Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer,European Radiology

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Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer
European Radiology ( IF 4.7 ) Pub Date : 2023-08-19 , DOI: 10.1007/s00330-023-09990-6
Yuan Guo ₁ , Xiaotong Xie _{2,

3} , Wenjie Tang ₁ , Siyi Chen ₁ , Mingyu Wang ₃ , Yaheng Fan ₃ , Chuxuan Lin ₃ , Wenke Hu ₁ , Jing Yang ₄ , Jialin Xiang ₅ , Kuiming Jiang ₅ , Xinhua Wei ₁ , Bingsheng Huang ₃ , Xinqing Jiang ₁

Affiliation

Objective

This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.

Methods

A total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately. Then, the DLR model was constructed to assess the HER2 status by averaging the output probabilities of the two models. Finally, a Kaplan‒Meier survival analysis was conducted to explore the disease-free survival (DFS) in patients with HER2-low-positive status. The multivariate Cox proportional hazard model was constructed to further determine the factors associated with DFS.

Results

First, the DLR model distinguished between HER2-negative and HER2-overexpressing patients with AUCs of 0.868 and 0.763 in the training and validation cohorts, respectively. Furthermore, the DLR model distinguished between HER2-low-positive and HER2-zero patients with AUCs of 0.855 and 0.750, respectively. Cox regression analysis showed that the prediction score obtained using the DLR model (HR, 0.175; p = 0.024) and lesion size (HR, 1.043; p = 0.009) were significant, independent predictors of DFS.

Conclusions

We successfully constructed a DLR model based on MRI to noninvasively evaluate the HER2 status and further revealed prospects for predicting the DFS of patients with HER2-low-positive status.

Clinical relevance statement

The MRI-based DLR model could noninvasively identify HER2-low-positive status, which is considered a novel prognostic predictor and therapeutic target.

Key Points

• The DLR model effectively distinguished the HER2 status of breast cancer patients, especially the HER2-low-positive status.

• The DLR model was better than the traditional radiomics model or DSFR model in distinguishing HER2 expression.

• The prediction score obtained using the model and lesion size were significant independent predictors of DFS.

中文翻译：

通过基于 MRI 的深度学习放射组学无创识别 HER2 低阳性状态可预测乳腺癌患者的无病生存期

客观的

本研究旨在建立基于MRI的深度学习放射组学（DLR）特征来预测人表皮生长因子受体2（HER2）低阳性状态，并进一步验证DLR模型的预后差异。

方法

回顾性招募来自两个机构的 481 名接受术前 MRI 的乳腺癌患者。从分割的肿瘤中提取传统的放射组学特征和基于深度语义分割特征的放射组学（DSFR）特征来分别构建模型。然后，构建 DLR 模型，通过平均两个模型的输出概率来评估 HER2 状态。最后，进行了 Kaplan-Meier 生存分析，以探讨 HER2 低阳性状态患者的无病生存 (DFS)。构建多变量Cox比例风险模型以进一步确定与DFS相关的因素。

结果

首先，DLR 模型区分了 HER2 阴性和 HER2 过表达患者，训练组和验证组中的 AUC 分别为 0.868 和 0.763。此外，DLR 模型区分 HER2 低阳性和 HER2 零患者的 AUC 分别为 0.855 和 0.750。 Cox 回归分析显示，使用 DLR 模型获得的预测评分（HR，0.175； p = 0.024）和病变大小（HR，1.043； p = 0.009）是 DFS 的显着且独立的预测因子。