Quantitative imaging biomarkers (QIBs) hold enormous potential to improve the efficiency of clinical trials that use standard-of-care CT imaging. Examples of QIBs include size, shape, intensity histogram characteristics, texture, radiomics, and more. There is, however, a well-recognized gap between discovery and the translation to practice of QIBs, which is driven in part by concerns about their repeatability and reproducibility in the diverse clinical environment. Our goal is to characterize QIB repeatability and reproducibility by using virtual imaging clinical trials (VICTs) to simulate the full data pathway. We start by estimating the probability distribution functions (PDFs) for patient-, disease-, treatment- , and imaging-related sources of variability. These are used to forward-model sinograms that are reconstructed and then analyzed by the QIB under evaluation in a virtual imaging pipeline. By repeatedly sampling from the variability PDFs, estimates of the bias, variance, repeatability and reproducibility of the QIB can be generated by comparison with the known ground truth. These estimates of QIB performance can be used as evidence of the utility of QIBs in clinical trials of new therapies.
There is a tremendous potential for AI-based quantitative imaging biomarkers to make clinical trials with standardof- care CT more efficient. There is, however, a well-recognized gap between discovery and the translation to practice for AI-based imaging biomarkers. Our goal is to enable more efficient and effective imaging clinical trials by characterizing the repeatability and reproducibility AI-based imaging biomarkers. We used virtual imaging clinical trials (VCTs) to simulate the data pathway by estimating the probability distributions functions for patient-, disease-, and imaging-related sources of variability. We evaluated the bias and variance in estimating the volume of liver lesions and the variance of an algorithm, that has shown success in predicting mortality risk for NSCLC patients. We used the volumetric XCAT anthropomorphic simulated phantom with inserted lesions with varied shape, size, and location. For CT acquisition and reconstruction we used the CatSim package and varied acquisition mAs and image reconstruction kernel. For each combination of parameters we generated 20 independent realizations with quantum and electronic noise. The resulting images were analyzed with the two AI-based imaging biomarkers described above, and from that we computed the mean and standard deviation of the results. Mean values and/or bias results were counter-intuitive in some cases, e.g. lower mean bias in scans with lower mAs. Addition of variations in lesion size, shape and location increased variance of the estimated parameters more than the mAs effects. These results indicate the feasibility of using VCTs to estimate the repeatability and reproducibility of AI-based biomarkers used in clinical trials with standard-of-care CT.
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