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19-08-2023 | Breast Cancer | Breast

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

Authors: Yuan Guo, Xiaotong Xie, Wenjie Tang, Siyi Chen, Mingyu Wang, Yaheng Fan, Chuxuan Lin, Wenke Hu, Jing Yang, Jialin Xiang, Kuiming Jiang, Xinhua Wei, Bingsheng Huang, Xinqing Jiang

Published in: European Radiology | Issue 2/2024

Abstract

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.

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Title: Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer
Authors: Yuan Guo
Xiaotong Xie
Wenjie Tang
Siyi Chen
Mingyu Wang
Yaheng Fan
Chuxuan Lin
Wenke Hu
Jing Yang
Jialin Xiang
Kuiming Jiang
Xinhua Wei
Bingsheng Huang
Xinqing Jiang
Publication date: 19-08-2023
Publisher: Springer Berlin Heidelberg
Keywords: Breast Cancer
Breast Cancer
Published in: European Radiology / Issue 2/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-09990-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Methods

Results

Conclusions

Clinical relevance statement

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

Clinical relevance statement

Key Points

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