Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study

Abstract

Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer.

Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558.

Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.

Funding: No funding.

Keywords: Axillary lymph node metastasis; Breast cancer; Machine learning; Radiomics; Tumor microenvironment.

Fig. 1

Study workflow (a) Radiomic workflow. Multi-sequence MRI images were used for breast tumor and ALN region identification and the delineation of regions of interest, then features were extracted using 3D Slicer software for the signatures construction. (b) The association between MRI radiomics and tumor microenvironment. MRI=magnetic resonance imaging. ALN = axillary lymph node. T1+C=contrast-enhanced T1-weighted imaging. T2WI=T2-weighted imaging. DWI-ADC=diffusion-weighted imaging quantitatively measured the apparent diffusion coefficient. SVM=support vector machine. DC= dendritic cell. MDSC=myeloid-derived suppressor cell. NK=natural killer cell. LncRNA=long non-coding RNA.

Fig. 2

Performance and clinical value of magnetic resonance imaging radiomic signatures (a) Overall distribution of key radiomic features from T1+C, T2WI, and DWI-ADC sequences among patients with and without ALN metastasis in the training cohort (n = 392). (b) Performance of the ALN-tumor radiomic signature for predicting ALN metastasis in the training (n = 392), the prospective-retrospective validation (n = 45), and the external validation (n = 112) cohorts. (c) Performance of the multiomic signature for predicting ALN metastasis in the training (n = 381), the prospective-retrospective validation (n = 30), and the external validation (n = 81) cohorts. (d) Decision curve analysis for the ALN-tumor radiomic signature and the multiomic signature (n = 381). ALN=axillary lymph node. AUC=area under the receiver operating characteristics curve. T1+C=contrast-enhanced T1-weighted imaging. T2WI=T2-weighted imaging. DWI-ADC=diffusion-weighted imaging quantitatively measured the apparent diffusion coefficient.

Fig. 3

Patients with inconsistent MRI-based ALN status and pathology-based ALN status Patient 1 had a pathologic positive ALN status, but the MRI-based status was considered negative by radiologists before surgery. In contrast, the MRI-based status of patient 2 had initially been marked as positive by radiologists, but it was later found to be negative on pathologic examination. The ALN status of two patients was accurately assessed through the multiomic radiomic signature by the cutoff value of 0·334. MRI=magnetic resonance imaging. ALN=axillary lymph node. T1+C=contrast-enhanced T1-weighted imaging.

Fig. 4

Clustering of key radiomic features before and after neoadjuvant chemotherapy The clustering of 180 key radiomic features (a) before neoadjuvant chemotherapy and (b) after neoadjuvant chemotherapy in 45 patients from the prospective-retrospective validation cohort.

Fig. 5

The association between MRI radiomics and tumor microenvironment The correlation between key MRI radiomic features and immune cells, lncRNAs and types of methylated sites in 90 breast cancer patients from The Cancer Genome Atlas and The Cancer Imaging Archive. Correlation analysis was used to estimate the strength of the correlations with Spearman ρ. T1+C=contrast-enhanced T1-weighted imaging. T2WI=T2-weighted imaging. LncRNA=long non-coding RNA. MRI= magnetic resonance imaging.