Adjustment for the Age-and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism

Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration,and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the[^(18)F]fluorodeoxyglucose positron emission tomography([^(18)F]FDG PET).We aimed to explore the potential of the adjustment of age-and gender-related confounding factors on[^(18)F]FDG PET images in differentiation of Parkinson’s disease(PD),multiple system atrophy(MSA)and progressive supra-nuclear palsy(PSP).Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included.An age-and gender-adjusted Z(AGAZ)score was established based on the gender-specific longitudinal metabolic changes on healthy subjects.AGAZ scores and standardized uptake value ratio(SUVR)values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification.Additional references of the classifications based on metabolic pattern scores were included.The feature-based AGAZ score showed the best performance in classification(accuracy for PD,MSA,PSP:93.1%,96.3%,94.8%).In both genders,the AGAZ score con-sistently achieved the best efficiency,and the improvements compared to the conventional SUVR value for PD,MSA,and PSP mainly laid in specificity(Male:5.7%;Female:11.1%),sensitivity(Male:7.2%;Female:7.3%),and sensitivity(Male:7.3%;Female:17.2%).Female patients benefited more from the adjustment on[^(18)F]FDG PET in MSA and PSP groups(absolute net reclassification index,p<0.001).Collectively,the adjustment of age-and gender-related confounding factors may improve the differential diagnosis of Parkinsonism.Particularly,the diagnosis of female Parkinsonian population has the best improvement from this correction.

放射性药物治疗芯片:微流控驱动的数字孪生在个体化癌症治疗中的展望

Radiopharmaceutical therapy(RPT)has shown great promise in the treatment of various cancers.However,the current approach is a"one size fits all"model,which results in varied patient responses,and there is a need for personalized RPT(Fig.S1 online).This personalized approach requires a better understanding of the physics,biology,radiobiology,pharmacology,immunology,chemistry,and imaging involved in RPT[1].Multidisciplinary research using experimental,numerical,and simulation platforms would be key to accelerating progress in this area.