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Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials.

ABSTRACT: The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival.Individual patient data from three North Central Cancer Treatment Group trials (N0026, n = 117; N9741, n = 1,109; and N9841, n = 332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements were considered in addition to a trichotomized response [TriTR: response (complete or partial) vs. stable disease vs. progression). Cox proportional hazards models, adjusted for treatment arm and baseline tumor burden, were used to assess the impact of the metrics on subsequent overall survival, using a landmark analysis approach at 12, 16, and 24 weeks postbaseline. Model discrimination was evaluated by the concordance (c) index.The overall best response rates for the three trials were 26%, 45%, and 25%, respectively. Although nearly all metrics were statistically significantly associated with overall survival at the different landmark time points, the concordance indices (c-index) for the traditional response metrics ranged from 0.59 to 0.65; for the continuous metrics from 0.60 to 0.66; and for the TriTR metrics from 0.64 to 0.69. The c-indices for TriTR at 12 weeks were comparable with those at 16 and 24 weeks.Continuous tumor measurement-based metrics provided no predictive improvement over traditional response-based metrics or TriTR; TriTR had better predictive ability than best TriTR or confirmed response. If confirmed, TriTR represents a promising endpoint for future phase II trials.

SUBMITTER: An MW

PROVIDER: S-EPMC3195893 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials.

An Ming-Wen MW Mandrekar Sumithra J SJ Branda Megan E ME Hillman Shauna L SL Adjei Alex A AA Pitot Henry C HC Goldberg Richard M RM Sargent Daniel J DJ

Clinical cancer research : an official journal of the American Association for Cancer Research 20110831 20

<h4>Purpose</h4>The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival.<h4>Experimental design</h4>Individual patient data from three North Central Cancer Treatment Group trials (N0026, n = 117; N9741, n = 1,109; and N9841, n = 332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements wer ...[more]

PMID: 21880789

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Project description:IntroductionLongitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST.Material and methodsJoint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices.ResultsData from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p < 0.001 for both association in JM and two-stage modeling parameters); but less so in the lung cancer trial (p = 0.053 for association in JM and p = 0.024 and 0.160 for two-stage modeling parameters). The c-indices from the two-stage modeling were higher than those from a model using RECIST (range: 0.611-0.633 versus 0.586-0.590). The dynamic c-indices from the JM were in the range of 0.627-0.683 indicating good predictive accuracy.ConclusionBoth modeling approaches provide highly interpretable and clinical meaningful results; the improved predictive performance compared with RECIST indicates the possibility of deriving better trial endpoints from these approaches.

Project description:Importance:Tumor measurements can be used to estimate time to nadir and depth of nadir as potential surrogates for overall survival (OS). Objective:To assess time to nadir and depth of nadir as surrogates for OS in metastatic colorectal cancer. Design, Setting, and Participants:Pooled analysis of 20 randomized clinical trials within the Aide et Recherche en Cancerologie Digestive database, which contains academic and industry-sponsored trials, was conducted. Three sets of comparisons were performed: chemotherapy alone, antiangiogenic agents, and anti-epidermal growth factor receptor agents in first-line treatment for patients with metastatic colorectal cancer. Main Outcomes and Measures:Surrogacy of time to nadir and depth of nadir was assessed at the trial level based on joint modeling of relative tumor-size change vs baseline and OS. Treatment effects on time to nadir and on depth of nadir were defined in terms of between-arm differences in time to nadir and in depth of nadir, and both were assessed in linear regressions for their correlation with treatment effects (hazard ratios) on OS within each set. The strengths of association were quantified using sample-size-weighted coefficients of determination (R2), with values closer to 1.00 indicating stronger association. At the patient level, the correlation was assessed between modeled relative tumor-size change and OS. Results:For 14 chemotherapy comparisons in 4289 patients, the R2 value was 0.63 (95% CI, 0.30-0.96) for the association between treatment effects on time to nadir and OS and 0.08 (95% CI, 0-0.37) for depth of nadir and OS. For 11 antiangiogenic agent comparisons (4854 patients), corresponding values of R2 were 0.25 (95% CI, 0-0.72) and 0.06 (95% CI, 0-0.35). For 8 anti-epidermal growth factor receptor comparisons (2684 patients), corresponding values of R2 were 0.24 (95% CI, 0-0.83) and 0.21 (95% CI, 0-0.78). Conclusions and Relevance:In contrast with early reports favoring depth of response as a surrogate, these results suggest that neither time to nadir nor depth of nadir is an acceptable surrogate for OS in the first-line treatment of metastatic colorectal cancer.

Project description:Background Increasing evidence indicates that the pathology and the modified Kadish system have some influence on the prognosis of esthesioneuroblastoma (ENB). However, an accurate system to combine pathology with a modified Kadish system has not been established. Methods This study aimed to set up and evaluate a model to predict overall survival (OS) accurately in ENB, including clinical characteristics, treatment and pathological variables. We screened the information of patients with ENB between January 1, 1976, and December 30, 2016 from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program as a training cohort. The validation cohort consisted of patients with ENB at Sun Yat-sen University Cancer Center and The First Affiliated Hospital of Sun Yat-sen University in the same period, and 87 patients were included. The Pearson’s chi-squared test was used to assess significance of clinicopathological and demographic characteristics. We used the Cox proportional hazards model to examine univariate and multivariate analyses. The model coefficients were used to calculate the Hazard ratios (HR) with 95% confidence intervals (CI). Prognostic factors with a p-value < 0.05 in multivariate analysis were included in the nomogram. The concordance index (c-index) and calibration curve were used to evaluate the predictive power of the nomogram. Results The c-index of training cohort and validation cohort are 0.737 (95% CI, 0.709 to 0.765) and 0.791 (95% CI, 0.767 to 0.815) respectively. The calibration curves revealed a good agreement between the nomogram prediction and actual observation regarding the probability of 3-year and 5-year survival. We used a nomogram to calculate the 3-year and 5-year growth probability and stratified patients into three risk groups. Conclusions The nomogram provided the risk group information and identified mortality risk and can serve as a reference for designing a reasonable follow-up plan.

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Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials.

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Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials.

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