Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer

From directive to practice: are pictorial warnings and plain packaging effective to reduce the tobacco addiction?

Tobacco packaging represents an important form of promotion of tobacco products and for this reason plain packaging (PP) can be considered an additional tobacco control measure. In Italy the current tobacco packaging is branded with textual warnings. The study investigated the perception of PP with textual warnings (PPTWs) and pictorial warnings (PPPWs) in Italy

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer

Balancing donor and recipient risk factors in liver transplantation: The value of D-MELD with particular reference to HCV recipients

Donor-recipient match is a matter of debate in liver transplantation. D-MELD (donor age × recipient biochemical model for end-stage liver disease [MELD]) and other factors were analyzed on a national Italian database recording 5946 liver transplants. Primary endpoint was to determine factors predictive of 3-year patient survival. D-MELD cutoff predictive of 5-year patient survival <50% (5yrsPS<50%) was investigated. A prognosis calculator was implemented (http://www.D-MELD.com). Differences among D-MELD deciles allowed their regrouping into three D-MELD classes (A < 338, B 338-1628, C >1628). At 3 years, the odds ratio (OR) for death was 2.03 (95% confidence interval [CI], 1.44-2.85) in D-MELD class C versus B. The OR was 0.40 (95% CI, 0.24-0.66) in class A versus class B. Other predictors were hepatitis C virus (HCV; OR = 1.42; 95% CI, 1.11-1.81), hepatitis B virus (HBV; OR = 0.69; 95% CI, 0.51-0.93), retransplant (OR = 1.82; 95% CI, 1.16-2.87) and low-volume center (OR = 1.48; 95% CI, 1.11-1.99). Cox regressions up to 90 months confirmed results. The hazard ratio was 1.97 (95% CI, 1.59-2.43) for D-MELD class C versus class B and 0.42 (95% CI, 0.29-0.60) for D-MELD class A versus class B. Recipient age, HCV, HBV and retransplant were also significant. The 5yrsPS<50% cutoff was identified only in HCV patients (D-MELD ≥ 1750). The innovative approach offered by D-MELD and covariates is helpful in predicting outcome after liver transplantation, especially in HCV recipients