Evaluating Continuous Tumor Measurement-Based Metrics as Phase II Endpoints for Predicting Overall Survival

Abstract

Absolute and relative change in tumor measurements do not demonstrate convincingly improved overall survival predictive ability over the RECIST model. Continued work is necessary to address issues of missing tumor measurements and model selection in identifying improved tumor measurement-based metrics.

Evaluating tumor response with FDG PET: updates on PERCIST, comparison with EORTC criteria and clues to future developments

“…Attempts have been made to improve prediction of patient survival by using continuous metrics for tumor response, but no significant differences as compared to RECIST have been observed so far [7, 8]. …”

On the relationship between tumour growth rate and survival in non-small cell lung cancer

“…The option of using continuous measures involving changes in SLD over the categorisation scheme has been a long-standing debate when analysing early patient response to treatment (3)(4)(5). These works have shown that there was no benefit in terms of survival concordance probability when using continuous changes in SLD compared to the current RECIST response classification.…”

“…There are two possible reasons why the results from Sharma et al and Kaiser differ: i) the difference in PFS between treatments, in the studies used by Kaiser, were greater than that in the study It is important to note that in our context, risk can be allocated within two different categories, individual and group risk. An individual risk metric is assessed through survival concordance probabilities (3)(4)(5)20), whereas a group risk metric is assessed on whether the correct decision was made to advance a compound from phase II to phase III. In this context, individual risk estimates the patient's survival prognosis based on the patient's historical imaging time series data.…”

“…For individual risk the model derived metrics, GR and TTG, calculated using the exponential decay/growth model used in previous studies (7-10,12,13, 18,19), were assessed via calculation of the survival concordance probability. Concordance probability was chosen 6 for two reasons: 1) it is a measure that is routinely used when reporting the predictive capability of a prognostic model developed for use in the clinic (3)(4)(5)20); 2) it is a nonparametric approach which we preferred over a regression model to avoid the use of a specific functional form when measuring evidence of relationship between variables. We examined the correlation to overall survival times for these quantities using two different data alignment approaches, forward and reverse.…”

Time to progression ratio: promising new metric or just another metric?

“…From a clinical trial standpoint, it would be immensely helpful if we can develop methodology for tumor-based measurement metrics that can address these issues. The TTP ratio metric faces other challenges, which is also shared by other metrics based on tumor measurements (3)(4)(5). First, it's not clear from the manuscript how the TTP ratio can be used as an early endpoint since it is based on time to progression, which can be quite long in many disease settings with effective therapies and therefore may still delay the process of detecting promising treatments early.…”