Radiomics studies, using features extracted from medical images, are often used for outcome prediction in oncology. Studies frequently use physical phantoms to assess radiomic feature reliability, however, few studies have utilized computer-generated phantoms to assess the impact of image acquisition parameters. Additionally, studies have introduced deep learning approaches to generate CT-realistic textures on computer-generated phantoms. Therefore, we aimed to assess the feasibility of using 4D extended cardiac-torso (XCAT) phantoms with generated realistic textures using a deep learning network adapted from a previous study to analyze the impact of slice thickness on radiomic features. Our dataset consisted of 70 organ maps (training: n=50, validation: n=20) generated from CT images of lung cancer patients. These were used as input for a dual-discriminator conditional-generative adversarial network to synthesize realistic textures in the organ maps. The validated network was used to generate realistic-textured XCAT phantoms. The phantoms were reconstructed using three different slice thicknesses. Pyradiomics was used to extract radiomics features from the tumor of each XCAT phantom. The intraclass correlation coefficient was used to assess the feature reliability for each acquisition protocol. Qualitatively, the generated XCAT phantoms had similar textures to that of the real CT images. The features demonstrated excellent reliability between each acquisition protocol for most feature types with GLCM texture features only showing moderate reliability, however, this may be due to the small sample size of the study. This study showed the feasibility of using generated realistic-textured XCAT phantoms to study the impact of acquisition protocols on radiomic features.
PurposeWe developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC).ApproachWe retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort (n = 101), validated in the testing cohort (n = 34) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan–Meier analysis.ResultsThe model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 (p < 0.005) and 0.60 (p = 0.008), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training (p < 0.005) and the testing (p = 0.03) cohorts.ConclusionsOur radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.
Radiomic studies utilize AI and quantitative features from medical images to create models that can predict patient outcomes. An integral step in these radiomic studies is the delineation of the regions of interest where the features are extracted. Manual segmentation is labor intensive and time-consuming for large studies. Semi-automatic segmentation tools have been used in recent radiomic studies to achieve more reproducible segmentations and robust radiomics features. However, for the segmentation of lung tumors on CT images, tools in the literature are difficult to find publicly and require extensive user interaction. Therefore, we aimed to build a semi-automatic segmentation tool which was intuitive, fast, and required minimal user interaction. We used one dataset to develop the segmentation algorithm on (n=49), and another to test its performance (n=144). All 144 tumors were segmented on the CT images using the semiautomatic tool by three separate users. A gold standard tumor delineation was determined by a trained radiologist. The segmentation robustness was assessed using the Dice, mean absolute boundary distance (MAD) and volume difference (VD). A total of 408 radiomic features were extracted and feature robustness was determined using an intra-class correlation coefficient (ICC) greater than 0.8. The developed tool achieved an average Dice of 0.90, MAD of 0.62 mm and a VD of 0.97 ml between the three users. A total of 181 (76%) of the extracted features displayed excellent reliability. This tool has the potential to augment the reliability of radiomic studies by making segmentations and feature sets more reproducible.
Non-small cell lung cancer (NSCLC) is one of the leading causes of death worldwide. Medical imaging is used to determine cancer staging; however, these images may hold additional information which could be utilized to aid in outcome prediction. A multi-modality radiomics approach incorporating quantitative and qualitative features from the tumor and its surrounding regions, along with clinical features, has yet to be explored. Therefore, we hypothesize that a model containing CT and PET radiomic features, in addition to clinical and qualitative features, has the potential improve risk-stratification of NSCLC patients better than cancer stage alone. Our dataset consisted of 135 NSCLC patients (training: n=94, testing: n=41) who underwent surgical resection. Each region of interest was segmented using a semi-automatic approach on both the pre-treatment CT and PET images. Radiomic features were extracted using the Quantitative Image Feature Engine. A total of 1030 features were extracted including clinical, qualitative, and radiomic features. LASSO regression was used to identify the top features to predict time to recurrence in the training cohort and the model was evaluated in the testing cohort. A total of nine features were selected, including two clinical, one CT, and six PET radiomic features. The model achieved a concordance of 0.81 in the training cohort, which was validated in the testing cohort (concordance=0.79) and outperformed stage alone (concordances=0.68-0.69). This model has the potential to assist physicians in risk-stratifying patients with NSCLC and could be used to identify patients that may benefit from more aggressive or personalized treatment options.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.