Risk of endometrial malignancy in women treated for breast cancer: the BLUSH prediction model - evidence from a comprehensive multicentric retrospective cohort study.

IF 2.9 4区 医学 Q1 OBSTETRICS & GYNECOLOGY Climacteric Pub Date : 2024-10-01 Epub Date: 2024-07-18 DOI:10.1080/13697137.2024.2376189
Salvatore Giovanni Vitale, Stefano Angioni, Maurizio Nicola D'Alterio, Carlo Ronsini, Stefania Saponara, Pasquale De Franciscis, Gaetano Riemma
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Abstract

Objective: This study aimed to evaluate characteristics of endometrial surveillance in women treated for breast cancer to build a clinical prediction model.

Design: A multicentric retrospective cohort study was conducted at two tertiary-care university hospitals from January 2020 to June 2023. Perimenopausal and postmenopausal women treated for breast cancer were categorized into two groups: patients with and without diagnosis of endometrial malignancy (endometrial carcinoma) or premalignancy (atypical endometrial hyperplasia). Characteristics of breast cancer and ultrasonographic and hysteroscopic examinations were compared. A prediction model for endometrial malignancy was built using logistic regression. Predictive accuracy was assessed using the receiver operating characteristic (ROC) curve and goodness of fit using the Hosmer-Lemeshow test.

Results: One hundred and thirty-two (28 with premalignancy or malignancy and 104 without malignancy) women were analyzed. A nomogram was produced for prediction model development utilizing the presence and duration in months of abnormal uterine (BL)eeding, ultrasound (US) vascular pattern and echogenicity and (H)ysteroscopic appearance of endometrium (BLUSH) as determined by logistic regression. Sensitivity and specificity were 79.17% and 95.19%, respectively, with an area under ROC curve of 0.965, indicating good accuracy. Good goodness of fit and prediction stability were indicated by the calibration curve and Hosmer-Lemeshow test (χ2 = 26.36; p = 0.999).

Conclusions: Breast cancer survivors undergoing endometrial surveillance might benefit from a potentially useful prediction model based on hysteroscopic appearance, ultrasonographic uniformity of endometrium, Doppler flow and presence of abnormal uterine bleeding.

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接受过乳腺癌治疗的妇女发生子宫内膜恶性肿瘤的风险:BLUSH 预测模型--来自一项综合性多中心回顾性队列研究的证据。
研究目的本研究旨在评估接受乳腺癌治疗妇女的子宫内膜监测特征,以建立临床预测模型:设计:2020 年 1 月至 2023 年 6 月,在两家三级甲等大学医院开展了一项多中心回顾性队列研究。将接受乳腺癌治疗的围绝经期和绝经后妇女分为两组:诊断为子宫内膜恶性肿瘤(子宫内膜癌)或恶性肿瘤前期(非典型子宫内膜增生)的患者和未诊断为子宫内膜恶性肿瘤(子宫内膜癌)或恶性肿瘤前期(非典型子宫内膜增生)的患者。对乳腺癌的特征以及超声波检查和宫腔镜检查进行了比较。利用逻辑回归建立了子宫内膜恶性肿瘤的预测模型。预测准确性采用接收器操作特征曲线(ROC)进行评估,拟合优度采用Hosmer-Lemeshow检验:分析了 132 名妇女(28 名患有恶性肿瘤或恶性肿瘤前期,104 名无恶性肿瘤)。利用逻辑回归确定的异常子宫(BL)出血的存在和持续时间(以月计)、超声(US)血管形态和回声以及子宫内膜(BLUSH)的(H)宫腔镜外观,制作了一个提名图,用于建立预测模型。灵敏度和特异性分别为 79.17% 和 95.19%,ROC 曲线下面积为 0.965,表明准确性良好。校准曲线和 Hosmer-Lemeshow 检验(χ2 = 26.36; p = 0.999)表明拟合度和预测稳定性良好:接受子宫内膜监测的乳腺癌幸存者可能会受益于一个基于宫腔镜外观、子宫内膜超声均匀性、多普勒血流和异常子宫出血的潜在有用预测模型。
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来源期刊
Climacteric
Climacteric 医学-妇产科学
CiteScore
1.70
自引率
7.10%
发文量
53
审稿时长
1 months
期刊介绍: Climacteric is the official journal of the International Menopause Society (IMS). As an international peer-reviewed journal it publishes original research and reviews of all aspects of aging in women. Climacteric was founded by the IMS in 1998 and today has become a leading journal in the publication of peer-reviewed papers on the menopause, climacteric and mid-life health. Topics covered include endocrine changes, symptoms attributed to the menopause and their treatment, hormone replacement and alternative therapies, lifestyles, and the counselling and education of peri- and postmenopausal women. Climacteric, published bimonthly, also features regular invited reviews, editorials and commentaries on recent developments. The editorial review board of Climacteric includes leading scientific and clinical experts in the field of midlife medicine and research and is headed by its Editor-in-Chief, Professor Rod Baber of Australia. He and his team of Associate Editors act independently to set a clear editorial policy, co-ordinate peer review, and ensure a rapid response to submitted papers.
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