多模态超声模型对子宫内膜癌的研究进展
Research Progress of Multimodal Ultrasound Model on Endometrial Cancer
DOI: 10.12677/ACM.2024.141069, PDF,    科研立项经费支持
作者: 梁亚蒙, 周慧丽*:新疆医科大学第一附属医院妇产超声科暨新疆医科大学第一临床医学院妇产超声科,新疆超声医学重点实验室,新疆 乌鲁木齐
关键词: 子宫内膜癌超声特征肿瘤标志物临床因素模型Endometrial Cancer Ultrasonic Feature Tumor Marker Clinical Variables Model
摘要: 子宫内膜癌(Endometrial cancer, EC)其发病率在全球范围内呈上升趋势,早期诊断EC可以改善患者的长期预后,寻找确切可靠的早期评价指标指导临床EC的治疗显得尤为重要。临床因素、超声特征(二维超声、多普勒超声等)及血清学肿瘤标志物对EC都具有良好的诊断价值,既有采用单一指标,也有多因素联合诊断,多模态超声模型也被证实可有效提高EC的诊断率,但是目前仍没有统一的共识。多模态超声模型在区别宫腔良恶性、预测淋巴结转移、预测子宫肌层浸润深度方面有较好的应用,可优化EC的生存率,在评估预后情况、治疗方案制定上发挥作用。本文对EC的影响因素及多模态超声模型在EC应用中的研究进展等相关问题进行综述。
Abstract: The incidence rate of endometrial cancer (EC) is on the rise worldwide. Early diagnosis of EC can improve the long-term prognosis of patients. It is particularly important to find accurate and relia-ble early evaluation indicators to guide clinical treatment of EC. Clinical treatment, ultrasound fea-tures (such as two-dimensional ultrasound, Doppler ultrasound, etc.), and serological tumor mark-ers all have good diagnostic value for EC. There are both single indicators and multi factor joint di-agnoses, and multimodal ultrasound models have been proven to effectively improve the diagnostic rate of EC. However, there is currently no unified consensus. The multimodal ultrasound model has good applications in distinguishing between benign and malignant uterine cavities, predicting lymph node metastasis, and predicting the depth of uterine myometrial invasion. It can optimize the survival rate of EC and play a role in evaluating prognosis and formulating treatment plans. This article reviews the influencing factors of EC and the research progress of multimodal ultrasound models in the application of EC.
文章引用:梁亚蒙, 周慧丽. 多模态超声模型对子宫内膜癌的研究进展[J]. 临床医学进展, 2024, 14(1): 488-495. https://doi.org/10.12677/ACM.2024.141069

参考文献

[1] Siegel, R.L., Miller, K.D., Fuchs, H.E., et al. (2022) Cancer Statistics, 2022. CA: A Cancer Journal for Clinicians, 72, 7-33. [Google Scholar] [CrossRef] [PubMed]
[2] 曹瑾瑾, 李佳, 李奇灵, 等. 子宫内膜癌筛查策略[J]. 中国实用妇科与产科杂志, 2023, 39(11): 1062-1065.
[3] Li, C., Yu, J. and Fu, Z. (2021) Application of CT and MRI Com-bined with VEGF-C and EGFR in the Identification of Endometrial Cancer Stages. American Journal of Translational Research, 13, 7164-7171.
[4] 李玲玲, 李赫, 李舰, 等. 结合分子分型的子宫内膜癌患者淋巴结转移的危险因素分析[J]. 中华妇产科杂志, 2023, 58(10): 733-741.
[5] Lu, K.H. and Broaddus, R.R. (2020) Endometrial Cancer. New England Journal of Medicine, 383, 2053-2064. [Google Scholar] [CrossRef
[6] Benedet, J.L., Bender, H., Jones, H., et al. (2000) FIGO Staging Classifications and Clinical Practice Guidelines in the Management of Gynecologic Cancers. FIGO Committee on Gyne-cologic Oncology. International Journal of Gynecology & Obstetrics, 70, 209-262. [Google Scholar] [CrossRef
[7] 李淑祎, 王青. 子宫内膜癌术前影像学检查及分期研究进展[J]. 中国中西医结合影像学杂志, 2021, 19(4): 402-404.
