Pathologic complete response prediction in breast cancer lesion segmentation and neoadjuvant therapy

Yue Liu; Zhihong Chen; Junhao Chen; Zhenwei Shi; Gang Fang

doi:10.3389/fmed.2023.1188207

Pathologic complete response prediction in breast cancer lesion segmentation and neoadjuvant therapy

Front Med (Lausanne). 2023 Dec 4:10:1188207. doi: 10.3389/fmed.2023.1188207. eCollection 2023.

Authors

Yue Liu^#^{1

2}, Zhihong Chen^#^{1

3}, Junhao Chen¹, Zhenwei Shi^{3

4

5}, Gang Fang¹

Affiliations

¹ Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.
² School of Information Engineering, Jiangxi College of Applied Technology, Ganzhou, China.
³ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
⁴ Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
⁵ Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

^# Contributed equally.

Abstract

Objectives: Predicting whether axillary lymph nodes could achieve pathologic Complete Response (pCR) after breast cancer patients receive neoadjuvant chemotherapy helps make a quick follow-up treatment plan. This paper presents a novel method to achieve this prediction with the most effective medical imaging method, Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI).

Methods: In order to get an accurate prediction, we first proposed a two-step lesion segmentation method to extract the breast cancer lesion region from DCE-MRI images. With the segmented breast cancer lesion region, we then used a multi-modal fusion model to predict the probability of axillary lymph nodes achieving pCR.

Results: We collected 361 breast cancer samples from two hospitals to train and test the proposed segmentation model and the multi-modal fusion model. Both segmentation and prediction models obtained high accuracy.

Conclusion: The results show that our method is effective in both the segmentation task and the pCR prediction task. It suggests that the presented methods, especially the multi-modal fusion model, can be used for the prediction of treatment response in breast cancer, given data from noninvasive methods only.

Keywords: DCE-MRI; PCR; breast cancer; lesion segmentation; multi-modal fusion model; neoadjuvant chemotherapy.