Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography

• Published in: European radiology Vol. 32; no. 5; pp. 3207 - 3219
• Main Authors: Mao, Ning, Shi, Yinghong, Lian, Chun, Wang, Zhongyi, Zhang, Kun, Xie, Haizhu, Zhang, Haicheng, Chen, Qianqian, Cheng, Guanxun, Xu, Cong, Dai, Yi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-05-2022; Springer Nature B.V
• Subjects: Breast; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - drug therapy; Breast Neoplasms - surgery; Calibration; Chemotherapy; Decision analysis; Diagnostic Radiology; Feature extraction; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Mammography; Mammography - methods; Mathematical analysis; Medicine; Medicine & Public Health; Neoadjuvant Therapy; Neuroradiology; Patients; Performance prediction; Radiology; Radiomics; Retrospective Studies; Signatures; Test sets; Ultrasound; Neoadjuvant therapy; Mammography; Breast Neoplasms; Machine Learning
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-08414-7

Objective
To investigative the performance of intratumoral and peritumoral radiomics based on contrast-enhanced spectral mammography (CESM) to preoperatively predict the effect of the neoadjuvant chemotherapy (NAC) of breast cancers.
Materials and methods
A total of 118 patients with breast cancer who underwent preoperative CESM and NAC from July 2017 to June 2020 were retrospectively analyzed, and the patients were grouped into training (
n 
= 81) and test sets (
n 
= 37) according to the CESM examination time. NAC effect for each patient was assessed by pathology. Intratumoral and peritumoral radiomics features were extracted from CESM images, and feature selection was performed through the Mann–Whitney
U
test and least absolute shrinkage and selection operator regression (LASSO). Five radiomics signatures based on intratumoral regions, 5-mm peritumoral regions, 10-mm peritumoral regions, intratumoral regions + 5-mm peritumoral regions, and intratumoral regions + 10-mm peritumoral regions were calculated through a linear combination of selected features weighted by their respective coefficients. The prediction performance of radiomics signatures was assessed by the area under the receiver operator characteristic (ROC) curve, the precision-recall (P-R) curve, the calibration curve, and decision curve analysis (DCA).
Results
Ten radiomics features were selected to establish the radiomics signature of intratumoral regions + 5-mm peritumoral regions, which yielded a maximum AUC of 0.85 (95% CI, 0.72–0.98) in the test set. The calibration curves, P-R curves, and DCA showed favorable predictive performance of the five radiomics signatures.
Conclusion
The intratumoral and peritumoral radiomics based on CESM exhibited potential for predicting the NAC effect in breast cancer, which could guide treatment decisions.
Key Points
•
The intratumoral and peritumoral CESM-based radiomics signatures show good performance in predicting the NAC effect in breast cancer.