Prediction of Cervical Intraepithelial Neoplasia and Invasive Cancer Using Deep Learning System Applied to Microscopic Cytological Images: A Clinical, Multicenter, Diagnostic Study

2019 ◽  
Author(s):  
Heling Bao ◽  
Hui Bi ◽  
Xiaosong Zhang ◽  
Liwen Fang ◽  
Linhong Wang
Keyword(s):  
Deep Learning ◽  
Cervical Intraepithelial Neoplasia ◽  
Intraepithelial Neoplasia ◽  
Learning System ◽  
Invasive Cancer ◽  
Diagnostic Study

scholarly journals An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening

2019 ◽  
Vol 111 (9) ◽  
pp. 923-932 ◽  
Author(s):  
Liming Hu ◽  
David Bell ◽  
Sameer Antani ◽  
Zhiyun Xue ◽  
Kai Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cervical Intraepithelial Neoplasia ◽  
Statistical Tests ◽  
Adult Population ◽  
Cervical Screening ◽  
Intraepithelial Neoplasia ◽  
Screening Methods ◽  
Adenocarcinoma In Situ ◽  
Visual Evaluation ◽  
Human Papillomavirus Vaccination

Abstract Background Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid application is practical but not reproducible or accurate. The objective of this study was to develop a “deep learning”-based visual evaluation algorithm that automatically recognizes cervical precancer/cancer. Methods A population-based longitudinal cohort of 9406 women ages 18–94 years in Guanacaste, Costa Rica was followed for 7 years (1993–2000), incorporating multiple cervical screening methods and histopathologic confirmation of precancers. Tumor registry linkage identified cancers up to 18 years. Archived, digitized cervical images from screening, taken with a fixed-focus camera (“cervicography”), were used for training/validation of the deep learning-based algorithm. The resultant image prediction score (0–1) could be categorized to balance sensitivity and specificity for detection of precancer/cancer. All statistical tests were two-sided. Results Automated visual evaluation of enrollment cervigrams identified cumulative precancer/cancer cases with greater accuracy (area under the curve [AUC] = 0.91, 95% confidence interval [CI] = 0.89 to 0.93) than original cervigram interpretation (AUC = 0.69, 95% CI = 0.63 to 0.74; P < .001) or conventional cytology (AUC = 0.71, 95% CI = 0.65 to 0.77; P < .001). A single visual screening round restricted to women at the prime screening ages of 25–49 years could identify 127 (55.7%) of 228 precancers (cervical intraepithelial neoplasia 2/cervical intraepithelial neoplasia 3/adenocarcinoma in situ [AIS]) diagnosed cumulatively in the entire adult population (ages 18–94 years) while referring 11.0% for management. Conclusions The results support consideration of automated visual evaluation of cervical images from contemporary digital cameras. If achieved, this might permit dissemination of effective point-of-care cervical screening.

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