Browsing by Author "Yamal, Jose-Miguel"

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    Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia without colposcopic tissue information; a step toward automation for low resource settings
    (Society of Photo-Optical Instrumentation Engineers, 2012-04) Yamal, Jose-Miguel; Zewdie, Getie A.; Cox, Dennis D.; Atkinson, E. Neely; Cantor, Scott B.; MacAulay, Calum; Davies, Kalatu; Adewole, Isaac; Buys, Timon P. H.; Follen, Michele
    Optical spectroscopy has been proposed as an accurate and low-cost alternative for detection of cervical intraepithelial neoplasia. We previously published an algorithm using optical spectroscopy as an adjunct to colposcopy and found good accuracy (sensitivity ¼ 1.00 [95% confidence interval ðCIÞ ¼ 0.92 to 1.00], specificity ¼ 0.71 [95% CI ¼ 0.62 to 0.79]). Those results used measurements taken by expert colposcopists as well as the colposcopy diagnosis. In this study, we trained and tested an algorithm for the detection of cervical intraepithelial neoplasia (i.e., identifying those patients who had histology reading CIN 2 or worse) that did not include the colposcopic diagnosis. Furthermore, we explored the interaction between spectroscopy and colposcopy, examining the importance of probe placement expertise. The colposcopic diagnosis-independent spectroscopy algorithm had a sensitivity of 0.98 (95% CI ¼ 0.89 to 1.00) and a specificity of 0.62 (95% CI ¼ 0.52 to 0.71). The difference in the partial area under the ROC curves between spectroscopy with and without the colposcopic diagnosis was statistically significant at the patient level (p ¼ 0.05) but not the site level (p ¼ 0.13). The results suggest that the device has high accuracy over a wide range of provider accuracy and hence could plausibly be implemented by providers with limited training.
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    Multilevel classification: Classification of populations from measurements on members
    (2007) Yamal, Jose-Miguel; Cox, Dennis D.
    Multilevel classification is a problem in statistics which has gained increasing importance in many real-world problems, but it has not yet received the same statistical understanding as the general problem of classification. An example we consider here is to develop a method to detect cervical neoplasia (pre-cancer) using quantitative cytology, which involves measurements on the cells obtained in a Papanicolou smear. The multilevel structure comes from the embedded cells within a patient, where we have quantitative measurements on the cells, yet we want to classify the patients, not the cells. An additional challenge comes from the fact that we have a high-dimensional feature vector of measurements on each cell. The problem has historically been approached in two ways: (a) ignore this multilevel structure of the data and perform classification at the microscopic (cellular) level, and then use ad-hoc methods to classify at the macroscopic (patient) level, or (b) summarize the microscopic level data using a few statistics and then use these to compare the subjects at the macroscopic level. We consider a more rigorous statistical approach, the Cumulative Log-Odds (CLO) Method, which models the posterior log-odds of disease for a patient given the cell-level measured feature vectors for that patient. Combining the CLO method with a latent variable model (Latent-Class CLO Method) helps to account for between-patient heterogeneity. We apply many different approaches and evaluate their performance using out of sample prediction. We find that our best methods classify with substantial greater accuracy than the subjective Papanicolou Smear interpretation by a clinical pathologist.
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    Prediction using hierarchical data: Applications for automated detection of cervical cancer
    (Wiley, 2015) Yamal, Jose-Miguel; Guillaud, Martial; Atkinson, E. Neely; Follen, Michele; MacAulay, Calum; Cantor, Scott B.; Cox, Dennis D.
    Although the Papanicolaou smear has been successful in decreasing cervical cancer incidence in the developed world, there exist many challenges for implementation in the developing world. Quantitative cytology, a semi-automated method that quantifies cellular image features, is a promising screening test candidate. The nested structure of its data (measurements of multiple cells within a patient) provides challenges to the usual classification problem. Here we perform a comparative study of three main approaches for problems with this general data structure: (i) extract patient-level features from the cell-level data, (ii) use a statistical model that accounts for the hierarchical data structure, and (iii) classify at the cellular level and use an ad hoc approach to classify at the patient level. We apply these methods to a dataset of 1728 patients, with an average of 2600 cells collected per patient and 133 features measured per cell, predicting whether a patient had a positive biopsy result. The best approach we found was to classify at the cellular level and count the number of cells that had a posterior probability greater than a threshold value, with estimated 61% sensitivity and 89% specificity on independent data. Recent statistical learning developments allowed us to achieve high accuracy.