Towards improved diagnosis: radiomics and quantitative biomarkers in 18 F-PSMA-1007 and 18 F-fluorocholine PET/CT for prostate cancer recurrence.

OBJECTIVE: This study compared the radiomic features and quantitative biomarkers of 18 F-PSMA-1007 [prostate-specific membrane antigen (PSMA)] and 18 F-fluorocholine (FCH) PET/computed tomography (CT) in prostate cancer patients with biochemical recurrence (BCR) enrolled in the phase 3, prospective, multicenter BIO-CT-001 trial. METHODS: A total of 106 patients with BCR, who had undergone primary definitive treatment for prostate cancer, were recruited to this prospective study. All patients underwent one PSMA and one FCH PET/CT examination in randomized order within 10 days. They were followed up for a minimum of 6 months. Pathology, prostate-specific antigen (PSA), PSA doubling time, PSA velocity, and previous or ongoing treatment were analyzed. Using LifeX software, standardized uptake value (SUV) maximum, SUV mean , PSMA and choline total volume (PSMA-TV/FCH-TV), and total lesion PSMA and choline (TL-PSMA/TL-FCH) of all identified metastatic lesions in both tracers were calculated. RESULTS: Of the 286 lesions identified, the majority 140 (49%) were lymph node metastases, 118 (41.2%) were bone metastases and 28 lesions (9.8%) were locoregional recurrences of prostate cancer. The median SUV max value was significantly higher for 18 F-PSMA compared with FCH for all 286 lesions (8.26 vs. 4.99, respectively, P  < 0.001). There were statistically significant differences in median SUV mean , TL-PSMA/FCH, and PSMA/FCH-TV between the two radiotracers (4.29 vs. 2.92, 1.97 vs. 1.53, and 7.31 vs. 4.37, respectively, P  < 0.001). The correlation between SUV mean /SUV max and PSA level was moderate, both for 18 F-PSMA ( r  = 0.44, P  < 0.001; r  = 0.44, P  < 0.001) and FCH ( r  = 0.35, P  < 0.001; r  = 0.41, P  < 0.001). TL-PSMA/FCH demonstrated statistically significant positive correlations with both PSA level and PSA velocity for both 18 F-PSMA ( r  = 0.56, P  < 0.001; r  = 0.57, P  < 0.001) and FCH ( r  = 0.49, P  < 0.001; r  = 0.51, P  < 0.001). While patients who received hormone therapy showed higher median SUV max values for both radiotracers compared with those who did not, the difference was statistically significant only for 18 F-PSMA ( P  < 0.05). CONCLUSION: Our analysis using both radiomic features and quantitative biomarkers demonstrated the improved performance of 18 F-PSMA-1007 compared with FCH in identifying metastatic lesions in prostate cancer patients with BCR.

Review of artificial intelligence clinical applications in Nuclear Medicine.

This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer's disease and Parkinson's disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.

Artificial intelligence in Nuclear Medicine Physics and Imaging.

No one can deny the significant impact of artificial intelligence (AI) on everyday life, especially in the health sector where it has emerged as a crucial and beneficial tool in Nuclear Medicine (NM) and molecular imaging. The objective of this review is to provide a summary of the various applications of AI in single-photon emission computed tomography (SPECT) and positron emission tomography (PET), with or without anatomical information (CT or magnetic resonance imaging (MRI)). This review analyzes subsets of AI, such as machine learning (ML) and Deep Learning (DL), and elaborates on their applications in NM imaging (NMI) physics, including the generation of attenuation maps, estimation of scattered events, depth of interaction (DOI), time of flight (TOF), NM image reconstruction (optimization of the reconstruction algorithm), and low dose imaging.