Suppr超能文献

用于检测子宫平滑肌肉瘤的生物标志物和临床评分的验证:一项伴有高级别子宫平滑肌肉瘤更新情况的病例对照研究

Validation of biomarkers and clinical scores for the detection of uterine leiomyosarcoma: a case-control study with an update of pLMS.

作者信息

Vollmer Marcus, Köhler Günter, Radosa Julia Caroline, Zygmunt Marek, Zimmermann Julia, Köller Martina, Seitz Christine, Bralo Helena, Radosa Marc Philipp, Kaya Askin Cangül, Krichbaum Johann, Solomayer Erich-Franz, Kaderali Lars, Alwafai Zaher

机构信息

Institute of Bioinformatics, University Medicine Greifswald, Felix-Hausdorff-Str. 8, Greifswald, 17475, Germany.

Department of Obstetrics and Gynecology, University Medicine Greifswald, Sauerbruchstr., Greifswald, 17475, Germany.

出版信息

BMC Cancer. 2025 Jan 8;25(1):33. doi: 10.1186/s12885-024-13396-y.

Abstract

BACKGROUND

The diagnosis of rare uterine leiomyosarcoma (uLMS) remains a challenge given the high incidence rates of benign uterine tumors such as leiomyoma (LM). In the last decade, several clinical scores and blood serum markers have been proposed. The aim of this study is to validate and update the pLMS clinical scoring system, evaluating the accuracy of the scoring system by Zhang et al. and examining the discriminatory ability of blood markers such as serum lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR).

METHODS

In a case-control study, 90 new uLMS from the DKSM consultation registry and 659 prospectively recruited LM cases from the Saarland University Hospital were used for validation. Welch's t-test and Hedges' g were used to evaluate blood markers and optimal thresholds and diagnostic odds ratios were calculated. Scoring systems were compared using receiver operating characteristics and proposed diagnostic cut-offs were reviewed. Missing values were imputed by random forest imputation to create the updated scoring system 'pLMS2' using penalized logistic regression based on the pooled data sets of 384 uLMS and 1485 LM.

RESULTS

pLMS achieved an AUC of 0.97 on the validation data, but sensitivity and specificity varied at the proposed thresholds due to a shift in the score distributions. 43 uLMS and 578 LM were included in the comparison of pLMS with Zhang's scoring system, with pLMS being superior (AUC 0.960 vs 0.845). LDH, NLR, and PLR achieved a diagnostic odds ratios of 18.03, 8.64 and 4.81, respectively. pLMS2 is based on subscores for menopausal status interacting with age, tumor diameter, intermenstrual bleeding, hypermenorrhea, dysmenorrhea, postmenstrual bleeding, rapid tumor growth, and suspicious sonography.

CONCLUSIONS

Validation of the pLMS shows stable discriminatory ability as expressed by AUC, although caution should be taken with cut-off values, as sensitivity and specificity may vary. Data collection of the updated clinical score pLMS2 remains simple and convenient, with no additional cost. The proposed thresholds of 1.5 and 5.5 can be used as a guide to avoid unnecessary or inappropriate surgery and to make the use of further diagnostic measures cost-effective. LDH, NLR and PLR provide further evidence to differentiate uLMS from LM in conjunction with clinical data.

摘要

背景

鉴于子宫肌瘤(LM)等良性子宫肿瘤的高发病率,罕见子宫平滑肌肉瘤(uLMS)的诊断仍然是一项挑战。在过去十年中,已经提出了几种临床评分和血清标志物。本研究的目的是验证和更新pLMS临床评分系统,评估Zhang等人的评分系统的准确性,并检验血清乳酸脱氢酶(LDH)、中性粒细胞与淋巴细胞比率(NLR)和血小板与淋巴细胞比率(PLR)等血液标志物的鉴别能力。

