A Prospective Randomized Multicenter Study on the Impact of [18F]F-Choline PET/CT Versus Conventional Imaging for Staging Intermediate- to High-Risk Prostate Cancer.

This study aimed to compare the efficacy of [18F]F-choline PET/CT with conventional imaging for staging and managing intermediate- to high-risk prostate cancer (PCa). The primary objective was to assess the ability of PET/CT with [18F]F-choline to identify lymph node and systemic involvement during initial staging. Secondary objectives included evaluating the impact of [18F]F-choline PET/CT on unnecessary local treatments and assessing the safety of [18F]F-choline agents. Additionally, the study aimed to analyze recurrence-free survival and overall survival 5 y after randomization. Methods: A prospective controlled, open, randomized multicenter phase III trial involving 7 Italian centers was conducted. Eligible patients with intermediate- to high-risk PCa were randomized in a 1:1 ratio. Two groups were formed: one undergoing conventional imaging (abdominopelvic contrast-enhanced CT and bone scanning) and the other receiving conventional imaging plus [18F]F-choline PET/CT. The study was terminated prematurely; however, all the endpoints were thoroughly analyzed and enriched. Results: Between February 2016 and December 2020, 256 patients were randomly assigned. In total, 236 patients (117 in the control arm and 119 in the experimental arm) were considered for the final assessment. In the experimental arm, the sensitivity for lymph node metastases, determined by final pathology and serial prostate-specific antigen evaluations, was higher than in the control arm (77.78% vs. 28.57% and 65.62% vs. 17.65%, respectively). The [18F]F-choline was tolerated well. The use of [18F]F-choline PET/CT resulted in an approximately 8% reduction in unnecessary extended lymphadenectomy compared with contrast-enhanced CT. Additionally, [18F]F-choline PET/CT had a marginal impact on 5-y overall survival, contributing to a 4% increase in survival rates. Conclusion: In the initial staging of PCa, [18F]F-choline PET/CT exhibited diagnostic performance superior to that of conventional imaging for detecting metastases. [18F]F-choline PET/CT reduced the rate of unnecessary extensive lymphadenectomy by up to 8%. These findings support the consideration of discontinuing conventional imaging for staging PCa.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.