The potential of photon-counting CT for the improved precision of lung nodule radiomics.

Lung nodule appearance may provide prognostic information, as the presence of spiculation increases the suspicion of a nodule being cancerous. Spiculations can be quantified using morphological radiomics features extracted from CT images. Radiomics features can be affected by the acquisition parameters and scanner technologies; thus, it is essential to identify imaging conditions that provide reliable measurements, particularly for emerging technologies like photon-counting CT. This study aimed to systematically quantify the effect of imaging parameters on the radiomics measurements using a virtual imaging trial (VIT) platform, and further verify the findings with human clinical data. The VIT utilized nine virtual patients, each with three 6-mm nodules of varying spiculations. The virtual patients were run through a validated CT simulator (DukeSim) to acquire images at three dose levels (CTDIvol = 2.85, 5.69, and 11.38 mGy) with a clinical energy-integrating CT and a photon-counting CT. The acquired projection images were reconstructed using multiple slice thicknesses, kernels, and matrix sizes. The reconstructed images were processed to extract morphological features using three segmentation methods. The features were clustered into three broad type categories. Features extracted from the acquired CT images were compared to their corresponding ground truth values, across all imaging conditions. Among all imaging conditions, slice thickness had the greatest effect on the radiomics measurements. When the thickest slices were used, the coefficient of variation increased by [1.19-9.66%] in the EICT images, and [3.94-24.43%] in the PCCT images. For both scanners, varying the kernel sharpness and dose affected the radiomics measurements insignificantly, while pixel size and segmentation method were observed to have stronger effects. Under varying imaging conditions, the trends and magnitude of radiomics features measurements were coherent with virtual trial results. The findings stress the importance of choosing optimal reconstruction settings for radiomics extraction to achieve precise feature quantifications.