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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.

[This corrects the article DOI: 10.1371/journal.pone.0118261.].

Combinations of targeted drugs have been employed to treat sarcomas, however, response rates have not improved notably, therefore emphasizing the need for novel treatments. In addition, imaging approaches to assess therapeutic response is lacking, as currently measurable indices, such as volume and/or diameter, do not accurately correlate with changes in tumor biology. In this study, quantitative and profound analyses of magnetic resonance imaging (MRI) were developed to evaluate these as imaging biomarkers for MK1775 and Gem in an osteosarcoma xenotransplant model at early time-points following treatment. Notably, we showed that Gem and Gem+MK1775 groups had significantly inhibited tumor growth by day 4, which was presaged by elevations in mean ADC by 24 hours post treatment. Significant differences were also observed at later time points for the Gem+MK1775 combination and MK1775 therapy. ADC distribution and entropy (randomness of ADC values) were also elevated by 24 hours following therapy. Immunohistochemistry demonstrated that these treatment-related increases in ADC correlated with apoptosis and observed cell condensations (dense- and exploded bodies). These findings underline the role of ADC as a quantitative imaging biomarker for therapy-induced response and show promising clinical relevance in the sarcoma patient population.

Ongoing developments in the field of medical imaging modalities have pushed the frontiers of modern medicine and biomedical engineering, prompting the need for new applications to improve diagnosis, treatment and prevention of diseases. Biomedical data visualization and modeling rely predominately on manual processing and utilization of voxel and facet based homogeneous models. Biological structures are naturally heterogeneous and in order to accurately design and biomimic biological structures, properties such as chemical composition, size and shape of biological constituents need to be incorporated in the computational biological models. Our proposed approach involves generating a density point cloud based on the intensity variations in a medical image slice, to capture tissue density variations through point cloud densities. The density point cloud is ordered and approximated with a set of cross-sectional least-squares B-Spline curves, based on which a skinned B-Spline surface is generated. The aim of this method is to capture and accurately represent density variations within the medical image data with a lofted surface function. The fitted B-Spline surface is sampled at uniformly distributed parameters, and our preliminary results indicate that the bio-CAD model preserves the density variations of the original image based point cloud. The resultant surface can thus be visualized by mapping the density in the parametric domain into color in pixel domain. The B-Spline function produced from each image slice can be used for medical visualization and heterogeneous tissue modeling. The process can be repeated for each slice in the medical dataset to produce heterogeneous B-Spline volumes. The emphasis of this research is placed on accuracy and shape fidelity needed for medical operations.

Combinations of targeted drugs have been employed to treat sarcomas, however, response rates have not improved notably, therefore emphasizing the need for novel treatments. In addition, imaging approaches to assess therapeutic response is lacking, as currently measurable indices, such as volume and/or diameter, do not accurately correlate with changes in tumor biology. In this study, quantitative and profound analyses of magnetic resonance imaging (MRI) were developed to evaluate these as imaging biomarkers for MK1775 and Gem in an osteosarcoma xenotransplant model at early time-points following treatment. Notably, we showed that Gem and Gem+MK1775 groups had significantly inhibited tumor growth by day 4, which was presaged by elevations in mean ADC by 24 hours post treatment. Significant differences were also observed at later time points for the Gem+MK1775 combination and MK1775 therapy. ADC distribution and entropy (randomness of ADC values) were also elevated by 24 hours following therapy. Immunohistochemistry demonstrated that these treatment-related increases in ADC correlated with apoptosis and observed cell condensations (dense- and exploded bodies). These findings underline the role of ADC as a quantitative imaging biomarker for therapy-induced response and show promising clinical relevance in the sarcoma patient population.