In CT radiomics, numerical parameters extracted from CT images are analyzed to find biomarkers. Since these numerical parameters can vary with imaging parameters, there is a need to optimize acquisition protocols for radiomics. In this work, we investigate the effect of deep-learning-based image reconstruction on the accuracy of radiomic parameters of tumors. We image a 3D printed lung phantom containing four tumors (ellipsoidal, lobulated, spherical, and spiculated), using the CAD model as ground truth. The phantom was 3D printed using fused deposition modeling with a PLA filament and 80% fill rate with a gyroidal pattern to mimic soft tissue. CT images of the 3D printed phantom and tumors were acquired with a GE revolution scanner with 120 kVp and 213 mAs. We reconstructed images using FBP and a vendor-supplied deep learning image reconstruction (DLIR) method (TrueFidelity, GE HealthCare). We also applied 24 custom convolutional neural network denoisers with a U-net architecture, trained on the AAPM-Mayo Clinic Low Dose CT dataset. After segmentation, 14 radiomic features were extracted using SlicerRadiomics. Results show that the vendor-supplied DLIR gave a smaller relative error than FBP for 87% of radiomic features. 8 out of 24 custom denoisers yielded a smaller error than FBP in 50% or more of the radiomic measurements. One denoiser, (VGG16+L1 loss, 32 features, batch size 16), outperformed FBP in 84% of measurements and outperformed the vendor-supplied DLIR in 63% of the measurements. In conclusion, our results demonstrate that deep-learning-based denoising has the potential to improve the accuracy of CT radiomics.