ArticleJournal of medical imaging (Bellingham, Wash.)2017
Feature analysis of cell nuclear chromatin distribution in support of cervical cytology.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.Journal of cancer research and clinical oncology · 2023Trial
- Development and validation of a deep learning model for individualized survival prediction in advanced cervical cancer.Discover oncology · 2025Article
- Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.Computational and mathematical methods in medicine · 2022Article
- Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.American journal of obstetrics and gynecology · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist's experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.
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