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恶性上皮性卵巢肿瘤的最新研究进展。

Current update on malignant epithelial ovarian tumors.

机构信息

The Department of Radiology, The University of Florida College of Medicine, Jacksonville, FL, USA.

Department of Radiology, UF College of Medicine-Jacksonville, 2nd Floor, Clinical Center, 655 West 8th Street, C90, Jacksonville, FL, 33209, USA.

出版信息

Abdom Radiol (NY). 2021 Jun;46(6):2264-2280. doi: 10.1007/s00261-021-03081-0. Epub 2021 Jun 5.

DOI:10.1007/s00261-021-03081-0
PMID:34089360
Abstract

Epithelial ovarian cancer (EOC) represents the most frequently occurring gynecological malignancy, accounting for more than 70% of ovarian cancer deaths. Preoperative imaging plays an important role in assessing the extent of disease and guides the next step in surgical decision-making and operative planning. In this article, we will review the multimodality imaging features of various subtypes of EOC. We will also discuss the role of imaging in the staging, management, and surveillance of EOC.

摘要

上皮性卵巢癌(EOC)是最常见的妇科恶性肿瘤,占卵巢癌死亡人数的 70%以上。术前影像学在评估疾病范围和指导手术决策和手术计划的下一步方面起着重要作用。本文将回顾各种 EOC 亚型的多模态影像学特征。我们还将讨论影像学在 EOC 的分期、治疗和监测中的作用。

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Two case reports of ovarian clear cell carcinoma: Atypical MRI findings with a renal corticomedullary contrast-like appearance.两例卵巢透明细胞癌病例报告:具有类似肾皮质髓质强化表现的非典型MRI表现。

本文引用的文献

1
Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses.附件包块良恶性诊断中 O-RADS、GI-RADS 和 IOTA 简单规则的比较:恶性率、有效性和可靠性。
Eur Radiol. 2021 Feb;31(2):674-684. doi: 10.1007/s00330-020-07143-7. Epub 2020 Aug 18.
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Does dual-energy CT differentiate benign and malignant ovarian tumours?双能CT能否鉴别卵巢良恶性肿瘤?
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Mutation landscape of germline and somatic BRCA1/2 in patients with high-grade serous ovarian cancer.
Radiol Case Rep. 2025 Mar 8;20(5):2513-2520. doi: 10.1016/j.radcr.2025.01.091. eCollection 2025 May.
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Evaluation of Selected MRI Parameters in the Differentiation of Mucinous Ovarian Carcinomas and Metastatic Ovarian Tumors.评估选定MRI参数在黏液性卵巢癌与转移性卵巢肿瘤鉴别诊断中的应用
Cancers (Basel). 2024 Oct 23;16(21):3569. doi: 10.3390/cancers16213569.
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Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model.使用可解释的随机森林模型预测上皮性卵巢癌患者的无进展生存期。
Heliyon. 2024 Jul 26;10(15):e35344. doi: 10.1016/j.heliyon.2024.e35344. eCollection 2024 Aug 15.
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Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study.基于磁共振成像的影像组学分析在卵巢透明细胞癌与子宫内膜样癌鉴别诊断中的应用:一项回顾性研究。
Jpn J Radiol. 2024 Jul;42(7):731-743. doi: 10.1007/s11604-024-01545-z. Epub 2024 Mar 12.
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Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images.基于深度学习的T2加权磁共振图像上皮性卵巢癌分割
Quant Imaging Med Surg. 2023 Mar 1;13(3):1464-1477. doi: 10.21037/qims-22-494. Epub 2023 Feb 9.
8
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Cancers (Basel). 2023 Feb 24;15(5):1453. doi: 10.3390/cancers15051453.
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Int J Gynecol Cancer. 2020 Jan;30(1):133-138. doi: 10.1136/ijgc-2019-000819. Epub 2019 Nov 21.
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PLoS One. 2019 Aug 22;14(8):e0221340. doi: 10.1371/journal.pone.0221340. eCollection 2019.
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