Nutritional and immune impairments and their effects on outcomes in early pancreatic cancer patients undergoing pancreatoduodenectomy.

BACKGROUND & AIMS: Nutritional impairments are highly frequent in pancreatic cancer even in the early stages and have a significant impact on outcomes. The aim of this prospective study was to investigate immune and nutritional impairments, their interrelations and impacts on outcomes in an unselected cohort of patients scheduled for pancreatoduodenectomy due to suspicion of pancreatic cancer. METHODS: All consecutive patients scheduled for pancreatoduodenectomy at Vilnius University Hospital Santaros Klinikos between January 2016 and November 2018 were recruited into the study according to the inclusion/exclusion criteria. Patients were randomly allocated into the groups of nutritional intervention with immunonutrition vs. control and stratified into the groups of pancreatic ductal adenocarcinoma (PDAC) vs. other pancreatic tumors. Nutritional evaluation included screening (NRS 2002), anthropometric measurements, bioelectrical impedance analysis and lumbar skeletal muscle index (LSMI). Inflammatory indicators were measured before and after surgery. Surgical outcomes were assessed 30 days postoperatively using Comprehensive Complication Index (CCI). RESULTS: Although increased nutritional risk was identified in 22.4% of patients, 41.4% were finally diagnosed with cachexia. While cachexia was predominantly diagnosed in underweight patients, sarcopenia was detected across all BMI categories and 11.7% of obese patients had sarcopenia. Decreased LSMI was identified in 52.5% of patients as compared to decreased phase angle in 39% of patients and decreased fat free mass index in only 3.4% of patients. Regression model indicate a large effect of nutritional indicators on CCI (R2 coefficient 71.1%). In comparison to patients with other pancreatic tumors, patients with PDAC had a characteristic pattern of increased systemic inflammation prior to surgery and decreased inflammation postoperatively (p = 0.02). CONCLUSIONS: A high rate of nutritional impairments was identified in our cohort of patients with early pancreatic cancer, including abnormal body composition phenotypes. They produced negative effects on postoperative outcomes. The highest diagnostic rates were obtained with LSMI measurement, while the highest value for prognostication was attained with the inclusion of multiple objective nutritional state indicators.

Prostate Health Index and Prostate Health Index Density as Diagnostic Tools for Improved Prostate Cancer Detection.

BACKGROUND: To evaluate the diagnostic potential of [-2] proPSA (p2PSA), %p2PSA, Prostate Health Index (phi), and phi density (PHID) as independent biomarkers and in composition of multivariable models in predicting high-grade prostatic intraepithelial neoplasia (HGPIN) and overall and clinically significant prostate cancer (PCa). METHODS: 210 males scheduled for prostate biopsy with total PSA (tPSA) range 2-10 ng/mL and normal digital rectal examination were enrolled in the prospective study. Blood samples to measure tPSA, free PSA (fPSA), and p2PSA were collected immediately before 12-core prostate biopsy. Clinically significant PCa definition was based on Epstein's criteria or ISUP grade ≥ 2 at biopsy. RESULTS: PCa has been diagnosed in 112 (53.3%) patients. Epstein significant and ISUP grade ≥ 2 PCa have been identified in 81 (72.3%) and 40 (35.7%) patients, respectively. Isolated HGPIN at biopsy have been identified in 24 (11.4%) patients. Higher p2PSA and its derivative mean values were associated with PCa. At 90% sensitivity, PHID with cut-off value of 0.54 have demonstrated the highest sensitivity of 35.7% for overall PCa detection, so PHID and phi with cut-off values of 33.2 and 0.63 have demonstrated the specificity of 34.7% and 34.1% for ISUP grade ≥ 2 PCa detection at biopsy, respectively. In univariate ROC analysis, PHID with AUC of 0.77 and 0.80 was the most accurate predictor of overall and Epstein significant PCa, respectively, so phi with AUC of 0.77 was the most accurate predictor of ISUP grade ≥ 2 PCa at biopsy. In multivariate logistic regression analysis, phi improved diagnostic accuracy of multivariable models by 5% in predicting ISUP grade ≥ 2 PCa. CONCLUSIONS: PHID and phi have shown the greatest specificity at 90% sensitivity in predicting overall and clinically significant PCa and would lead to significantly avoid unnecessary biopsies. PHID is the most accurate predictor of overall and Epstein significant PCa, so phi is the most accurate predictor of ISUP grade ≥ 2 PCa. phi significantly improves the diagnostic accuracy of multivariable models in predicting ISUP grade ≥ 2 PCa.