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Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning



Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screening have led to a drastic reduction in the mortality rate. However, the routine examination of screening as a regular health checkup of women is characterized as time-consuming and labor-intensive, while there is lack of characteristic phenotypic profile and quantitative analysis. In this research, over the analysis of a privately collected and manually annotated dataset of 130 cytological whole-slide images, the authors proposed a deep-learning diagnostic system to localize, grade, and quantify squamous cell abnormalities. The system can distinguish abnormalities at the morphology level, namely atypical squamous cells of undetermined significance, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, and squamous cell carcinoma, as well as differential phenotypes of normal cells. The case study covered 51 positive and 79 negative digital gynecologic cytology slides collected from 2016 to 2018. Our automatic diagnostic system demonstrated its sensitivity of 100% at slide-level abnormality prediction, with the confirmation with three pathologists who performed slide-level diagnosis and training sample annotations. In the cellular-level classification, we yielded an accuracy of 94.5% in the binary classification between normality and abnormality, and the AUC was above 85% for each subtype of epithelial abnormality. Although the final confirmation from pathologists is often a must, empirically, computer-aided methods are capable of the effective extraction, interpretation, and quantification of morphological features, while also making it more objective and reproducible.

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Fig. 1: The overall architecture of the proposed computer-aided cytology image diagnostic system.
Fig. 2: A couple of example image patches cropped from the collected slides that were used as training input in the deep-learning system.
Fig. 3: Performance of the 12-category classification in cervical cells.
Fig. 4: An example of image patch of 2,500 × 2,500 pixels cropped from a whole-slide image.
Fig. 5: The automatic quantitative profile of abnormal cells in whole slide images.
Fig. 6: Quantitative analysis on nuclear pixels of the annotated multiple-class cells at the magnification rate of 40×.


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Author information




JK conceived and designed the system, performed the analysis, interpreted the results, and wrote the paper. YS constructed the mathematical models. YS and YL worked on the figures and graphs. YL and JD implemented the system and profiled the experiment results. JDW, YZ, QH, and FJ made the pathological annotation and diagnosed the slides. NJ and DW suggested the overall architecture. XL and JK collected the data.

Corresponding author

Correspondence to Jing Ke.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

The data used in this study were collected from Shanxi Tumor Hospital, China. All experiments were conducted in accordance with the Ethical Guidelines for Shanxi Tumor Hospital. An ethics commitment from Shanxi Tumor Hospital granted this dataset to the corresponding author for this research on July 29, 2019, and the ethical approval ID was 201903.

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Ke, J., Shen, Y., Lu, Y. et al. Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning. Lab Invest (2021).

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