[8] Knific, T., Osredkar, J., Smrkolj, P., et al. (2017) Novel Algorithm Including CA-125, HE4 and Body Mass Index in the Diagnosis of Endometrial Cancer. Gynecologic Oncology, 147, 126-132. [Google Scholar] [CrossRef] [PubMed]
[9] Crosbie, E.J., Kitson, S.J., McAlpine, J.N., et al. (2022) Endo-metrial Cancer. The Lancet, 399, 1412-1428. [Google Scholar] [CrossRef
[10] Liu, F., Cheung, E.C.W. and Lao, T.T. (2021) Obesity In-creases Endometrial Cancer Risk in Chinese Women with Postmenopausal Bleeding. Menopause (New York, NY), 28, 1093-1098. [Google Scholar] [CrossRef
[11] Goodman, A. (2021) Is Obesity Predictive of Endometrial Cancer for Women with Postmenopausal Bleeding? Menopause, 28, 1081-1082. [Google Scholar] [CrossRef
[12] Lundberg, F.E., Iliadou, A.N., Rodriguez-Wallberg, K., et al. (2019) The Risk of Breast and Gynecological Cancer in Women with a Diagnosis of Infertility: A Nationwide Popula-tion-Based Study. European Journal of Epidemiology, 34, 499-507. [Google Scholar] [CrossRef] [PubMed]
[13] Skalkidou, A., et al. (2017) Risk of Endometrial Cancer in Wom-en Treated with Ovary-Stimulating Drugs for Subfertility. Cochrane Database of Systematic Reviews, 3, CD010931. [Google Scholar] [CrossRef
[14] Bagepalli, S.S., Kubakaddi, S.S., Polisetti, S., et al. (2020) A Novel Risk-Scoring Model for Prediction of Premalignant and Malignant Lesions of Uterine Endometrium among Symptomatic Premenopausal Women. International Journal of Women’s Health, 12, 883-891. [Google Scholar] [CrossRef
[15] Giannella, L., Cerami, L.B., Setti, T., et al. (2019) Prediction of En-dometrial Hyperplasia and Cancer among Premenopausal Women with Abnormal Uterine Bleeding. BioMed Research International, 2019, Article ID: 8598152. [Google Scholar] [CrossRef
[16] Nicklin, J., Janda, M., Gebski, V., et al. (2012) The Utility of Serum CA-125 in Predicting Extra-Uterine Disease in Apparent Early-Stage Endometrial Cancer. International Journal of Can-cer, 131, 885-890. [Google Scholar] [CrossRef] [PubMed]
[17] Nithin, K.U., Sridhar, M.G., Srilatha, K., et al. (2018) CA125 Is a Better Marker to Differentiate Endometrial Cancer and Abnormal Uterine Bleeding. African Health Sciences, 18, 972-978. [Google Scholar] [CrossRef] [PubMed]
[18] D’Ambrosio, V., Brunelli, R., Musacchio, L., et al. (2021) Adnexal Masses in Pregnancy: An Updated Review on Diagnosis and Treatment. Tumori Journal, 107, 12-16. [Google Scholar] [CrossRef] [PubMed]
[19] Li, J., Wang, X., Qu, W., et al. (2019) Comparison of Serum Human Epididymis Protein 4 and CA125 on Endometrial Cancer Detection: A Meta-Analysis. Clinica Chimica Acta, 488, 215-220. [Google Scholar] [CrossRef] [PubMed]
[20] Brennan, D.J., Hackethal, A., Metcalf, A.M., et al. (2014) Serum HE4 as a Prognostic Marker in Endometrial Cancer—A Population Based Study. Gynecologic Oncology, 132, 159-165. [Google Scholar] [CrossRef] [PubMed]
[21] Degez, M., Chauvire-Drouard, H., Leroy, A., Lair, M., Winer, D., et al. (2021) Endometrial Cancer: A Systematic Review of HE4, REM and REM-B. Clinica Chimica Acta: International Journal of Clinical Chemistry and Applied Molecular Biology, 515, 27-36. [Google Scholar] [CrossRef] [PubMed]
