Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images

Timmeren, Janita E. van; Leijenaar, Ralph T.H.; Elmpt, Wouter van; Reymen, Bart; Oberije, Cary; Monshouwer, R.; Bussink, Johan; Brink, Carsten; Hansen, Olfred; Lambin, Philippe

doi:10.1016/j.radonc.2017.04.016

Cited by 139 publications

(118 citation statements)

References 34 publications

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“…Previous studies have shown that the CBCT imaging features could assess response to treatment and serve as an early biomarker . By summarizing previous research findings on NSCLC, we found that the radiomics features of CT and CBCT were interchangeable and that the radiomics features of the CBCT prior to the first fraction of treatment showed prognostic information for the overall survival of NSCLC patients acquired . The radiomics features of CBCTs acquired early during a course of treatment may be associated with overall survival in locally advanced NSCLC …”

Section: Discussionsupporting

confidence: 67%

Cone‐beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients

et al. 2020

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Background Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. Methods A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. Results Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. Conclusions Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. Key points Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.

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Section: Discussionsupporting

confidence: 67%

Cone‐beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients

et al. 2020

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“…Textural features were divided in five neighborhood gray-tone difference (NGTDM) features, sixteen neighboring gray-level dependence matrix (NGLDM) features, sixteen graylevel size zone matrix (GLSZM) features, sixteen gray-level run length matrix (GLRLM) features, sixteen gray-level distance zone matrix (GLDZM) features and 26 gray-level co-occurrence matrix (GLCM) features. The definitions of the radiomic features are previously described in van Timmeren et al [14].…”

Section: Feature Extractionmentioning

confidence: 99%

Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study

et al. 2017

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Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study, Acta Oncologica, 56:11, 1544-1553, DOI: 10.1080 Background: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition-and reconstruction protocols influences robustness of radiomic analyses. The aim of this study was to investigate the influence of different CT-scanners, slice thicknesses, exposures and gray-level discretization on radiomic feature values and their stability. Material and methods: A texture phantom with ten different inserts was scanned on nine different CT-scanners with varying tube currents. Scans were reconstructed with 1.5 mm or 3 mm slice thickness. Image pre-processing comprised gray-level discretization in ten different bin widths ranging from 5 to 50 HU and different resampling methods (i.e., linear, cubic and nearest neighbor interpolation to 1 Â 1 Â 3 mm 3 voxels) were investigated. Subsequently, 114 textural radiomic features were extracted from a 2.1 cm 3 sphere in the center of each insert. The influence of slice thickness, exposure and bin width on feature values was investigated. Feature stability was assessed by calculating the concordance correlation coefficient (CCC) in a test-retest setting and for different combinations of scanners, tube currents and slice thicknesses. Results: Bin width influenced feature values, but this only had a marginal effect on the total number of stable features (CCC > 0.85) when comparing different scanners, slice thicknesses or exposures. Most radiomic features were affected by slice thickness, but this effect could be reduced by resampling the CT-images before feature extraction. Statistics feature 'energy' was the most dependent on slice thickness. No clear correlation between feature values and exposures was observed. Conclusions: CT-scanner, slice thickness and bin width affected radiomic feature values, whereas no effect of exposure was observed. Optimization of gray-level discretization to potentially improve prognostic value can be performed without compromising feature stability. Resampling images prior to feature extraction decreases the variability of radiomic features.

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“…Current research in the area of radiation response assessment has demonstrated that radiomics, defined as postprocessing for high‐throughput extraction of textural and intensity‐based information from medical images, can potentially identify the biomarkers of diagnosis and response for patients with cancer . Studies have shown that radiomic features extracted from CT images can be correlated with pathologic information patient overall survival, gene expression, cancer staging, and response assessment . Recent research has focused on the role of radiomics toward increasing the performance and stability of feature classifiers for predicting overall survival in head and neck cancer patients .…”

Section: Introductionmentioning

confidence: 99%

“…31,32 Studies have shown that radiomic features extracted from CT images can be correlated with pathologic information 33 patient overall survival, 30,32,[34][35][36][37][38][39] gene expression, 30,38,40 cancer staging, 41 and response assessment. 31,[42][43][44][45] Recent research has focused on the role of radiomics toward increasing the performance and stability of feature classifiers for predicting overall survival in head and neck cancer patients. 22,[46][47][48][49] Identification of optimal classification methods and the best radiomic features for prognostic analyses in head and neck cancer patients may indeed broaden the scope of radiomics in precision oncology.…”

Section: Introductionmentioning

confidence: 99%

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

et al. 2020

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Purpose To perform radiomic analysis of primary tumors extracted from pretreatment contrast‐enhanced computed tomography (CE‐CT) images for patients with oropharyngeal cancers to identify discriminant features and construct an optimal classifier for the characterization and prediction of human papilloma virus (HPV) status. Materials and methods One hundred and eighty seven patients with oropharyngeal cancers with known HPV status (confirmed by immunohistochemistry—p16 protein testing) were retrospectively studied as follows: Group A: 95 patients (19HPV− and 76HPV+) from the MICAII grand challenge. Group B: 92 patients (52HPV− and 40HPV+) from our institution. Radiomic features (172) were extracted from pretreatment diagnostic CE‐CT images of the gross tumor volume (GTV). Levene and Kolmogorov–Smirnov's tests with absolute biserial correlation (>0.48) were used to identify the discriminant features between the HPV+ and HPV− groups. The discriminant features were used to train and test eight different classifiers. Area under receiver operating characteristic (AUC), positive predictive and negative predictive values (PPV and NPV, respectively) were used to evaluate the performance of the classifiers. Principal component analysis (PCA) was applied on the discriminant feature set and seven PCs were used to train and test a generalized linear model (GLM) classifier. Results Among 172 radiomic features only 12 radiomic features (from 3 categories) were significantly different (P < 0.05, |BSC| > 0.48) between the HPV+ and HPV− groups. Among the eight classifiers trained and applied for prediction of HPV status, the GLM showed the highest performance for each discriminant feature and the combined 12 features: AUC/PPV/NPV = 0.878/0.834/0.811. The GLM high prediction power was AUC/PPV/NPV = 0.849/0.731/0.788 and AUC/PPV/NPV = 0.869/0.807/0.870 for unseen test datasets for groups A and B, respectively. After eliminating the correlation among discriminant features by applying PCA analysis, the performance of the GLM was improved by 3.3%, 2.2%, and 1.8% for AUC, PPV, and NPV, respectively. Conclusions Results imply that GTV's for HPV+ patients exhibit higher intensities, smaller lesion size, greater sphericity/roundness, and higher spatial intensity‐variation/heterogeneity. Results are suggestive that radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on contrast‐enhanced diagnostic CT datasets may be potentially used for classification of HPV status.

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Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images

Cited by 139 publications

References 34 publications

Cone‐beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients

Cone‐beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients

Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

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