Variation in genes encoding for interferon λ-3 and λ-4 in the prediction of HCV-1 treatment-induced viral clearance.

BACKGROUND & AIMS: In patients with chronic HCV-1 infection, recent evidences indicate that determination of a dinucleotide polymorphism (ss469415590, ΔG/TT) of a new gene, designated IFN λ-4, might be more accurate than the 12979860CC type of the IL28B locus in predicting sustained virological response (SVR) following peg-interferon and ribavirin. In addition, combined genotyping of different SNPs of the IL28B locus was shown to help dissect patients most prone to SVR among those with rs12979860CT. We examined whether single or combined genotyping of two IL28B SNPs, rs12979860 and rs8099917, and ss469415590 variation might improve the prediction of SVR. RESULTS: In the study cohort of 539 patients, 38% had SVR. The SNPs 12979860CC, rs8099917TT, and rs469415590TT/TT correlated significantly with SVR (68%, 50%, and 67%). Carriers of either the triplotype rs12979860CC_ss469415590TT/TT_rs8099917TT or the diplotype rs12979860CC_ss469415590TT/TT had the highest SVR rate (72%). In carriers of the rs12979860 T allele, neither the rs8099917 nor the ss469415590 improved the response prediction. After pooling this finding with data from previous studies, in rs12979860 T heterozygous individuals the co-presence of the rs8099917TT SNP was associated with improved response prediction. CONCLUSION: In HCV-1 patients, the rs12979860 polymorphism appeared as the hit SNP better predicting response following peg-interferon and ribavirin treatment. Additional ss469415590 or rs8099917 genotyping had no added benefit for response prediction. In the subset of carriers of the rs12979860 T allele, genotyping of the rs8099917 SNP was unhelpful in the present investigation, but may inform clinical prediction of treatment response when our data were pooled with previous investigations.

Neural network analysis for evaluating cancer risk in thyroid nodules with an indeterminate diagnosis at aspiration cytology: identification of a low-risk subgroup.

Thyroid nodules with a predominant follicular structure are often diagnosed as indeterminate at fine-needle aspiration biopsy (FNAB). We studied 453 patients with a thyroid nodule diagnosed as indeterminate at FNAB by using a feed-forward artificial neural network (ANN) analysis to integrate cytologic and clinical data, with the goal of subgrouping patients into a high-risk and in a low-risk category. Three hundred seventy-one patients were used to train the network and 82 patients were used to validate the model. The cytologic smears were blindly reviewed and classified in a high-risk and a low-risk subgroup on the basis of standard criteria. Neural network analysis subdivided the 371 lesions of the first series into a high-risk group (cancer rate of approximately 33% at histology) and a low-risk group (cancer rate of 3%). Only cytologic parameters contributed to this classification. Analysis of the receiver operating characteristic (ROC) curves demonstrated that the ANN model discriminated with higher sensitivity and specificity between benign and malignant nodules compared to standard cytologic criteria (p < 0.001). This value did not show degradation when ANN predictions were applied to the validation series of 82 nodules. In conclusion, neural network analysis of cytologic data may be a useful tool to refine the risk of cancer in patients with lesions diagnosed as indeterminate by FNAB.