[22] Prueksaritanond, N., Cheanpracha, P. and Yanaranop, M. (2016) Association of Serum HE4 with Primary Tumor Diameter and Depth of Myometrial Invasion in Endometrial Cancer Pa-tients at Rajavithi Hospital. Asian Pacific Journal of Cancer Prevention, 17, 1489-1492. [Google Scholar] [CrossRef
[23] Quan, Q., Liao, Q., Yin, W., et al. (2021) Serum HE4 and CA125 Combined to Predict and Monitor Recurrence of Type II Endometrial Carcinoma. Scientific Reports, 11, Article No. 21694. [Google Scholar] [CrossRef] [PubMed]
[24] Lin, D., Zhao, L., Zhu, Y., et al. (2021) Combination IETA Ul-trasonographic Characteristics Simple Scoring Method with Tumor Biomarkers Effectively Improves the Differentiation Ability of Benign and Malignant Lesions in Endometrium and Uterine Cavity. Frontiers in Oncology, 11, Article ID: 605847. [Google Scholar] [CrossRef] [PubMed]
[25] Wynants, L., Verbakel, J.Y.J., Valentin, L., et al. (2022) The Risk of Endometrial Malignancy and Other Endometrial Pathology in Women with Abnormal Uterine Bleeding: An Ultrasound-Based Model Development Study by the IETA Group. Gynecologic and Obstetric Investigation, 87, 54-61. [Google Scholar] [CrossRef] [PubMed]
[26] Hao, Y., Ren, G., Yang, W., et al. (2020) Combination Diagnosis with Elastography Strain Ratio and Molecular Markers Effectively Improves the Diagnosis Rate of Small Breast Cancer and Lymph Node Metastasis. Quantitative Imaging in Medicine and Surgery, 10, 678-691. [Google Scholar] [CrossRef] [PubMed]
[27] Opolskiene, G., Sladkevicius, P. and Valentin, L. (2007) Ultrasound Assessment of Endometrial Morphology and Vascularity to Predict Endometrial Malignancy in Women with Postmeno-pausal Bleeding and Sonographic Endometrial Thickness > or = 4.5 mm. Ultrasound in Obstetrics & Gynecology, 30, 332-340. [Google Scholar] [CrossRef] [PubMed]
[28] Sladkevicius, P., et al. (2017) Prospective Temporal Validation of Mathematical Models to Calculate Risk of Endometrial Malignancy in Patients with Postmenopausal Bleeding. Ultra-sound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 49, 649-656. [Google Scholar] [CrossRef] [PubMed]
[29] Opolskiene, G., Sladkevicius, P. and Valentin, L. (2011) Prediction of En-dometrial Malignancy in Women with Postmenopausal Bleeding and Sonographic Endometrial Thickness ≥ 4.5 mm. Ul-trasound in Obstetrics & Gynecology, 37, 232-240. [Google Scholar] [CrossRef] [PubMed]
[30] Dueholm, M., Møller, C., Rydbjerg, S., et al. (2014) An Ultrasound Algorithm for Identification of Endometrial Cancer. Ultrasound in Obstet-rics & Gynecology, 43, 557-568. [Google Scholar] [CrossRef] [PubMed]
[31] Dueholm, M., Hjorth, I.M.D., Dahl, K., et al. (2019) Ultrasound Scoring of Endometrial Pattern for Fast-Track Identification or Exclusion of Endometrial Cancer in Women with Postmenopausal Bleeding. Journal of Minimally Invasive Gynecology, 26, 516-525. [Google Scholar] [CrossRef] [PubMed]
[32] Jha, S., Singh, A., Sinha, H.H., et al. (2021) Rate of Premalignant and Malignant Endometrial Lesion in “Low-Risk” Premenopausal Women with Abnormal Uterine Bleeding Undergoing Endometrial Biopsy. Obstetrics & Gynecology Science, 64, 517-523. [Google Scholar] [CrossRef] [PubMed]
