  Many studies are devoted to the design of radiomic models for a prediction
task. When no effective model is found, it is often difficult to know whether
the radiomic features do not include information relevant to the task or
because of insufficient data. We propose a downsampling method to answer that
question when considering a classification task into two groups. Using two
large patient cohorts, several experimental configurations involving different
numbers of patients were created. Univariate or multivariate radiomic models
were designed from each configuration. Their performance as reflected by the
Youden index (YI) and Area Under the receiver operating characteristic Curve
(AUC) was compared to the stable performance obtained with the highest number
of patients. A downsampling method is described to predict the YI and AUC
achievable with a large number of patients. Using the multivariate models
involving machine learning, YI and AUC increased with the number of patients
while they decreased for univariate models. The downsampling method better
estimated YI and AUC obtained with the largest number of patients than the YI
and AUC obtained using the number of available patients and identifies the lack
of information relevant to the classification task when no such information
exists.
