Analysis of the application values of different combination schemes of liquid-based cytology and high-risk human papilloma virus test in the screening of high-grade cervical lesions

Wang, Jian

doi:10.1590/1414-431x20187517

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Ranking as the second most common cancer in the female reproductive system, cervical cancer still manifests high morbidity and mortality in developing countries including China, imposing a strong impact on the body health and quality of life of women 1 . Fortunately, studies have demonstrated that high-risk human papillomavirus (HPV) infection can be the definite etiology of cervical cancer [2][3][4][5] , making it possible to detect 80.7-98.7% of cervical intraepithelial neoplasia (CIN) early through screening combined with HPV testing and cytology [6][7][8] . As the gold standard for diagnosing cervical cancer and its precancerous lesions, colposcopy-guided biopsies play a key role in the early detection of cervical cancer.…”

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confidence: 99%

The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images

Yuan

Yao

Cheng

et al. 2020

Sci Rep

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Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. Methods The data were collected to establish three deeplearning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. Results The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of highgrade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. Implications The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.

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The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images

Yuan

Yao

Cheng

et al. 2020

Sci Rep

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“…Extensive research confirms high-risk human papillomavirus infection as a primary cause of cervical cancer [12,17,8,25]. Early screening, when paired with HPV testing and cytology, has the potential to identify 80.7-98.7% of cervical intraepithelial neoplasia [26,3]. Colposcopy-guided biopsies are the gold standard for detecting cervical cancer and its precancerous lesions.…”

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confidence: 99%

AnnoCerv: A new dataset for feature-driven and image-based automated colposcopy analysis

Minciună,

Socolov,

Szőcs

et al. 2023

Acta Universitatis Sapientiae, Informatica

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Colposcopy imaging is pivotal in cervical cancer diagnosis, a major health concern for women. The computational challenge lies in accurate lesion recognition. A significant hindrance for many existing machine learning solutions is the scarcity of comprehensive training datasets. To reduce this gap, we present AnnoCerv: a comprehensive dataset tailored for feature-driven and image-based colposcopy analysis. Distinctively, AnnoCerv include detailed segmentations, expert-backed colposcopic annotations and Swede scores, and a wide image variety including acetic acid, iodine, and green-filtered captures. This rich dataset supports the training of models for classifying and segmenting low-grade squamous intraepithelial lesions, detecting high-grade lesions, aiding colposcopy-guided biopsies, and predicting Swede scores – a crucial metric for medical assessments and treatment strategies. To further assist researchers, our release includes code that demonstrates data handling and processing and exemplifies a simple feature extraction and classification technique.

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Evaluation of Diagnostic Tests

Barrett

Fardy

2021

Methods in Molecular Biology

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Analysis of the application values of different combination schemes of liquid-based cytology and high-risk human papilloma virus test in the screening of high-grade cervical lesions

Cited by 3 publications

References 18 publications

The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images

The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images

AnnoCerv: A new dataset for feature-driven and image-based automated colposcopy analysis

Evaluation of Diagnostic Tests

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