Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer

Tao, Junli; Lv, Rongfei; Liang, Changyu; Fang, Jiayang; Liu, Daihong; Xiao, Lan; Huang, Houpei; Zhang, Jiuquan

doi:10.1016/j.acra.2020.12.007

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Finally, efforts have been made to reduce the risk of radiomics feature biases and improve the quality of the prediction models. A wide range of candidate radiomics features were extracted in our study, which provided the foundation for algorithms to select relevant radiomics features and obtained the valuable information to reflect the local control status of lung cancer lesions (24). In order to reduce the deviation of the interobservers and examine the feature stability, we calculated the ICC of radiomics features (44).…”

Section: Discussionmentioning

confidence: 99%

“…The images which were used to extract the radiomics features could be either the original image or the derivative filtered images including Laplacian of Gaussian (LoG), Wavelet, Square, SquareRoot, Logarithm. Collectively, the feature types extracted from each image type include shape features provided the geometric volume of ROI, first-order features described the individual voxel value distribution in the intensity histogram of ROI, texture features reflected the organization and arrangement of the surface structure with slow change or periodic change, including gray-level co-occurrence matrix (GLCM), gray-level dependence matrix (GLDM), gray-level run length matrix (GLRLM), graylevel size zone matrix (GLSZM), and neighborhood gray-tone difference matrix (NGTDM) (24). In order to ensure the repeatability of the results, the images and features were resampled and z-score normalized respectively.…”

Section: Ct Image Acquisition Region Of Interest Segmentation and Quantitative Radiomics Features Extractionmentioning

confidence: 99%

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A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters

et al. 2022

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PurposeStereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters.MethodsThe radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve.ResultsThe LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility.ConclusionsThe combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.

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Section: Discussionmentioning

confidence: 99%

Section: Ct Image Acquisition Region Of Interest Segmentation and Quantitative Radiomics Features Extractionmentioning

confidence: 99%

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters

et al. 2022

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“…Whether the density of primary lesions is uniform also reflects the difference in the growth and proliferation rate of malignant tumor cells and tumor burden to some extent. When the tumor shows partial liquefaction and necrosis, it indicates that the tumor is overloaded, and the metastatic potential is closely related to the tumor burden [ 23 ]. The pleural depression sign is caused by the scar tissue in the tumor pulling the adjacent visceral pleura.…”

Section: Discussionmentioning

confidence: 99%

[Retracted] Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

Chen

et al. 2022

Contrast Media & Molecular Imaging

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Objective. To explore the CT radiomic features and clinical imaging features of the primary tumor in patients with nonsmall cell lung cancer (NSCLC) before treatment and their predictive value for the occurrence of bone metastases. Methods. From June 2017 to June 2021, 195 patients with NSCLC who were pathologically diagnosed without any treatment in the Cancer Hospital Affiliated to Hainan Medical College were retrospectively analyzed, and they were divided into a bone metastasis group and a nonbone metastasis group. The relationship between clinical imaging features and bone metastasis in patients was analyzed by the t-test, rank sum test, and χ2 test. At the same time, ITK software was used to extract the radiomic characteristics of the primary tumor of the patients, and the patients were randomly divided into a training group and a validation group in a ratio of 7 : 3. The training model was validated in the validation group, and the performance of the model for predicting bone metastases in NSCLC patients was verified by the ROC curve, and a multivariate logistic regression prediction model was established based on the omics parameters extracted from the best prediction model combined with clinical image features. Results. Seven features were screened from the primary tumor by LASSO to establish a model for predicting metastasis. The area under the curve was 0.82 and 0.73 in the training and validation sets. The best omics signature and univariate analysis suggested clinical imaging factors P < 0.05 associated with bone metastases were included in multivariate binary logistic analysis to obtain clinical characteristics of the primary tumor such as gender (OR = 0.141, 95% CI: 0.022–0.919, P = 0.04), increased Cyfra21-1 (OR = 0.12, 95% CI: 0.018–0.782, P = 0.027), Fe content in blood (OR = 0.774, 95% CI: 0.626–0.958, P = 0.018), CT signs such as lesion homogeneity (OR = 0.052, 95% CI: 0.006–0.419, P = 0.006), pleural indentation sign (OR = 0.007, 95% CI: 0.001–0.696, P = 0.034), and omics characteristics glszm_Small Area High Gray Level Emphasis (OR = 0.016, 95% CI: 0.001–0.286, P = 0.005) were independent risk factors for bone metastasis in patients. Conclusion. The prediction model established based on radiomics and clinical imaging features has high predictive performance for the occurrence of bone metastasis in NSCLC patients.

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“…Previous studies have suggested that the radiomics signature combined with clinical risk factors can significantly improve the performance of various clinical tasks related to lung cancer ( 31 - 34 ). For instance, a combined model incorporating CT-based radiomics and clinical features outperforms CT-based radiomics alone for predicting distant metastases of NSCLC ( 35 ), estimating BM-free survival of curatively resected locally advanced NSCLC ( 9 ), and predicting survival after whole brain radiotherapy of NSCLC patients with BM ( 8 ). In this study, the results of univariate analyses of clinical risk factors showed that the primary tumor location had significant correlations with occult BM status and that the upper lobe, including the upper right and left lobe, were the most frequently occurring primary metastatic sites for BM, occurring at a rate of 47%.…”

Section: Discussionmentioning

confidence: 99%

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Cong¹,

Qiu²,

Li³

et al. 2021

Transl Cancer Res TCR

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Background: To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).Methods: One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra-and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.Results: A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI), 0.903-0.919)] and an AP of 0.885 (95% CI, 0.876-0.894) in the training cohort, and an AUC of 0.873 (95% CI, 0.866-0.80) and an AP of 0.827 (95% CI, 0.820-0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427). Conclusions:The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.

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Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer

Cited by 7 publications

References 34 publications

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters

[Retracted] Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

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