人工智能在宫颈癌诊疗中的应用进展

Progress in applying artificial intelligence in the diagnosis and treatment of cervical cancer

摘要

宫颈癌是全球女性第四大常见恶性肿瘤。当前诊疗面临筛查效率低下、诊断一致性欠佳等挑战。人工智能在宫颈癌诊疗中展现出显著潜力：在筛查环节实现自动化高效分析，显著提升检测精准度；在诊断阶段通过智能影像分割技术和数字病理分析优化诊断精准性；在治疗过程中辅助优化术前评估与放疗靶区勾画。本文旨在通过综述人工智能在宫颈癌筛查、精准诊断、治疗优化等诊疗环节中的应用，为人工智能在宫颈癌诊疗的策略优化及未来研究提供理论参考与实践指引。

Abstract

Cervical cancer ranks as the fourth most common malignancy among women worldwide. Current clinical management faces significant challenges, including inefficient screening processes and inconsistent diagnostic outcomes. Artificial intelligence has demonstrated transformative potentials in cervical cancer care: enabling automated high-efficiency analysis to substantially enhance screening accuracy during detection phases; improving diagnostic precision through intelligent image segmentation and digital pathology analysis and optimizing treatment planning with artificial intelligence-assisted preoperative evaluation and radiotherapy target delineation. This comprehensive review examined artificial intelligence applications across cervical cancer screening, precision diagnosis and treatment optimization, providing both theoretical frameworks and practical guidance for advancing artificial intelligence-integrated clinical strategies and future research directions.

关键词

人工智能 / 机器学习 / 深度学习 / 大模型 / 宫颈癌 / 筛查 / 智能影像 / 数字病理

Key words

artificial intelligence / machine learning / deep learning / large model / cervical cancer / screening / intelligent imaging / digital pathology

参考文献

[1] BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. Ca: a cancer journal for clinicians, 2024, 74(3): 229-263.

[2] RAHANGDALE L, MUNGO C, O’CONNOR S, et al. Human papillomavirus vaccination and cervical cancer risk[J]. BMJ, 2022, 379: e070115.

[3] ZHANG M, ZHONG Y J, BAO H L, et al. Breast cancer screening rates among women aged 20 years and above: China, 2015[J]. China CDC weekly, 2021, 3(13): 267-273.

[4] ZHANG M, ZHONG Y J, ZHAO Z P, et al. Cervical cancer screening rates among Chinese women: China, 2015[J]. China CDC weekly, 2020, 2(26): 481-486.

[5] 中华人民共和国国家卫生健康委员会, 中华人民共和国教育部, 中华人民共和国民政部, 等. 关于印发加速消除宫颈癌行动计划（2023—2030年）的通知（国卫妇幼发〔2023〕1号）[EB/OL]. (2023—01—05)[2025—07—16]. https://www.gov.cn/zhengce/zhengceku/2023—01/21/content_5738364.htm.

[6] QI J L, LI M L, WANG L J, et al. National and subnational trends in cancer burden in China, 2005—20: an analysis of national mortality surveillance data[J]. The lancet public health, 2023, 8(12): e943-e955.

[7] DIAO X Y, GUO C, JIN Y K, et al. Cancer situation in China: an analysis based on the global epidemiological data released in 2024[J]. Cancer communications, 2025, 45(2): 178-197.

[8] GROSSI E. The long journey of artificial intelligence in medicine: an overview[J]. Clinical and experimental rheumatology, 2025, 43(5): 815-821.

[9] GREENER J G, KANDATHIL S M, MOFFAT L, et al. A guide to machine learning for biologists[J]. Nature reviews molecular cell biology, 2022, 23(1): 40-55.

[10] SOFFER S, BEN—COHEN A, SHIMON O, et al. Convolutional neural networks for radiologic images: a radiologist’s guide[J]. Radiology, 2019, 290(3): 590-606.

[11] DENECKE K, MAY R, RIVERA—ROMERO O. Transformer models in healthcare: a survey and thematic analysis of potentials, shortcomings and risks[J]. Journal of medical systems, 2024, 48(1): 23.

[12] 雷紫微, 严文浩, 胡伟男, 等. 我国基层医疗卫生资源配置均衡性分析[J]. 南京医科大学学报（社会科学版）, 2023, 23(5): 404-408.

[13] LI Y, MA L, YANG C X, et al. A study on service capacity of primary medical and health institutions for cervical cancer screening in urban and rural areas in China[J]. Chinese journal of cancer research, 2019, 31(5): 838-848.

[14] 徐玲超, 项艳, 陈吉琴, 等. 宫颈薄层液基细胞学检测与人乳头瘤病毒诊断早期宫颈癌病变的一致性及二者联合诊断准确率的影响因素分析[J]. 现代实用医学, 2023, 35(8): 1004-1007.

