Currently available serum biomarkers are insufficiently reliable to distinguish patients with epithelial ovarian cancer (EOC) from healthy individuals. Metabonomics, the study of metabolic processes in biologic systems, is based on the use of 1 H-NMR spectroscopy and multivariate statistics for biochemical data generation and interpretation and may provide a characteristic fingerprint in disease. In an effort to examine the utility of the metabonomic approach for discriminating sera from women with EOC from healthy controls, we performed 1 H-NMR spectroscopic analysis on preoperative serum specimens obtained from 38 patients with EOC, 12 patients with benign ovarian cysts and 53 healthy women. After data reduction, we applied both unsupervised Principal Component Analysis (PCA) and supervised Soft Independent Modeling of Class Analogy (SIMCA) for pattern recognition. The sensitivity and specificity tradeoffs were summarized for each variable using the area under the receiver-operating characteristic (ROC) curve. In addition, we analyzed the regions of NMR spectra that most strongly influence separation of sera of EOC patients from healthy controls. PCA analysis allowed correct separation of all serum specimens from 38 patients with EOC (100%) from all of the 21 premenopausal normal samples (100%) and from all the sera from patients with benign ovarian disease (100%). In addition, it was possible to correctly separate 37 of 38 (97.4%) cancer specimens from 31 of 32 (97%) postmenopausal control sera. SIMCA analysis using the Cooman's plot demonstrated that sera classes from patients with EOC, benign ovarian cysts and the postmenopausal healthy controls did not share multivariate space, providing validation for the class separation. ROC analysis indicated that the sera from patients with and without disease could be identified with 100% sensitivity and specificity at the 1 H-NMR regions 2.77 parts per million (ppm) and 2.04 ppm from the origin (AUC of ROC curve ‫؍‬ 1.0). In addition, the regression coefficients most influential for the EOC samples compared to postmenopausal controls lie around ␦3.7 ppm (due mainly to sugar hydrogens). Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies. There are more than 23,000 cases annually in the United States, and 14,000 women can be expected to die from the disease in 2003. 1 Despite important advances in surgery and chemotherapy that have been made over the past 20 years, the overall survival for patients with EOC has not changed significantly. The high mortality rate of EOC occurs primarily because most women are diagnosed with advanced disease (stage III/IV), which has a 5-year survival rate of 15-20%. 1 In contrast, the small proportion of patients with accurately diagnosed stage I disease have 5-year survival rates in excess of 90%. 2 Current candidate strategies for the detection of EOC are based on biochemical tumor markers, such as CA125, and biophysical markers assessed by ultrasound and/or Doppler imaging of the ovaries. Unf...

Up to 10% of cystic fibrosis (CF) children develop cirrhosis by the first decade. We evaluated the utility of two simple biomarkers, aspartate aminotransferase to platelet ratio index (APRI) and FIB-4, in predicting degree of fibrosis in pediatric CF liver disease (CFLD) validated by liver biopsy. In this retrospective, cross-sectional study, 67 children with CFLD had dual-pass liver biopsies and 104 age-and sex-matched CF children without liver disease (CFnoLD) had serum to calculate APRI and FIB-4 collected at enrollment. CFLD was defined as having two of the following: (1) hepatomegaly 6 splenomegaly; (2) >6 months elevation of ALT (>1.53 upper limit of normal ULN); or (3) abnormal liver ultrasound findings. Biopsies were staged according to Metavir classification by two blinded pathologists. Receiver operating characteristic (ROC) analysis and continuation ratio logistic regression were performed to assess the predictability of these biomarkers to distinguish CFLD from CFnoLD and determine fibrosis stage-specific cutoff values. The AUC for APRI was better than FIB-4 (0.75 vs. 0.60; P 5 0.005) for predicting CFLD and severe CFLD (F3-F4) (0.81). An APRI score >0.264 demonstrated a sensitivity (95% confidence interval [CI]) of 73.1% (60.9, 83.2) and specificity of 70.2% (60.4, 78.8) in predicting CFLD. A 50% increase in APRI was associated with a 2.4-fold (95% CI: 1.7, 3.3) increased odds of having CFLD. APRI demonstrated full agreement with histology staging 37% of the time, but was within one stage 73% of the time. Only FIB-4 predicted portal hypertension at diagnosis (area under the receiver operator characteristic curve [AUC] 5 0.91; P < 0.001). Conclusion: This is the first liver biopsyvalidated study of APRI and FIB-4 in pediatric CFLD. APRI appears superior to FIB-4 in differentiating CFLD versus CFnoLD. APRI also exhibited a high AUC in predicting severe liver fibrosis with specific cutoffs for lower stages. (HEPATOLOGY 2015;62:1576-1583