Nomograms for predicting recurrence of HER2-positive breast cancer with different HR status based on ultrasound and clinicopathological characteristics

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-09-09 DOI:10.1002/cam4.70146

Xudong Zhang, Hanqing Kong, Xiaoxue Liu, Qingxiang Li, Xinran Fang, Junjia Wang, Zihao Qin, Nana Hu, Jiawei Tian, Hao Cui, Lei Zhang

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Abstract

Purpose

This study aimed to identify ultrasound and clinicopathological characteristics related to recurrence in HER2-positive (HER2+) breast cancer, and to develop nomograms for predicting recurrence.

Methods

In this dual-center study, we retrospectively enrolled 570 patients with HER2+ breast cancer. The ultrasound and clinicopathological characteristics of hormone receptor (HR)−/HER2+ patients and HR+/HER2+ patients were analyzed separately according to HR status. Eighty percent of the original samples from HR−/HER2+ and HR+/HER2+ patients were extracted by bootstrap sampling as the training cohorts, while the remaining 20% were used as the external validation cohorts. Informative characteristics were screened through univariate and multivariable Cox regression in the training cohorts and used to develop nomograms for predicting recurrence. The predictive accuracy was calculated using Harrell's C-index and calibration curves.

Results

Three informative characteristics (axillary nodal status, calcification, and Adler degree) were identified in HR−/HER2+ patients, and another three (histological grade, axillary nodal status, and echogenic halo) in HR+/HER2+ patients. Based on these, two separate nomograms were constructed to assess recurrence risk. In the training cohorts, the C-index was 0.740 (95% CI: 0.667–0.811) for HR−/HER2+ nomogram, and 0.749 (95% CI: 0.679–0.820) for HR+/HER2+ nomogram. In the validation cohorts, the C-index was 0.708 (95% CI: 0.540–0.877) for HR−/HER2+ group, and 0.705 (95% CI: 0.557–0.853) for HR+/HER2+ group. The calibration curves also indicated the excellent accuracy of the nomograms.

Conclusions

Ultrasound performance of HER2+ breast cancers with different HR status was significantly different. Nomograms integrating ultrasound and clinicopathological characteristics exhibited favorable performance and have the potential to serve as a reliable method for predicting recurrence in heterogeneous breast cancer.

Abstract Image

查看原文本刊更多论文

基于超声和临床病理特征预测不同 HR 状态的 HER2 阳性乳腺癌复发的提名图。

目的：本研究旨在确定与HER2阳性（HER2+）乳腺癌复发相关的超声和临床病理特征，并制定预测复发的提名图：在这项双中心研究中，我们回顾性地纳入了570名HER2+乳腺癌患者。根据HR状态分别分析了激素受体（HR）-/HER2+患者和HR+/HER2+患者的超声和临床病理特征。通过引导取样法提取了80%的HR-/HER2+和HR+/HER2+患者原始样本作为训练队列，其余20%作为外部验证队列。在训练队列中通过单变量和多变量 Cox 回归筛选出有参考价值的特征，并用于制定预测复发的提名图。使用 Harrell's C 指数和校准曲线计算预测准确性：结果：在HR-/HER2+患者中发现了三个信息特征（腋窝结节状态、钙化和阿德勒程度），在HR+/HER2+患者中发现了另外三个信息特征（组织学分级、腋窝结节状态和回声晕）。在此基础上，构建了两个独立的提名图来评估复发风险。在训练队列中，HR-/HER2+提名图的C指数为0.740（95% CI：0.667-0.811），HR+/HER2+提名图的C指数为0.749（95% CI：0.679-0.820）。在验证队列中，HR-/HER2+ 组的 C 指数为 0.708（95% CI：0.540-0.877），HR+/HER2+ 组的 C 指数为 0.705（95% CI：0.557-0.853）。校准曲线也表明提名图非常准确：结论：不同HR状态的HER2+乳腺癌的超声表现存在显著差异。综合超声和临床病理特征的提名图表现出良好的性能，有望成为预测异质性乳腺癌复发的可靠方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.