Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma

Abstract

Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis

“…However, to our knowledge, there has been a limited number of studies using textural heterogeneity difference as an imaging biomarker in assessment of pulmonary nodules and masses. This novel texture analysis method in our study was first described by Grove and co-workers (17), who demonstrated the potential prognostic value of entropy ratio as an intratumor intensity variation feature. Spatially explicit texture analysis subdividing the entire lesion into spatially distinct regions (eg, core and edge) based on the imaging characteristics may facilitate the intralesional habitat characterization (18).…”

“…Because contrast-enhanced computed tomography (CECT) with the use of iodine contrast agent gives insight into lesion heterogeneity related to the presence of areas with different vascularization, it can be hypothesized that hyperintensity on CECT presents high vascularization and hypointensity corresponds to low vascularization (19). Additionally, as described by Grove et al and Gatenby et al, subdividing the pulmonary lesion into core and edge regions can separately evaluate the spatially explicit biological processes and reveal varying textural behavior across the whole lesion (17,18). Therefore, the purpose of our study was to investigate the value of heterogeneity difference between edge and core by using whole-lesion texture analysis (including intensity and entropy features) on CECT for differentiation of malignant from inflammatory pulmonary nodules and masses.…”

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

“…[3][4][5] Models built using radiomics features, or imaging features, are one novel approach for identifying patients with the highest risk for disease progression, poor survival, or other clinical outcomes. [6][7][8][9][10][11][12] Radiomics features are extracted from the region-of-interest (ROI) in an image in order to assign a quantitative value to that ROI. Data mining and machine learning techniques are then used to build models and capture valuable insights from those imaging features.…”

Automated 3-D Tissue Segmentation Via Clustering

“…From this repository, we obtained three full body series that were utilized for brain extraction. The second database from this archive, named LungCT-Diagnosis, contained series of scans with a slice thickness between three to six millimeters that were taken for diagnosis prior to surgery [22]. These images were enhanced with a contrast medium, allowing the heart and liver to be segmented in addition to the lungs.…”