方法

在一项病例对照研究中,使用了来自DKSM咨询登记处的90例新uLMS病例和来自萨尔州大学医院前瞻性招募的659例LM病例进行验证。采用Welch's t检验和Hedges' g评估血液标志物和最佳阈值,并计算诊断比值比。使用受试者工作特征曲线比较评分系统,并对提出的诊断临界值进行审查。通过随机森林插补法填补缺失值,基于384例uLMS和1485例LM的汇总数据集,使用惩罚逻辑回归创建更新后的评分系统“pLMS2”。

结果

pLMS在验证数据上的AUC为0.97,但由于评分分布的变化,在所提出的阈值下敏感性和特异性有所不同。在将pLMS与Zhang的评分系统进行比较时,纳入了43例uLMS和578例LM病例,pLMS更具优势(AUC分别为0.960和0.845)。LDH、NLR和PLR的诊断比值比分别为18.03、8.64和4.81。pLMS2基于绝经状态与年龄、肿瘤直径、经间期出血、月经过多、痛经、经后出血、肿瘤快速生长和可疑超声检查的子评分。

结论

pLMS的验证显示,AUC所表示的鉴别能力稳定,尽管在临界值方面应谨慎,因为敏感性和特异性可能会有所不同。更新后的临床评分pLMS2的数据收集仍然简单方便,无需额外费用。所提出的1.5和5.5的阈值可作为指导,以避免不必要或不适当的手术,并使进一步诊断措施的使用具有成本效益。LDH、NLR和PLR结合临床数据为区分uLMS和LM提供了进一步的证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78fe/11708173/4abd936dd514/12885_2024_13396_Fig1_HTML.jpg

相似文献

3
Benign uterine mass-discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study.
Arch Gynecol Obstet. 2019 Dec;300(6):1719-1727. doi: 10.1007/s00404-019-05344-0. Epub 2019 Nov 1.
4
Neutrophil to lymphocyte ratio for preoperative diagnosis of uterine sarcomas: a case-matched comparison.
Eur J Surg Oncol. 2010 Jul;36(7):691-8. doi: 10.1016/j.ejso.2010.05.004. Epub 2010 Jun 8.
5
Diagnostic value of combination serum assay of lactate dehydrogenase, D-dimer, and C-reactive protein for uterine leiomyosarcoma.
J Obstet Gynaecol Res. 2019 Jan;45(1):189-194. doi: 10.1111/jog.13792. Epub 2018 Aug 27.
7
Preoperative Blood Inflammatory Markers for the Differentiation of Uterine Leiomyosarcoma from Leiomyoma.
Cancer Manag Res. 2021 Jun 24;13:5001-5011. doi: 10.2147/CMAR.S314219. eCollection 2021.
8
Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma.
Cancer Sci. 2019 Dec;110(12):3718-3726. doi: 10.1111/cas.14215. Epub 2019 Nov 16.
10
The Role of Endometrial Biopsy in the Preoperative Detection of Uterine Leiomyosarcoma.
J Minim Invasive Gynecol. 2016 May-Jun;23(4):567-72. doi: 10.1016/j.jmig.2016.01.022. Epub 2016 Feb 4.

本文引用的文献

2
MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma.
Abdom Radiol (NY). 2024 May;49(5):1522-1533. doi: 10.1007/s00261-024-04198-8. Epub 2024 Mar 11.
3
Management of Uterine Fibroids and Sarcomas: The Palermo Position Paper.
Gynecol Obstet Invest. 2024;89(2):73-86. doi: 10.1159/000537730. Epub 2024 Feb 21.
4
Molecular-Based Immunohistochemical Algorithm for Uterine Leiomyosarcoma Diagnosis.
Mod Pathol. 2023 Apr;36(4):100084. doi: 10.1016/j.modpat.2022.100084. Epub 2023 Jan 10.
9
Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review.
Artif Intell Med. 2021 Oct;120:102164. doi: 10.1016/j.artmed.2021.102164. Epub 2021 Sep 3.
10
Preoperative Blood Inflammatory Markers for the Differentiation of Uterine Leiomyosarcoma from Leiomyoma.
Cancer Manag Res. 2021 Jun 24;13:5001-5011. doi: 10.2147/CMAR.S314219. eCollection 2021.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验