Management of Abnormal Cervical Cancer Screening Test: A Risk-based Approach

Guido, Richard; Perkins, Rebecca B.

doi:10.1097/grf.0000000000000794

Clinical Obstetrics &Amp; Gynecology

2023

DOI: 10.1097/grf.0000000000000794

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Management of Abnormal Cervical Cancer Screening Test: A Risk-based Approach

Richard Guido,

Rebecca B. Perkins

Abstract: Cervical cancer screening plays a major role in preventing cervical cancer. The field is based on understanding the natural history of human papillomavirus and its role in cervical cancer. Screening has evolved to assessing the risk for cervical intraepithelial neoplasia grade 3, a true cancer precursor, and performing diagnostic tests based on those risks. This article summarizes the present state of management of abnormal cervical cancer screening tests in the United States, based on the most recent 2019 Ame… Show more

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2024

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Cited by 4 publications

References 45 publications

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Association Between Clinician Confidence and Making Guideline-Recommended Decisions in the Management of Abnormal Cervical Cancer Screening Results

Dang,

Rieu-Werden,

Kobrin

et al. 2024

J GEN INTERN MED

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Association Between Clinician Confidence and Making Guideline-Recommended Decisions in the Management of Abnormal Cervical Cancer Screening Results

Dang,

Rieu-Werden,

Kobrin

et al. 2024

J GEN INTERN MED

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Cervical Cancer

Sahasrabuddhe

2024

Hematology/Oncology Clinics of North America

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Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression

Aquilina,

Papagiannakis

2024

J Low Genit Tract Dis

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Objective A deep learning classifier that improves the accuracy of colposcopic impression. Methods Colposcopy images taken 56 seconds after acetic acid application were processed by a cervix detection algorithm to identify the cervical region. We optimized models based on the SegFormer architecture to classify each cervix as high-grade or negative/low-grade. The data were split into histologically stratified, random training, validation, and test subsets (80%–10%–10%). We replicated a 10-fold experiment to align with a prior study utilizing expert reviewer analysis of the same images. To evaluate the model's robustness across different cameras, we retrained it after dividing the dataset by camera type. Subsequently, we retrained the model on a new, histologically stratified random data split and integrated the results with patients' age and referral data to train a Gradient Boosted Tree model for final classification. Model accuracy was assessed by the receiver operating characteristic area under the curve (AUC), Youden's index (YI), sensitivity, and specificity compared to the histology. Results Out of 5,485 colposcopy images, 4,946 with histology and a visible cervix were used. The model's average performance in the 10-fold experiment was AUC = 0.75, YI = 0.37 (sensitivity = 63%, specificity = 74%), outperforming the experts' average YI of 0.16. Transferability across camera types was effective, with AUC = 0.70, YI = 0.33. Integrating image-based predictions with referral data improved outcomes to AUC = 0.81 and YI = 0.46. The use of model predictions alongside the original colposcopic impression boosted overall performance. Conclusions Deep learning cervical image classification demonstrated robustness and outperformed experts. Further improved by including additional patient information, it shows potential for clinical utility complementing colposcopy.

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Management of Abnormal Cervical Cancer Screening Test: A Risk-based Approach

Cited by 4 publications

References 45 publications

Association Between Clinician Confidence and Making Guideline-Recommended Decisions in the Management of Abnormal Cervical Cancer Screening Results

Association Between Clinician Confidence and Making Guideline-Recommended Decisions in the Management of Abnormal Cervical Cancer Screening Results

Cervical Cancer

Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression

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