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Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling.

ABSTRACT: Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model-based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time-to-tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologist blinded to the form data. Response Evaluation Criteria in Solid Tumors (RECIST)-based progression-free survival (PFS) measures were perfectly concordant between the original forms data and the blinded radiologist re-evaluation (intraclass correlation coefficient = 1), but these routine interrater differences in the identification and measurement of target lesions were associated with an average 18-week delay (range, -20 to 55 weeks) in TTG (intraclass correlation coefficient = 0.32). To exploit computational metrics for improving statistical power in small clinical trials will require increased precision of tumor burden assessments.

SUBMITTER: Li CH

PROVIDER: S-EPMC4760886 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling.

Li C H CH Bies R R RR Wang Y Y Sharma M R MR Karovic S S Werk L L Edelman M J MJ Miller A A AA Vokes E E EE Oto A A Ratain M J MJ Schwartz L H LH Maitland M L ML

Clinical and translational science 20160121 1

Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model-based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time-to-tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologi ...[more]

PMID: 26790562

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Project description:PurposeTo examine the correlations between uni-dimensional RECIST and volumetric measurements in patients with lung adenocarcinoma and to assess their association with overall survival (OS) and progression-free survival (PFS).Materials and methodsIn this study of patients receiving chemotherapy for lung cancer in the setting of a clinical trial, response was prospectively evaluated using RECIST 1.0. Retrospectively, volumetric measurements were recorded and response was assessed by two different volumetric methods at each followup CT scan using a semi-automated segmentation algorithm. We subsequently evaluated the correlation between the uni-dimensional RECIST measurements and the volumetric measurements and performed landmark analyses for OS and PFS at the completion of the first and second follow-ups. Kaplan-Meier curves together with log-rank tests were used to evaluate the association between the different response criteria and patient outcome.ResultsForty-two patients had CT scans at baseline, after the first follow up scan and second followup scan, and then every 8 weeks. The uni-dimensional RECIST measurements and volumetric measurements were strongly correlated, with a Spearman correlation coefficient (ρ) of 0.853 at baseline, ρ=0.861 at the first followup, ρ=0.843 at the 2nd followup, and ρ=0.887 overall between-subject. On first follow-up CT, partial responders and non responders as assessed by an "ellipsoid" volumetric criteria showed a significant difference in OS (p=0.008, 1-year OS of 70% for partial responders and 46% for non responders). There was no difference between the groups when assessed by RECIST criteria on first follow-up CT (p=0.841, 1-year OS rate of 64% for partial responders and 64% for non responders).ConclusionVolumetric response on first follow-up CT may better predict OS than RECIST response.Clinical relevance statementAssessment of tumor size and response is of utmost importance in clinical trials. Volumetric measurements may help to better predict OS than uni-dimensional RECIST criteria.

Project description:UnlabelledLarge-molecule tracers, such as labeled antibodies, have shown success in immuno-PET for imaging of specific cell surface biomarkers. However, previous work has shown that localization of such tracers shows high levels of heterogeneity in target tissues, due to both the slow diffusion and the high affinity of these compounds. In this work, we investigate the effects of subvoxel spatial heterogeneity on measured time-activity curves in PET imaging and the effects of ignoring diffusion limitation on parameter estimates from kinetic modeling.MethodsPartial differential equations (PDE) were built to model a radially symmetric reaction-diffusion equation describing the activity of immuno-PET tracers. The effects of slower diffusion on measured time-activity curves and parameter estimates were measured in silico, and a modified Levenberg-Marquardt algorithm with Bayesian priors was developed to accurately estimate parameters from diffusion-limited data. This algorithm was applied to immuno-PET data of mice implanted with prostate stem cell antigen-overexpressing tumors and injected with (124)I-labeled A11 anti-prostate stem cell antigen minibody.ResultsSlow diffusion of tracers in linear binding models resulted in heterogeneous localization in silico but no measurable differences in time-activity curves. For more realistic saturable binding models, measured time-activity curves were strongly dependent on diffusion rates of the tracers. Fitting diffusion-limited data with regular compartmental models led to parameter estimate bias in an excess of 1,000% of true values, while the new model and fitting protocol could accurately measure kinetics in silico. In vivo imaging data were also fit well by the new PDE model, with estimates of the dissociation constant (Kd) and receptor density close to in vitro measurements and with order of magnitude differences from a regular compartmental model ignoring tracer diffusion limitation.ConclusionHeterogeneous localization of large, high-affinity compounds can lead to large differences in measured time-activity curves in immuno-PET imaging, and ignoring diffusion limitations can lead to large errors in kinetic parameter estimates. Modeling of these systems with PDE models with Bayesian priors is necessary for quantitative in vivo measurements of kinetics of slow-diffusion tracers.

Project description:Simple Summary This study compared the measured diameters of Response Evaluation Criteria in Solid Tumors (RECIST)-defined chest target lesions and lymph nodes between deep learning image reconstruction (DLIR)-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT and found that the measured diameters in ULDCT were highly correlated with that of contrast-enhanced CT and highly repeatable. It is hopeful to evaluate pulmonary lesions, nodules, and lymph nodes of different sizes by using ULDCT in the future, as it is beneficial to repeated scanning in tumor response evaluation and lung cancer screening. ULDCT is expected to further reduce the radiation dose of chest imaging. Abstract Background: Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT. Methods: The consecutive adult patients with noncontrast chest ULDCT (0.07–0.14 mSv) and contrast-enhanced CT (2.38 mSv) were prospectively enrolled. Patients with poor image quality and body mass index ≥ 30 kg/m2 were excluded. The diameter of pulmonary target lesions and lymph nodes defined by Response Evaluation Criteria in Solid Tumors (RECIST) was measured. The measurement variability between ULDCT and enhanced CT was evaluated by Bland-Altman analysis. Results: The 141 enrolled patients (62 ± 12 years) had 89 RECIST-defined measurable pulmonary target lesions (including 30 malignant lesions, mainly adenocarcinomas) and 45 measurable mediastinal lymph nodes (12 malignant). The measurement variation of pulmonary lesions between high-strength DLIR (DLIR-H) images of ULDCT and contrast-enhanced CT was 2.2% (95% CI: 1.7% to 2.6%) and the variation of lymph nodes was 1.4% (1.0% to 1.9%). Conclusions: The measured diameters of pulmonary lesions and lymph nodes in DLIR-H images of ULDCT are highly close to those of contrast-enhanced CT. DLIR-based ULDCT may facilitate evaluating target lesions with greatly reduced radiation exposure in tumor evaluation and lung cancer screening.

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Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling.

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Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling.

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