Ruimei Chai1, He Ma2, Mingjie Xu2, Dooman Arefan3, Xiaoyu Cui2, Yi Liu1, Lina Zhang1, Shandong Wu3, Ke Xu1. 1. Department of Radiology, First Hospital of China Medical University, Shenyang, Liaoning Province, China. 2. Sino-Dutch Biomedical and Infornation Engineering School, Northeastern University, Shenyang, Liaoning Province, China. 3. Imaging Research Division, Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Abstract
BACKGROUND: The axillary lymph node status is critical for breast cancer staging and individualized treatment planning. PURPOSE: To assess the effect of determining axillary lymph node (ALN) metastasis by breast MRI-derived radiomic signatures, and compare the discriminating abilities of different MR sequences. STUDY TYPE: Retrospective. POPULATION: In all, 120 breast cancer patients, 59 with ALN metastasis and 61 without metastasis, all confirmed by pathology. FIELD STRENGTH/SEQUENCE: 3 .0T scanner with T1 -weighted imaging, T2 -weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced (DCE) sequences. ASSESSMENT: Typical morphological and texture features of the segmented tumor were extracted from four sequences, ie, T1 WI, T2 WI, DWI, and the second postcontrast phase (CE2) of the dynamic contrast-enhanced sequences. Additional contrast enhancement kinetic features were extracted from all DCE sequences (one pre- and seven postcontrast phases). Linear discriminant analysis classifiers were built and compared when using features from an individual sequence or the combination of the sequences in differentiating the ALN metastasis status. STATISTICAL TESTS: Mann-Whitney U-test, Fisher's exact test, least absolute shrinkage selection operator (LASSO) regression, and receiver operating characteristic analysis were performed. RESULTS: The accuracy/AUC of the four sequences was 79%/0.87, 77%/0.85, 74%/0.79, and 79%/0.85 for the T1 WI, CE2, T2 WI, and DWI, respectively. When CE2 was augmented by adding kinetic features, the model achieved the highest performance (accuracy = 0.86 and AUC = 0.91). When all features from the four sequences and the kinetics were combined, it did not lead to a further increase in the performance (P = 0.48). DATA CONCLUSION: Breast tumor's radiomic signatures from preoperative breast MRI sequences are associated with the ALN metastasis status, where CE2 phase and the contrast enhancement kinetic features lead to the highest classification effect. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:1125-1132.
BACKGROUND: The axillary lymph node status is critical for breast cancer staging and individualized treatment planning. PURPOSE: To assess the effect of determining axillary lymph node (ALN) metastasis by breast MRI-derived radiomic signatures, and compare the discriminating abilities of different MR sequences. STUDY TYPE: Retrospective. POPULATION: In all, 120 breast cancerpatients, 59 with ALN metastasis and 61 without metastasis, all confirmed by pathology. FIELD STRENGTH/SEQUENCE: 3 .0T scanner with T1 -weighted imaging, T2 -weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced (DCE) sequences. ASSESSMENT: Typical morphological and texture features of the segmented tumor were extracted from four sequences, ie, T1 WI, T2 WI, DWI, and the second postcontrast phase (CE2) of the dynamic contrast-enhanced sequences. Additional contrast enhancement kinetic features were extracted from all DCE sequences (one pre- and seven postcontrast phases). Linear discriminant analysis classifiers were built and compared when using features from an individual sequence or the combination of the sequences in differentiating the ALN metastasis status. STATISTICAL TESTS: Mann-Whitney U-test, Fisher's exact test, least absolute shrinkage selection operator (LASSO) regression, and receiver operating characteristic analysis were performed. RESULTS: The accuracy/AUC of the four sequences was 79%/0.87, 77%/0.85, 74%/0.79, and 79%/0.85 for the T1 WI, CE2, T2 WI, and DWI, respectively. When CE2 was augmented by adding kinetic features, the model achieved the highest performance (accuracy = 0.86 and AUC = 0.91). When all features from the four sequences and the kinetics were combined, it did not lead to a further increase in the performance (P = 0.48). DATA CONCLUSION:Breast tumor's radiomic signatures from preoperative breast MRI sequences are associated with the ALN metastasis status, where CE2 phase and the contrast enhancement kinetic features lead to the highest classification effect. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:1125-1132.
Authors: Simon J Doran; Santosh Kumar; Matthew Orton; James d'Arcy; Fenna Kwaks; Elizabeth O'Flynn; Zaki Ahmed; Kate Downey; Mitch Dowsett; Nicholas Turner; Christina Messiou; Dow-Mu Koh Journal: Cancer Imaging Date: 2021-05-20 Impact factor: 3.909