[33] Tameish, S., Florez, N., Vidal, J.R.P., et al. (2023) Transvaginal Ultra-sound versus Magnetic Resonance Imaging for Preoperative Assessment of Myometrial Infiltration in Patients with Low-Grade Endometrioid Endometrial Cancer: A Systematic Review and Head-to-Head Meta-Analysis. Journal of Clin-ical Ultrasound, 51, 1188-1197. [Google Scholar] [CrossRef] [PubMed]
[34] Juan Luis Alcázar, M., Rosendo Galván, M., Albela, S., et al. (2009) As-sessing Myometrial Infiltration by Endometrial Cancer: Uterine Virtual Navigation with Three-Dimensional US. Radiol-ogy, 250, 776-783. [Google Scholar] [CrossRef] [PubMed]
[35] Van Holsbeke, C., Ameye, L., Testa, A.C., et al. (2014) Develop-ment and External Validation of New Ultrasound-Based Mathematical Models for Preoperative Prediction of High-Risk Endometrial Cancer. Ultrasound in Obstetrics & Gynecology, 43, 586-595. [Google Scholar] [CrossRef] [PubMed]
[36] Smet, F.D., Brabanter, J.D., Bosch, T.V.D., et al. (2006) New Models to Predict Depth of Infiltration in Endometrial Carcinoma Based on Transvaginal Sonography. Ultrasound in Obstetrics and Gynecology, 27, 664-671. [Google Scholar] [CrossRef] [PubMed]
[37] Verbakel, J.Y., Mascilini, F., Wynants, L., et al. (2020) Validation of Ultra-sound Strategies to Assess Tumor Extension and to Predict High-Risk Endometrial Cancer in Women from the Prospec-tive IETA (International Endometrial Tumor Analysis)-4 Cohort. Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 55, 115-124. [Google Scholar] [CrossRef] [PubMed]
[38] Epstein, E. and Blomqvist, L. (2014) Imaging in Endometrial Cancer. Best Practice & Research Clinical Obstetrics & Gynaecology, 28, 721-739. [Google Scholar] [CrossRef] [PubMed]
[39] Berg, H.F., Ju, Z., Myrvold, M., et al. (2020) Development of Prediction Models for Lymph Node Metastasis in Endometrioid Endometrial Carcinoma. British Journal of Cancer, 122, 1014-1022. [Google Scholar] [CrossRef] [PubMed]
[40] Lee, J., Kong, T.W., Paek, J., et al. (2016) Predicting Model of Lymph Node Metastasis Using Preoperative Tumor Grade, Transvaginal Ultrasound, and Serum CA-125 Level in Pa-tients with Endometrial Cancer. International Journal of Gynecological Cancer, 26, 1630-1635. [Google Scholar] [CrossRef
[41] Ignatov, T., Eggemann, H., Burger, E., et al. (2018) Ovarian Metastasis in Patients with Endometrial Cancer: Risk Factors and Impact on Survival. Journal of Cancer Research & Clinical Oncology, 144, 1103-1107. [Google Scholar] [CrossRef] [PubMed]
[42] Pan, Z., Wang, X., Zhang, X., et al. (2011) Retrospective Analy-sis on Coexisting Ovarian Cancer in 976 Patients with Clinical Stage I Endometrial Carcinoma. Journal of Obstetrics & Gynaecology Research, 37, 352-358. [Google Scholar] [CrossRef] [PubMed]
[43] Hou, T., Li, Y.D., Liu, J.Y., et al. (2017) The Safety of Ovarian Preservation in Stage I Endometrial Endometrioid Adenocarcinoma Based on Propensity Score Matching. Com-binatorial Chemistry & High Throughput Screening, 20, 647-655. [Google Scholar] [CrossRef] [PubMed]
[44] Shen, L., Xie, L., Li, R., et al. (2020) A Preoperative Prediction Model for Predicting Coexisting Adnexa Malignancy of Patients with G1/G2 Endometrioid Endometrial Can-cer. Gynecologic Oncology, 159, 402-408. [Google Scholar] [CrossRef] [PubMed]