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人工智能辅助细胞学系统在宫颈癌高危人群筛查策略中的临床评估。

Clinical evaluation of an artificial intelligence-assisted cytological system among screening strategies for a cervical cancer high-risk population.

机构信息

Department of Obstetrics and Gynecology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.

Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China.

出版信息

BMC Cancer. 2024 Jun 27;24(1):776. doi: 10.1186/s12885-024-12532-y.

Abstract

BACKGROUND

Primary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage rates of human papillomavirus (HPV) vaccines and routine screening are low in most developing countries and even some developed countries. This study aimed to explore the benefit of an artificial intelligence-assisted cytology (AI) system in a screening program for a cervical cancer high-risk population in China.

METHODS

A total of 1231 liquid-based cytology (LBC) slides from women who underwent colposcopy at the Chinese PLA General Hospital from 2018 to 2020 were collected. All women had received a histological diagnosis based on the results of colposcopy and biopsy. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), false-positive rate (FPR), false-negative rate (FNR), overall accuracy (OA), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and Youden index (YI) of the AI, LBC, HPV, LBC + HPV, AI + LBC, AI + HPV and HPV Seq LBC screening strategies at low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) thresholds were calculated to assess their effectiveness. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic values of the different screening strategies.

RESULTS

The Se and Sp of the primary AI-alone strategy at the LSIL and HSIL thresholds were superior to those of the LBC + HPV cotesting strategy. Among the screening strategies, the YIs of the AI strategy at the LSIL + threshold and HSIL + threshold were the highest. At the HSIL + threshold, the AI strategy achieved the best result, with an AUC value of 0.621 (95% CI, 0.587-0.654), whereas HPV testing achieved the worst result, with an AUC value of 0.521 (95% CI, 0.484-0.559). Similarly, at the LSIL + threshold, the LBC-based strategy achieved the best result, with an AUC of 0.637 (95% CI, 0.606-0.668), whereas HPV testing achieved the worst result, with an AUC of 0.524 (95% CI, 0.491-0.557). Moreover, the AUCs of the AI and LBC strategies at this threshold were similar (0.631 and 0.637, respectively).

CONCLUSIONS

These results confirmed that AI-only screening was the most authoritative method for diagnosing HSILs and LSILs, improving the accuracy of colposcopy diagnosis, and was more beneficial for patients than traditional LBC + HPV cotesting.

摘要

背景

原发性宫颈癌筛查和癌前病变治疗是预防宫颈癌的有效方法。然而,在大多数发展中国家,甚至一些发达国家,人乳头瘤病毒(HPV)疫苗和常规筛查的覆盖率都很低。本研究旨在探讨人工智能辅助细胞学(AI)系统在我国宫颈癌高危人群筛查项目中的获益。

方法

收集 2018 年至 2020 年在中国人民解放军总医院行阴道镜检查的 1231 例液基细胞学(LBC)涂片。所有女性均根据阴道镜和活检结果进行了组织学诊断。计算 AI、LBC、HPV、LBC+HPV、AI+LBC、AI+HPV 和 HPV Seq LBC 筛查策略在低级别鳞状上皮内病变(LSIL)和高级别鳞状上皮内病变(HSIL)阈值下的灵敏度(Se)、特异性(Sp)、阳性预测值(PPV)、阴性预测值(NPV)、假阳性率(FPR)、假阴性率(FNR)、总准确率(OA)、阳性似然比(PLR)、阴性似然比(NLR)和约登指数(YI),以评估其有效性。进行受试者工作特征(ROC)曲线分析以评估不同筛查策略的诊断价值。

结果

在 LSIL 和 HSIL 阈值下,初级 AI 单独策略的 Se 和 Sp 优于 LBC+HPV 联合检测策略。在筛查策略中,LSIL+阈值和 HSIL+阈值下 AI 策略的 YI 最高。在 HSIL+阈值下,AI 策略的 AUC 值最高,为 0.621(95%CI,0.587-0.654),而 HPV 检测的 AUC 值最低,为 0.521(95%CI,0.484-0.559)。同样,在 LSIL+阈值下,基于 LBC 的策略的 AUC 最高,为 0.637(95%CI,0.606-0.668),而 HPV 检测的 AUC 最低,为 0.524(95%CI,0.491-0.557)。此外,该阈值下 AI 和 LBC 策略的 AUC 值相似(分别为 0.631 和 0.637)。

结论

这些结果证实,AI 单独筛查是诊断 HSIL 和 LSIL 最权威的方法,提高了阴道镜诊断的准确性,比传统的 LBC+HPV 联合检测更有利于患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22be/11212367/974af953cdf3/12885_2024_12532_Fig1_HTML.jpg

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