[15] AGGARWAL A, COURT L E, HOSKIN P, et al. ARCHERY: a prospective observational study of artificial intelligence—based radiotherapy treatment planning for cervical, head and neck and prostate cancer—study protocol[J]. BMJ open, 2023, 13(12): e077253.

[16] ZHU X H, LI X M, ONG K, et al. Hybrid AI—assistive diagnostic model permits rapid TBS classification of cervical liquid—based thin—layer cell smears[J]. Nature communications, 2021, 12: 3541.

[17] XUE P, XU H M, TANG H P, et al. Assessing artificial intelligence enabled liquid—based cytology for triaging HPV—positive women: a population—based cross—sectional study[J]. Acta obstetricia et gynecologica scandinavica, 2023, 102(8): 1026-1033.

[18] BAO H L, SUN X R, ZHANG Y, et al. The artificial intelligence—assisted cytology diagnostic system in large—scale cervical cancer screening: a population—based cohort study of 0.7 million women[J]. Cancer medicine, 2020, 9(18): 6896-6906.

[19] DU H, DAI W K, ZHOU Q, et al. AI—assisted system improves the work efficiency of cytologists via excluding cytology—negative slides and accelerating the slide interpretation[J]. Frontiers in oncology, 2023, 13: 1290112.

[20] ZHU X C, YAO Q, DAI W, et al. Cervical cancer screening aided by artificial intelligence, China[J]. Bulletin of the World Health Organization, 2023, 101(6): 381-390.

[21] WANG J, YU Y F, TAN Y J, et al. Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer[J]. Nature communications, 2024, 15: 4369.

[22] XUE P, DANG L, KONG L H, et al. Deep learning enabled liquid—based cytology model for cervical precancer and cancer detection[J]. Nature communications, 2025, 16: 3506.

[23] BAI X R, WEI J J, STARR D, et al. Assessment of efficacy and accuracy of cervical cytology screening with artificial intelligence assistive system[J]. Modern pathology, 2024, 37(6): 100486.

[24] XUE P, NG M T A, QIAO Y L. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence[J]. BMC medicine, 2020, 18(1): 169.

[25] XUE P, TANG C, LI Q, et al. Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies[J]. BMC medicine, 2020, 18(1): 406.

[26] FU L, XIA W, SHI W, et al. Deep learning based cervical screening by the cross—modal integration of colposcopy, cytology, and HPV test[J]. International journal of medical informatics, 2022, 159: 104675.

[27] WANG B, ZHANG Y Y, WU C Y, et al. Multimodal MRI analysis of cervical cancer on the basis of artificial intelligence algorithm[J]. Contrast media & molecular imaging, 2021, 2021: 1673490.

[28] LU P Y, FANG F M, ZHANG H, et al. AugMS—Net: augmented multiscale network for small cervical tumor segmentation from MRI volumes[J]. Computers in biology and medicine, 2022, 141: 104774.

[29] QIU H F, WANG M, WANG S W, et al. Integrating MRI—based radiomics and clinicopathological features for preoperative prognostication of early—stage cervical adenocarcinoma patients: in comparison to deep learning approach[J]. Cancer imaging, 2024, 24(1): 101.

[30] WANG X, SU R X, LI L R, et al. Machine learning—based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy[J]. Computers in biology and medicine, 2024, 177: 108593.

[31] JIANG C Q, LI X J, ZHOU Z Y, et al. Imaging based artificial intelligence for predicting lymph node metastasis in cervical cancer patients: a systematic review and meta—analysis[J]. Frontiers in oncology, 2025, 15: 1532698.

[32] MING Y, DONG X Y, ZHAO J H, et al. Deep learning—based multimodal image analysis for cervical cancer detection[J]. Methods, 2022, 205: 46-52.

[33] HANNA M G, REUTER V E, HAMEED M R, et al. Whole slide imaging equivalency and efficiency study: experience at a large academic center[J]. Modern pathology, 2019, 32(7): 916-928.

[34] VAN DER LAAK J, LITJENS G, CIOMPI F. Deep learning in histopathology: the path to the clinic[J]. Nature medicine, 2021, 27(5): 775-784.

[35] BAETEN I G T, HOOGENDAM J P, STATHONIKOS N, et al. Artificial intelligence—based sentinel lymph node metastasis detection in cervical cancer[J]. Cancers, 2024, 16(21): 3619.

[36] CHEN C, CAO Y Y, LI W L, et al. The pathological risk score: a new deep learning—based signature for predicting survival in cervical cancer[J]. Cancer medicine, 2023, 12(2): 1051-1063.

