Assessing the Performance of the PREDICT Breast Version 3.0 Prognostic Tool in Patients With Breast Cancer in the United States.

IF 14.8 2区 医学 Q1 ONCOLOGY
Yi-Wen Hsiao, Gordon C Wishart, Paul D P Pharoah, Pei-Chen Peng
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引用次数: 0

Abstract

Background: PREDICT Breast version 3 (v3) is the latest updated prognostication tool, developed using data from approximately 35,000 women diagnosed with breast cancer between 2000 and 2018 in the United Kingdom. Although an earlier version of PREDICT was tested in the United States, the performance of the latest version remains unknown. This study aims to validate PREDICT Breast v3 using newly released SEER outcome data for patients with breast cancer in the United States and to address potential health disparities.

Methods: A total of 615,865 female patients diagnosed with primary breast cancer between 2000 and 2018 and followed for at least 10 years were selected from the SEER database. Predicted and observed 10- and 15-year breast cancer-specific survival outcomes were compared for the overall cohort, stratified by estrogen receptor (ER) status and predefined subgroups. Discriminatory accuracy was evaluated using the area under the receiver operating characteristic curve (AUC).

Results: PREDICT Breast v3 demonstrated good calibration and discrimination for long-term breast cancer-specific survival. It provided accurate mortality estimates (within ±10% absolute error) across the US population for 10-year (-10% in ER-positive and 2% in ER-negative breast cancer) and 15-year (4% in ER-positive and 3% in ER-negative breast cancer) all-cause mortality, for both ER statuses. The model also showed good performance for 10- and 15-year all-cause mortality across the US population, with AUC values of 0.769 and 0.794 for ER-positive breast cancer as well as AUC of 0.738 and 0.746 for ER-negative breast cancer, respectively, indicating good discriminatory ability. However, recalibration is needed for specific groups, including non-Hispanic Asian and non-Hispanic Black patients with ER-negative disease.

Conclusions: PREDICT v3 accurately predicts 10- and 15-year breast cancer-specific survival in contemporary US patients with breast cancer. Future efforts should focus addressing disparities observed in predictive tools to promote equitable care.

美国乳腺癌患者预后预测工具PREDICT Breast Version 3.0的性能评估
背景:PREDICT Breast version 3 (v3)是最新更新的预测工具,使用2000年至2018年英国约35,000名被诊断患有乳腺癌的女性的数据开发。虽然PREDICT的早期版本在美国进行了测试,但最新版本的性能仍然未知。本研究旨在利用最新发布的美国乳腺癌患者SEER结果数据验证PREDICT Breast v3,并解决潜在的健康差异。方法:从SEER数据库中选择2000年至2018年间诊断为原发性乳腺癌并随访至少10年的女性患者615,865例。根据雌激素受体(ER)状态和预先定义的亚组,对整个队列的预测和观察到的10年和15年乳腺癌特异性生存结果进行比较。采用受试者工作特征曲线下面积(AUC)评价鉴别精度。结果:PREDICT Breast v3对长期乳腺癌特异性生存具有良好的校准和区分能力。它提供了准确的死亡率估计(绝对误差在±10%以内),在美国人群中,10年(雌激素受体阳性乳腺癌为-10%,雌激素受体阴性乳腺癌为2%)和15年(雌激素受体阳性乳腺癌为4%,雌激素受体阴性乳腺癌为3%)的全因死亡率,包括两种雌激素受体状态。该模型对美国人群10年和15年全因死亡率也表现良好,er阳性乳腺癌的AUC值分别为0.769和0.794,er阴性乳腺癌的AUC值分别为0.738和0.746,表明该模型具有良好的区分能力。然而,需要对特定人群进行重新校准,包括er阴性疾病的非西班牙裔亚洲人和非西班牙裔黑人患者。结论:PREDICT v3可以准确预测当代美国乳腺癌患者10年和15年乳腺癌特异性生存率。未来的努力应侧重于解决预测工具中观察到的差异,以促进公平护理。
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
CiteScore
20.20
自引率
0.00%
发文量
388
审稿时长
4-8 weeks
期刊介绍: JNCCN—Journal of the National Comprehensive Cancer Network is a peer-reviewed medical journal read by over 25,000 oncologists and cancer care professionals nationwide. This indexed publication delivers the latest insights into best clinical practices, oncology health services research, and translational medicine. Notably, JNCCN provides updates on the NCCN Clinical Practice Guidelines in Oncology® (NCCN Guidelines®), review articles elaborating on guideline recommendations, health services research, and case reports that spotlight molecular insights in patient care. Guided by its vision, JNCCN seeks to advance the mission of NCCN by serving as the primary resource for information on NCCN Guidelines®, innovation in translational medicine, and scientific studies related to oncology health services research. This encompasses quality care and value, bioethics, comparative and cost effectiveness, public policy, and interventional research on supportive care and survivorship. JNCCN boasts indexing by prominent databases such as MEDLINE/PubMed, Chemical Abstracts, Embase, EmCare, and Scopus, reinforcing its standing as a reputable source for comprehensive information in the field of oncology.
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