Comparison of signal-to-cutoff values in first, second, and third generation enzyme immunoassays for the diagnosis of HTLV-1/2 infection in ""at-risk"" individuals from Sao Paulo, Brazil

Data obtained during routine diagnosis of human T-cell lymphotropic virus type 1 (HTLV-1) and 2 (HTLV-2) in ""at-risk"" individuals from Sao Paulo, Brazil using signal-to-cutoff (S/C) values obtained by first, second, and third generation enzyme immunoassay (EIA) kits, were compared. The highest S/C values were obtained with third generation EIA kits, but no correlation was detected between these values and specific antibody reactivity to HTLV-1, HTLV-2, or untyped HTLV (p = 0.302). In addition, use of these third generation kits resulted in HTLV-1/2 false-positive samples. In contrast, first and second generation EIA kits showed high specificity, and the second generation EIA kits showed the highest efficiency, despite lower S/C values. Using first and second generation EIA kits, significant differences in specific antibody detection of HTLV-1, relative to HTLV-2 (p = 0.019 for first generation and p < 0.001 for second generation EIA kits) and relative to untyped HTLV (p = 0.025 for first generation EIA kits), were observed. These results were explained by the composition and format of the assays. In addition, using receiver operating characteristics (ROC) analysis, a slight adjustment in cutoff values for third generation EIA kits improved their specificities and should be used when HTLV ""at-risk"" populations from this geographic area are to be evaluated. (C) 2009 Elsevier B.V. All rights reserved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Noninvasive Evaluation of Oral Lesions Using Depth-sensitive Optical Spectroscopy

BACKGROUND: Optical spectroscopy is a noninvasive technique with potential applications for diagnosis of oral dysplasia and early cancer. In this study, we evaluated the diagnostic performance of a depth-sensitive optical spectroscopy (DSOS) system for distinguishing dysplasia and carcinoma from non-neoplastic oral mucosa. METHODS: Patients with oral lesions and volunteers without any oral abnormalities were recruited to participate. Autofluorescence and diffuse reflectance spectra of selected oral sites were measured using the DSOS system. A total of 424 oral sites in 124 subjects were measured and analyzed, including 154 sites in 60 patients with oral lesions and 270 sites in 64 normal volunteers. Measured optical spectra were used to develop computer-based algorithms to identify the presence of dysplasia or cancer. Sensitivity and specificity were calculated using a gold standard of histopathology for patient sites and clinical impression for normal volunteer sites. RESULTS: Differences in oral spectra were observed in: (1) neoplastic versus nonneoplastic sites, (2) keratinized versus nonkeratinized tissue, and (3) shallow versus deep depths within oral tissue. Algorithms based on spectra from 310 nonkeratinized anatomic sites (buccal, tongue, floor of mouth, and lip) yielded an area under the receiver operating characteristic curve of 0.96 in the training set and 0.93 in the validation set. CONCLUSIONS: The ability to selectively target epithelial and shallow stromal depth regions appeared to be diagnostically useful. For nonkeratinized oral sites, the sensitivity and specificity of this objective diagnostic technique were comparable to that of clinical diagnosis by expert observers. Thus, DSOS has potential to augment oral cancer screening efforts in community settings. Cancer 2009;115:1669-79. (C) 2009 American Cancer Society.