- 入组人数
- 150
- 试验地点
- 1
- 主要终点
- Radiomic feature selection and model establishment
研究概览
简要总结
To assess the potential usefulness of radiogenomics for tumor driving genes heterogeneity in non-small cell lung cancer.
详细描述
Patients with advanced NSCLC underwent 18F-FDG PET/CT and PET/CT-guided synchronous targeted biopsy of primary and distant metastatic tumors. The LIFEx package was used to extract PET and CT radiomic features from primary and metastatic lesions. The radiomic ROI sites of primary and distant metastatic tumors were point-to-point corresponding to the PET/ CT-guided targeted biopsy sites. Whole exon sequencing of primary and distant metastatic tumor samples obtained by PET/CT-guided targeted biopsy was used to get genomic data of primary and distant metastatic tumor. Predictive radiogenomics models were established and validation.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •(i) adult patients (aged 18 years or order);
- •(ii) patients with suspected or newly diagnosed or previously treated malignant tumors (supporting evidence may include magnetic resonance imaging (MRI), CT, tumor markers and pathology report);
- •(iii) patients who had scheduled both 18F-FDG PET/CT scans and PET/CT guided biopsy;
- •(iv) patients who were able to provide informed consent (signed by participant, parent or legal representative) and assent according to the guidelines of the Clinical Research Ethics Committee.
排除标准
- •(i) patients with non-malignant lesions;
- •(ii) patients with pregnancy;
- •(iii) the inability or unwillingness of the research participant, parent or legal representative to provide written informed consent.
结局指标
主要结局
Radiomic feature selection and model establishment
时间窗: 3 years
In this study, the investigators first selected the features with significant differences between genes mutant and wild type in the training set using the Mann-Whitney U test, obtaining a total of 53 features with p value \< 0.05. Then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal predictive features among the 53 selected in the training set. The LASSO algorithm adds a L1 regularization term to a least square algorithm to avoid overfitting. A prediction model was established by logistic regression, and the radiomics signature score (rad-score) for each participant was calculated based on the selected discriminating radiomic features. The model performance was tested in the validation set. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the model performance in the training and validation sets.
次要结局
- Radiomic feature extraction(30 days)
- Genes mutation detection(30 days)
- Image acquisition(30 days)