利用常规数字组织病理学图像对基于人工智能的乳腺癌风险分层解决方案进行验证。

IF 7.4 1区 医学 Q1 Medicine
Abhinav Sharma, Sandy Kang Lövgren, Kajsa Ledesma Eriksson, Yinxi Wang, Stephanie Robertson, Johan Hartman, Mattias Rantalainen
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引用次数: 0

摘要

背景介绍Stratipath Breast是一种具有CE-IVD标志的人工智能解决方案,它利用血色素和伊红(H&E)染色的组织病理学全切片图像(WSI)将乳腺癌患者分为高危和低危两组。在这项验证研究中,我们评估了 Stratipath Breast 在两个独立乳腺癌队列中的预后性能:这项多地点回顾性验证研究包括来自瑞典两家医院的 2719 名原发性乳腺癌患者。根据手术切除肿瘤的 H&E 染色诊断组织切片的数字化 WSI,应用 Stratipath Breast 工具对患者进行分层。以无进展生存期(PFS)为主要终点,通过多变量考克斯比例危害分析(Cox Proportional Hazards Analysis)对预后效果进行了评估:结果:在临床相关的雌激素受体(ER)阳性/人表皮生长因子受体2(HER2)阴性患者亚组中,低危组和高危组之间与PFS相关的估计危险比(HR)为2.76(95% CI:1.63-4.66,P值 结论:结果表明,雌激素受体(ER)阳性/人表皮生长因子受体2(HER2)阴性患者亚组的预后具有独立性:结果表明,Stratipath Breast 对所有乳腺癌患者以及临床相关的 ER+/HER2- 亚组和 NHG2/ER+/HER2- 亚组具有独立的预后价值。中危ER+/HER2-乳腺癌风险分层的改进为辅助化疗的治疗决策提供了相关信息,并有可能减少治疗不足和治疗过度。与分子诊断相比,基于图像的风险分层具有准备时间短、成本低的额外优势,因此有可能惠及更广泛的患者群体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images.

Background: Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.

Methods: This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.

Results: In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups.

Conclusion: The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
76
审稿时长
12 weeks
期刊介绍: Breast Cancer Research, an international, peer-reviewed online journal, publishes original research, reviews, editorials, and reports. It features open-access research articles of exceptional interest across all areas of biology and medicine relevant to breast cancer. This includes normal mammary gland biology, with a special emphasis on the genetic, biochemical, and cellular basis of breast cancer. In addition to basic research, the journal covers preclinical, translational, and clinical studies with a biological basis, including Phase I and Phase II trials.
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