Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Stratipath AB, Solna, Sweden.
Breast Cancer Res. 2024 Aug 14;26(1):123. doi: 10.1186/s13058-024-01879-6.
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.
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.
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.
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.
Stratipath Breast 是一种基于 CE-IVD 的人工智能解决方案，用于将乳腺癌患者的预后风险分为高风险和低风险组，使用苏木精和伊红（H&E）染色的组织学全切片图像（WSI）。在这项验证研究中，我们在两个独立的乳腺癌队列中评估了 Stratipath Breast 的预后性能。
这是一项回顾性多中心验证研究，纳入了来自瑞典两家医院的 2719 名原发性乳腺癌患者。Stratipath Breast 工具应用于基于手术切除肿瘤的诊断性 H&E 染色组织切片的数字化 WSI 对患者进行分层。预后性能通过多变量 Cox 比例风险分析进行时间事件分析，以无进展生存期（PFS）作为主要终点。
在具有临床意义的雌激素受体（ER）阳性/人表皮生长因子受体 2（HER2）阴性患者亚组中，在调整了既定风险因素后，低风险组和高风险组之间与 PFS 相关的估计风险比（HR）为 2.76（95%CI：1.63-4.66，p 值<0.001）。在 ER+/HER2-诺丁汉组织学分级（NHG）2 亚组中，低风险组和高风险组之间的 HR 为 2.20（95%CI：1.22-3.98，p 值=0.009）。
结果表明 Stratipath Breast 在所有乳腺癌患者中具有独立的预后价值，在具有临床意义的 ER+/HER2-亚组和 NHG2/ER+/HER2-亚组中也是如此。改善中间风险 ER+/HER2-乳腺癌的风险分层为辅助化疗的治疗决策提供了相关信息，并有可能减少过度治疗和治疗不足。与分子诊断相比，基于图像的风险分层具有较短的前置时间和显著降低的成本的额外优势，因此有可能惠及更广泛的患者群体。
