Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics

Abstract

Background Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection. Purpose To explore the use of dynamic contrast‐enhanced (DCE)‐magnetic resonance imaging (MRI)‐based radiomics for preoperative prediction of LVI in invasive breast cancer. Study Type Prospective. Population Ninety training cohort patients (22 LVI‐positive and 68 LVI‐negative) and 59 validation cohort patients (22 LVI‐positive and 37 LVI‐negative) were enrolled. Field Strength/Sequence 1.5 T and 3.0 T, T1‐weighted DCE‐MRI. Assessment Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE‐MRI. A radiomics signature was constructed in the training cohort with 10‐fold cross‐validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort. Statistical Tests Mann–Whitney U‐test, chi‐square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). Results Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone. Data Conclusion The DCE‐MRI‐based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847–857.

Exosomes deep sequencing identifies miR-363-5p as a non-invasive biomarker of axillary lymph node metastasis and prognosis in breast cancer

“…We performed in silico diagnosis test and veri ed that miR-363-5p alone has an AUC of 0.621-0.958 in predicting LN metastasis in multiple independent datasets. Previous studies have shown that imaging approaches namely axillary ultrasound and MRI have similar performance in ALN staging, and the AUC of MRI alone was 0.665 [27,28]. We deem that exosomal miR-363-5p can help elevate the accuracy of clinical prediction models if taken into consideration.…”

Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer

“…LVI is regarded as an important negative factor in treatment options and prognostication in rectal cancer from multiple statistically published trials. The previous study which was reported by Liu et al found that the DCE-MRIbased radiomic features and LVI status were correlated in breast cancer, and indicated that the radiomic features were effective in predicting the LVI status of patients with invasive breast cancer before surgery [29]. However, it is still a challenge for LVI preoperative prediction in rectal cancer.…”

Value of digital mammography in predicting lymphovascular invasion of breast cancer

“…There have been many reports on prediction of axillary lymph node metastasis [3][4][5] and on digital mammography screening [6][7][8][9]. However, there has been no report on predicting LVI of breast cancer based on imaging patterns of digital mammography, except a little literature in which MRI findings were used for prediction [2,[10][11][12].…”