title: Prediction using hierarchical data: Applications for automated detection of cervical cancer.
abstract: 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: a) extract patient-level features from the cell-level data; b) use a statistical model that accounts for the hierarchical data structure; and c) 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 1,728 patients, with an average of 2,600 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.
authors.affiliation: Department of Biostatistics, The University of Texas School of Public Health, 1200 Herman Pressler, Suite W-928, Houston, TX 77030, USA.
authors.forename: Jose-Miguel
authors.initials: JM
authors.lastname: Yamal
authors.affiliation: Department of Integrative Oncology, British Columbia Cancer Research Centre, 675 West 10th Ave, Vancouver, BC, V5Z 1L3, Canada.
authors.forename: Martial
authors.initials: M
authors.lastname: Guillaud
authors.affiliation: Department of Statistics, Rice University, 6100 Main St., Houston, TX 77005, USA.
authors.forename: E Neely
authors.initials: EN
authors.lastname: Atkinson
authors.affiliation: Department of Obstetrics and Gynecology, Brookdale Hospital and Medical Center, 555 Rockaway Pkwy, Brooklyn, NY 11212, USA.
authors.forename: Michele
authors.initials: M
authors.lastname: Follen
authors.affiliation: Department of Integrative Oncology, British Columbia Cancer Research Centre, 675 West 10th Ave, Vancouver, BC, V5Z 1L3, Canada.
authors.forename: Calum
authors.initials: C
authors.lastname: MacAulay
authors.affiliation: Department of Health Services Research, The University of Texas MD Anderson Cancer Center, P.O. Box 301402, Unit 1444, Houston, TX 77230-1402, USA.
authors.forename: Scott B
authors.initials: SB
authors.lastname: Cantor
authors.affiliation: Department of Statistics, Rice University, 6100 Main St., Houston, TX 77005, USA.
authors.forename: Dennis D
authors.initials: DD
authors.lastname: Cox
keywords: DNA ploidy; L1-regularized logistic regression; cross-validation; multilevel classification; quantitative cytology; variable selection