Figure 3. Predictive Assessment of Respondents From a Simulated Model Comprising 1000 Patients With Locally Advanced Gastric Cancer.

A and B, The closer the curves, the higher the probability that the radiomics signature would identify positive responders from a total estimated number of responders. The threshold value represents the value after which the rate of misdiagnosis would be lowest, thereby providing an optimal treatment to benefit ratio for the patient. The blue line represents the total number of patients who would be considered as having a true-positive response (tumor regression grade 1a-1b) for each threshold. C and D, Probability assessment using the radiomics signature to differentiate between true-positive (tumor regression grade 1a-1b) and false-positive (tumor regression grade 2-3) responders. The farther apart the curves are, the greater the probability that the model would differentiate between the 2 types of responders.