MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study

被引:6
|
作者
Ma, Jiafan [1 ,2 ]
Chen, Kai [1 ,2 ,3 ]
Li, Shunrong [1 ,2 ]
Zhu, Liling [1 ,2 ]
Yu, Yunfang [4 ]
Li, Jingwu [5 ]
Ma, Jie [5 ]
Ouyang, Jie [6 ]
Wu, Zhuo [3 ,7 ]
Tan, Yujie [4 ]
He, Zifan [4 ]
Liu, Haiqing [7 ]
Pan, Zhilong [1 ,2 ]
Li, Haojiang [8 ]
Liu, Qiang [1 ,2 ]
Song, Erwei [1 ,2 ]
机构
[1] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Guangdong Prov Key Lab Malignant Tumor Epigenet &, Guangzhou 510120, Peoples R China
[2] Sun Yat Sen Univ, Yat Sen Breast Tumor Hosp, Sun Yat Sen Mem Hosp, Breast Tumor Ctr, Guangzhou 510120, Guangdong, Peoples R China
[3] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Artificial Intelligence Lab, Guangzhou 510120, Guangdong, Peoples R China
[4] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Med Oncol, Guangzhou 510120, Peoples R China
[5] Tangshan Peoples Hosp, Dept Breast Surg, Tangshan 063001, Hebei, Peoples R China
[6] Sun Yat Sen Univ, Tungwah Hosp, Dept Breast Surg, Dongguan 523413, Peoples R China
[7] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Radiol, Guangzhou 510120, Guangdong, Peoples R China
[8] Sun Yat Sen Univ, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Collaborat Innovat Ctr Canc Med,Canc Ctr, State Key Lab Oncol South China,Dept Radiol, 651 Dongfeng Rd East, Guangzhou 510060, Guangdong, Peoples R China
关键词
Breast neoplasms; General surgery; Magnetic resonance imaging; Tumor microenvironment; Machine learning; POSITIVE RESECTION MARGINS; CORE-NEEDLE-BIOPSY; 20-YEAR FOLLOW-UP; AMERICAN SOCIETY; NEOADJUVANT CHEMOTHERAPY; MESENCHYMAL TRANSITION; LOCAL RECURRENCE; IRRADIATION; LUMPECTOMY; GUIDELINE;
D O I
10.1007/s00330-023-10144-x
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objectives Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. Methods We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. Results The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. Conclusions This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status.
引用
收藏
页码:1774 / 1789
页数:16
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