Classification using the cumulative log-odds in the quantitative pathologic diagnosis of adenocarcinoma of the cervix

Swartz, Richard J.; West, Loyd A.; Boiko, Iouri; Malpica, Anaís; Guillaud, Martial; MacAulay, Calum; Follen, Michele; Atkinson, E. Neely; Cox, Dennis D.

doi:10.1016/j.ygyno.2005.07.038

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…41,42 Wherever possible, semiquantitative measures should be replaced by quantitative analysis using computer-assisted image processing instrumentation. [43][44][45] Automated computerassisted quantitative image processing techniques are essential because of the ever-expanding list of biomarker surrogates under evaluation and development that can be rapidly sampled through tissue and gene arrays. Automated acquisition of composite, high-resolution digitized tissue section images with rapid, precise image analysis is also needed to ensure standardized objective assessments.…”

Section: Quantitative Analysis and Measurement Methods Reproducibilitymentioning

confidence: 99%

Validation of novel optical imaging technologies: the pathologists’ view

et al. 2007

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Noninvasive optical imaging technology has the potential to improve the accuracy of disease detection and predict treatment response. Pathology provides the critical link between the biological basis of an image or spectral signature and clinical outcomes obtained through optical imaging. The validation of optical images and spectra requires both morphologic diagnosis from histopathology and parametric analysis of tissue features above and beyond the declared pathologic "diagnosis." Enhancement of optical imaging modalities with exogenously applied biomarkers also requires validation of the biological basis for molecular contrast. For an optical diagnostic or prognostic technology to be useful, it must be clinically important, independently informative, and of demonstrated beneficial value to patient care. Its usage must be standardized with regard to methods, interpretation, reproducibility, and reporting, in which the pathologist plays a key role. By providing insight into disease pathobiology, interpretive or quantitative analysis of tissue material, and expertise in molecular diagnosis, the pathologist should be an integral part of any team that is validating novel optical imaging modalities. This review will consider (1) the selection of validation biomarkers; (2) standardization in tissue processing, diagnosis, reporting, and quantitative analysis; (3) the role of the pathologist in study design; and (4) reference standards, controls, and interobserver variability.

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Section: Quantitative Analysis and Measurement Methods Reproducibilitymentioning

confidence: 99%

Validation of novel optical imaging technologies: the pathologists’ view

et al. 2007

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“…We applied novel statistical models that were developed to account for the nested structure of the data. The cumulative log-odds (CLO) method assumes that given the disease state, the cellular measurements share an identical distribution and are independent of each other, modeled by the posterior log-odds of disease [22]. An extension of this method relaxes the assumption, allowing for heterogeneity of the distributions of the data within a disease state [23].…”

Section: Statistical Modelmentioning

confidence: 99%

Prediction using hierarchical data: Applications for automated detection of cervical cancer

Yamal¹,

Guillaud

Atkinson

et al. 2015

Statistical Analysis

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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.

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“…Individuals associated with this core have done extensive work to address each of the technology development categories outlined above, including statistical analyses and algorithm development for several research components (23, 25–27, 30, 36, 38–40, 50, 52–53, 55–59, 62, 65, 67, 69, 71, 74–75, 81, 85–87, 92–93, 95–96, 98–99, 103, 105, 113–118). They have also worked closely with the other research cores associated with this project (30, 64, 72, 106–108, 110–111, 118–121). This group has also been responsible for the creation and proper maintenance of our secure patient database – and the development of software and statistical approaches for analyzing this collected information (102, 122).…”

Section: Foundations (Project Cores)mentioning

confidence: 99%

Optical Technologies and Molecular Imaging for Cervical Neoplasia: A Program Project Update

et al. 2012

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There is an urgent global need for effective and affordable approaches to cervical cancer screening and diagnosis. For developing nations, cervical malignancies remain the leading cause of cancer death in women. This reality is difficult to accept given that these deaths are largely preventable; where cervical screening programs are implemented, cervical cancer deaths decrease dramatically. In the developed world, the challenges with respect to cervical disease stem from high costs and over-treatment. We are presently eleven years into a National Cancer Institute-funded Program Project (P01 CA82710) that is evaluating optical technologies for their applicability to the cervical cancer problem. Our mandate is to create new tools for disease detection and diagnosis that are inexpensive, require minimal expertise to use, are more accurate than existing modalities, and will be feasibly implemented in a variety of clinical settings. Herein, we update the status of this work and explain the long-term goals of this project.

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Classification using the cumulative log-odds in the quantitative pathologic diagnosis of adenocarcinoma of the cervix

Cited by 6 publications

References 7 publications

Validation of novel optical imaging technologies: the pathologists’ view

Validation of novel optical imaging technologies: the pathologists’ view

Prediction using hierarchical data: Applications for automated detection of cervical cancer

Optical Technologies and Molecular Imaging for Cervical Neoplasia: A Program Project Update

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