[37] WANG R Y, GUNESLI G N, SKINGEN V E, et al. Deep learning for predicting prognostic consensus molecular subtypes in cervical cancer from histology images[J]. NPJ precision oncology, 2025, 9: 11.

[38] LIU C Y, XIU C F, ZOU Y F, et al. Cervical cancer diagnosis model using spontaneous Raman and Coherent anti—Stokes Raman spectroscopy with artificial intelligence[J]. Spectrochimica acta part A: molecular and biomolecular spectroscopy, 2025, 327: 125353.

[39] JIN W Q, LUO Q Q. When artificial intelligence meets PD—1/PD—L1 inhibitors: population screening, response prediction and efficacy evaluation[J]. Computers in biology and medicine, 2022, 145: 105499.

[40] JIANG X R, LI J X, KAN Y Y, et al. MRI based radiomics approach with deep learning for prediction of vessel invasion in early—stage cervical cancer[J]. ACM transactions on computational biology and bioinformatics, 2021, 18(3): 995-1002.

[41] LI H Y, HAN Z Q, WU H X, et al. Artificial intelligence in surgery: evolution, trends, and future directions[J]. International journal of surgery, 2025, 111(2): 2101-2111.

[42] PAVONE M, BABY B, CARLES E, et al. Critical view of safety assessment in sentinel node dissection for endometrial and cervical cancer: artificial intelligence to enhance surgical safety and lymph node detection (LYSE study)[J]. International journal of gynecological cancer, 2025, 35(5): 101789.

[43] LEON S, LEE S, PEREZ J E, et al. Artificial intelligence and the education of future surgeons[J]. The American journal of surgery, 2025, 246: 116257.

[44] JEONG S, YU H, PARK S H, et al. Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI[J]. Scientific reports, 2024, 14: 1180.

[45] CAI Z H, LI S, XIONG Z, et al. Multimodal MRI—based deep—radiomics model predicts response in cervical cancer treated with neoadjuvant chemoradiotherapy[J]. Scientific reports, 2024, 14: 19090.

[46] TIAN M, WANG H Q, LIU X G, et al. Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks[J]. Medical physics, 2023, 50(10): 6354-6365.

[47] XUE X, SUN L N, LIANG D Z, et al. Deep learning—based segmentation for high—dose—rate brachytherapy in cervical cancer using 3D Prompt—ResUNet[J]. Physics in medicine & biology, 2024, 69(19): 195008.

[48] YANG C Z, QIN L H, XIE Y E, et al. Deep learning in CT image segmentation of cervical cancer: a systematic review and meta—analysis[J]. Radiation oncology, 2022, 17(1): 175.

[49] REIJTENBAGH D, GODART J, DE LEEUW A, et al. Multi—center analysis of machine—learning predicted dose parameters in brachytherapy for cervical cancer[J]. Radiotherapy and oncology, 2022, 170: 169-175.

[50] SALEHI M, VAFAEI SADR A, MAHDAVI S R, et al. Deep learning—based non—rigid image registration for high—dose rate brachytherapy in inter—fraction cervical cancer[J]. Journal of digital imaging, 2023, 36(2): 574-587.

[51] SUN S, GONG X Y, CHENG S Y, et al. Fully automated online adaptive radiation therapy decision—making for cervical cancer using artificial intelligence[J]. International journal of radiation oncology, biology, physics, 2025, 122(4): 1012-1021.

[52] SHI R, CHANG L L, SHI L Y, et al. Development and validation of a prognostic model for cervical cancer by combination of machine learning and high—throughput sequencing[J]. European journal of surgical oncology, 2024, 50(4): 108241.

[53] LIANG J Y, HE T S, LI H, et al. Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma[J]. Journal of translational medicine, 2022, 20(1): 293.

[54] GUO C Y, WANG J, WANG Y M, et al. Novel artificial intelligence machine learning approaches to precisely predict survival and site—specific recurrence in cervical cancer: a multi—institutional study[J]. SSRN electronic journal, 2021, 14: 101032.

[55] HERMANN C E, PATEL J M, BOYD L, et al. Let’s chat about cervical cancer: assessing the accuracy of ChatGPT responses to cervical cancer questions[J]. Gynecologic oncology, 2023, 179: 164-168.

[56] 卢建华, 易碧影, 梁红飞. 利用AI技术实现院级智能随访平台的设计与应用分析[J]. 电子元器件与信息技术, 2025, 9(4): 139—141, 145.

[57] 雷婷, 曹梦祯, 李君芬, 等. 深度学习在子宫内膜癌影像诊断中的研究进展[J]. 兰州大学学报（医学版）, 2025, 51(11): 81-87.

基金资助

国家自然科学基金资助项目(82560468)

甘肃省科技计划资助项目(25YFFA051)

甘肃省科技计划资助项目(24JRRA916)