Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Analysis of the genome and transcriptome of Cryptococcus neoformans var. grubii reveals complex RNA expression and microevolution leading to virulence attenuation.

Cryptococcus neoformans is a pathogenic basidiomycetous yeast responsible for more than 600,000 deaths each year. It occurs as two serotypes (A and D) representing two varieties (i.e. grubii and neoformans, respectively). Here, we sequenced the genome and performed an RNA-Seq-based analysis of the C. neoformans var. grubii transcriptome structure. We determined the chromosomal locations, analyzed the sequence/structural features of the centromeres, and identified origins of replication. The genome was annotated based on automated and manual curation. More than 40,000 introns populating more than 99% of the expressed genes were identified. Although most of these introns are located in the coding DNA sequences (CDS), over 2,000 introns in the untranslated regions (UTRs) were also identified. Poly(A)-containing reads were employed to locate the polyadenylation sites of more than 80% of the genes. Examination of the sequences around these sites revealed a new poly(A)-site-associated motif (AUGHAH). In addition, 1,197 miscRNAs were identified. These miscRNAs can be spliced and/or polyadenylated, but do not appear to have obvious coding capacities. Finally, this genome sequence enabled a comparative analysis of strain H99 variants obtained after laboratory passage. The spectrum of mutations identified provides insights into the genetics underlying the micro-evolution of a laboratory strain, and identifies mutations involved in stress responses, mating efficiency, and virulence.

Prevalence and incidence of liver enzyme elevations in a pooled oncology clinical trial cohort.

Few epidemiologic studies describe longitudinal liver chemistry (LC) elevations in cancer patients. A population-based retrospective cohort was identified from 31 Phase 2-3 oncology trials (excluding targeted therapies) conducted from 1985 to 2005 to evaluate background rates of LC elevations in patients (n = 3998) with or without liver metastases. Patients with baseline liver metastases (29% of patients) presented with a 3% prevalence of alanine transaminase (ALT) ≥ 3x upper limits normal (ULN) and 0.2% prevalence of bilirubin ≥ 3xULN. During follow-up, the incidence (per 1000 person-months) of new onset ALT elevations ≥3xULN was 6.1 (95% CI: 4.5, 8.0) and 2.2 (95% CI: 0.9, 4.5) in patients without and with liver metastases, respectively. No new incident cases of ALT and bilirubin elevations suggestive of severe liver injury occurred among those with liver metastases; a single case occurred among those without metastasis. Regardless of the presence of liver metastases, LC elevations were rare in cancer patients during oncology trials, which may be due to enrollment criteria. Our study validates uniform thresholds for detection of LC elevations in oncology studies and serves as an empirical referent point for comparing liver enzyme abnormalities in oncology trials of novel targeted therapies. These data support uniform LC stopping criteria in oncology trials.