Abstract 355P
Background
Our study aimed to explore the improvement of image quality with spectral computed tomography (CT) of prostate cancer by combining the deep learning image reconstruction (DLIR) algorithm with small field of view (FOV) and the diagnostic value of lymph node metastasis. Our study aimed to explore the improvement of image quality with spectral computed tomography (CT) of prostate cancer by combining the deep learning image reconstruction (DLIR) algorithm with small field of view (FOV) and the diagnostic value of lymph node metastasis.
Methods
22 patients who underwent pelvic spectral CT scans for prostate cancer at Sun Yat-sen University Cancer Centre between March and December 2022 were enrolled. Axial images were reconstructed using DLIR-H, DLIR-M, DLIR-L, and the adaptive statistical iterative reconstruction algorithm with 50% weight (ASIR-V 50%). To evaluate the image quality, the signal-to-noise ratios (SNR) of spectral parameter images were measured. Lymph nodes were classified into benign and malignant groups based on surgical pathology results.
Results
With increasing in reconstruction strengths, both image SNR and subjective scores increased progressively, with DLIR-H having the highest subjective scores and SNR. Regarding the diagnostic performance, significant differences were showed between benign and malignant lymph node in the measured values of mono-energy images at 40-80 keV, iodine (water) and iodine (fat) based material decomposition images, and effective atomic number images, regardless of the reconstruction algorithm. Notably, compared to ASIR-V 50% and DLIR-L, the measurements of water (iodine) concentration and fat (iodine) concentration in DLIR-M and DLIR-H presented significant differences between benign and malignant groups. The maximum AUCs for distinguishing benign from malignant prostate cancer lymph nodes for the four reconstruction algorithms were 0.916, 0.907, 0.909, and 0.913, respectively (ASIR 50%, DLIR-L/M/H).
Conclusions
Compared to ASIR-V 50%, DLIR-H significantly enhanced image quality and provided more spectral parameters that can be considered as risk factors between benign and malignant lymph nodes of prostate cancer.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.