Cervical cancer screening system TruScreen with graph neural network-based classification versus HPV DNA quantification in high-risk HPV-positive patients
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1.Department of Gynecology,Qianjiang Hospital,Chongqing University;2.School of Computer Science and Technology,Chongqing University of Posts and Telecommunications;3.Department of Clinical Laboratory, Qianjiang Hospital,Chongqing University;4.Science and Education Department,Yubei District People’s Hospital of Chongqing

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R71

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    Abstract:

    Objective To compare the effectiveness of TruScreen(TS) integrated with a graph neural network and human papillomavirus(HPV) DNA quantification in cervical precancer and cancer screening in high-risk HPV-positive patients.Methods We included 400 patients whotested positive for high-risk HPV subtypes at the Department of Gynecology,Qianjiang Hospital,Chongqing University from January 2022 to May 2023. All the patients underwent TS,HPV DNA quantification in cervical cells,and a colposcopy-directed biopsy. The biopsy pathology results were used as the gold standard. The TS system was combined with graph neural network-based representation learning and feature extraction and classification with support vector machine and random forest classifiers. We calculated and compared the sensitivity,specificity,area under the receiver operating characteristic curve(AUC),negative predictive value,positive predictive value,agreement with biopsy pathology (kappa value) of the modified TS system and HPV DNA quantification in cervical precancer and cancer screening.Results The sensitivity,specificity,AUC,negative predictive value,and positive predictive value of TS with graph neural network-based classification for cervical precancer and cancer screening were 93.75%,95.45%,0.97,98.18%,and 87.50%,respectively,which were higher than those of HPV DNA quantitative testing (81.25%,90.91%,0.92,96.15%,and 64.29%,respectively). The kappa coefficient of agreement of TS with graph neural network-based classification with biopsy pathology was 0.89,higher than 0.72 of HPV DNA quantitative testing with biopsy pathology. There was a significant difference in the AUC of TS with graph neural network-based classification and HPV DNA quantitative testing (P<0.05).Conclusion TS with graph neural network-based classification has better performance in screening for cervical precancer and cancer in high-risk HPV-positive patients than HPV DNA quantitative testing,with high accuracy and reliability. It can be used as a simple and effective cervical cancer screening method.

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Hu Yanli, Zhou Leming, Huang Zhongxiu, Huang Shixin, Li Xiaosong. Cervical cancer screening system TruScreen with graph neural network-based classification versus HPV DNA quantification in high-risk HPV-positive patients[J]. Journal of Chongqing Medical University,2023,48(12):1501-1506

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  • Received:October 23,2023
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  • Online: January 08,2024
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