Hamid Abdollahi Nasehabad

To compare the reproducibility of radiomics features between deep learning (DL) and adaptive statistical iterative reconstruction (ASIR) CT images under systematic acquisition parameter variations using the GE Revolution Apex scanner.

To evaluate the reproducibility and electron density sensitivity of CT radiomics features across spectral image types and deep learning reconstruction levels using a tissue-equivalent phantom.

To determine whether radiomic features extracted from normal endocrine organs, when combined with clinical variables, can capture systemic imaging signatures predictive of clinical progression in patients with PSMA-negative prostate cancer.