Browsing by Subject "non-small cell lung cancer"

Background: Quantitative medical imaging has been increasingly utilized in modern medicine. The field of radiomics is an emerging subset of quantitative medical imaging. Radiomics can identify a large number of quantitative features as the biomarkers from the radiography images. Subsequent mining and analysis of these features may potentially uncover subclinical tumor characteristics of disease in a non-invasive manner, and provide clinical decision support. Biomarkers on gross tumor regions have been studied for many years for different clinical endpoints. Correlations between the radiomic features and treatment outcomes, disease diagnosis, pathological information, and organ functions can be studied for clinical decision support. Peritumor region is the region around the gross tumor, where may also be used to extract radiomics biomarkers. Currently, limited studies are available in this area. Machine learning is a powerful tool to correlate radiomics features with various clinical endpoints and provide a predictive model to better define tumor characteristics.

Purpose: To investigate the association between CT radiomic data from peritumoral regions and non-local treatment failure recurrence of early stage non-small cell lung cancers (NSCLC) following lung stereotactic body radiation therapy (SBRT), to identify secondary quantitative parameters from the dose-driven peritumoral CT radiomic data, to compare the model performance of using radiomic data with and without secondary quantitative parameters for assessing the non-local treatment failure following lung SBRT.

Materials and Methods: Sixty-three patients who received SBRT for early-stage NSCLC were retrospectively identified by an IRB approved clinical trial with treatment outcome provided. Treatment failure was defined as both local cancer recurrence and non-local cancer recurrence following SBRT. Gross Tumor Volumes (GTVs) were segmented on the pre-treatment free-breathing CT images by radiation oncologists. Two types of peritumoral volumes were defined on pre-treatment free-breathing CT: uniform ring and dose-driven. The uniform ring peritumoral volumes were generated by expanding GTVs at radial distances of 3mm, 6mm, 9mm and 12mm. The dose-driven peritumoral volumes were generated by converting ten isodose volumes (100%, 98%, 95%, 90%, 85%, 80%, 70%, 50%, 30%, and 10%) into structures in the treatment planning system. All peritumoral volumes were then modified using a Boolean process to exclude the GTV and non-lung tissue. Sixty radiomic features (4 Intensity, 21 GLCOM, 11 GLRLM, 13 GLSZM, 5 NGLDM, and 6 Shape) were extracted from GTVs and peritumoral volumes using an in-house radiomics calculation platform as biomarkers for cancer recurrence. A univariate feature selection was used to eliminate highly correlated features. Two multivariate machine learning based feature selection algorithms were followed to find the important features and to avoid overfitting. Machine learning algorithms (Random Forest, LASSO Logistic Regression, Ridge Logistic Regression, Linear Support Vector Machine and Kernel Support Vector Machine) were used to build classification models to predict non-local treatment failure. 63 patients were randomly separated into the training group (75%) and the test group (25%). Model generalization was cross validated using a stratified 10-fold with 50 iterations. The learning curve and grid search were used to find the best hyperparameters to enhance the model performance and avoid the overfitting in the random forest model. The performance of each feature selection and classifier combination was based on the area under the receiver operating characteristic curve (ROC). Pair t-tests with 100 iteration permutation tests were used to evaluate the significant difference between different AUCs.

Results: In uniform expansion ring peritumoral region, from the results, 3mm ring was more predictive of non-local treatment failure than all peritumoral radiomic data and gross tumor radiomic data. In dose-driven peritumoral region, from the results, 80% isodose region was more predictive of non-local treatment failure than all peritumoral radiomic data and gross tumor radiomic data. The model combinations of tree-based feature selection and Random Forest classifier presented the highest AUC values. No significant difference in predictive performance was found before and after adding secondary features in the dataset (p-value > 0.05). However, secondary features may be useful in other studies given that obvious patterns were seen in the clustering heatmap.

Conclusions: This study has demonstrated strong prognostic value of peritumoral radiomics for non-local treatment failure in patients with stage I NSCLC. The presented peritumoral radiomics was shown to have better predictive performance compared to the gross tumor radiomics.

Keywords: Radiomics, peritumoral region, machine learning, stereotactic body radiation therapy, non-small cell lung cancer