Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

Zhao, Ming; Wu, Qiong; Guo, Lili; Zhou, Li; Fu, Kuang I.

doi:10.1016/j.ejrad.2020.109093

Cited by 29 publications

(22 citation statements)

References 36 publications

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“…The molecular subtype is also associated with BC prognosis and ALNM 41 . A previous study showed that washout TIC could be a predictive factor for ALNM 14 , which was not observed in the present study, possibly because of cancer stage and patient characteristics. These variables were not significant in the multivariable analysis, possibly because of collinearity and correlation of some of these factors among themselves.…”

Section: Discussioncontrasting

confidence: 90%

“…In this study, MR-reported LN status was the most important factor for ALNM. Since MRI has a low sensitivity for ALNM 14 , a report of a suspicious ALNM might indicate a true positive and a more advanced lesion. LVI was an important factor of ALNM.…”

Section: Discussionmentioning

confidence: 99%

“…It has a sensitivity of 94% to 100% in BC 13 . Still, breast MRI scanning may only be used to evaluate the ALNs partially because the ALNs can be located at the coil edge, the signal-to-noise ratio (SNR) of the image is usually low, and image quality is affected by heartbeat 14 .…”

Section: Introductionmentioning

confidence: 99%

“…developed a radiomic nomogram for the preoperative prediction of ALNM in BC patients, and its prediction ability was moderate (AUC of 0.76 and 0.78 in training and validation cohorts). Zhao et al14 assessed the clinical value of conventional MRI and intravoxel incoherent motion (IVIM) features to predict the risk of ALNM in patients with BC, and the AUC of MRI combined with slow ADC, fast ADC, and fraction of fast ADC were 0.814, 0.803 and 0.900, respectively. These techniques might be very difficult to use in clinical practice, considering it is time-consuming because of experimental sequences or manual segmentation.…”

mentioning

confidence: 99%

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Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

Meng

Che

Tian

et al. 2022

Clinical Breast Cancer

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Section: Discussioncontrasting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

Meng

Che

Tian

et al. 2022

Clinical Breast Cancer

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“…Therefore, the identi cation of ALN metastasis is crucial for patients with breast cancer. The gold standard for detecting ALN involvement is ALN dissection, which however can bring many adverse complications such as arm edema, pain, neuropathy, and so on (5). Although minimally invasive methods including sentinel lymph node biopsy, core needle biopsy and ne needle aspiration cytology lead to fewer complications than ALN dissection, they have the risk of implantation metastasis (6).…”

Section: Introductionmentioning

confidence: 99%

18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG

Tian

Zhang

et al. 2021

Preprint

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18F-Alfatide II has been translated into clinical use and been proven to have good performance in identifying breast cancer. In this study, we investigated 18F-Alfatide II for evaluation of axillary lymph nodes (ALN) in breast cancer patients and compared the performances with 18F-FDG. Methods: A total of 44 female patients with clinically suspected breast cancer were enrolled and underwent 18F-Alfatide II and 18F-FDG PET/CT within a week. Tracer uptakes in ALN were evaluated by visual analysis, semiquantitative analysis with maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and SUVmax ratio of target/non-target (T/NT). Results: Among 44 patients, 37 patients were pathologically diagnosed with breast cancer with metastatic (17 cases) or non-metastatic (20 cases) ALN. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of visual analysis were 70.59%, 90.00%, 81.08%, 85.71% and 78.26% for 18F-Alfatide II, 64.71%, 90.00%, 78.38%, 84.62% and 75.00% for 18F-FDG, respectively. By combining 18F-Alfatide II and 18F-FDG, the sensitivity significantly increased to 82.35%, the specificity was 85.00%, the accuracy increased to 83.78%, the PPV was 82.35% and the NPV significantly increased to 85.00%. Three cases of luminal B subtype were false negative for both 18F-Alfatide II and 18F-FDG. The other 2 false negative cases of 18F-Alfatide II were triple-negative subtype and 3 false negative cases of 18F-FDG were luminal B subtype too. The AUCs of three semi-quantitative parameters (SUVmax, SUVmean, T/NT) for 18F-Alfatide II were between 0.8 and 0.9, whereas those for 18F-FDG were more than 0.9. 18F-Alfatide II T/NT had the highest Youden index (76.5%), specificity (100%), accuracy (89.19%) and PPV (100%) among these semi-quantitative parameters. 18F-Alfatide II uptake as well as 18F-FDG uptake in metastatic axillary lymph nodes (MALN) was significantly higher than that in benign axillary lymph nodes (BALN). Both 18F-Alfatide II and 18F-FDG did not show difference in primary tumor uptake irrespective of ALN status. Conclusion: 18F-Alfatide II can be used in breast cancer patients to detect metastatic ALN, however, like 18F-FDG, with high specificity but relatively low sensitivity. The combination of 18F-Alfatide II and 18F-FDG can significantly improve sensitivity and NPV. 18F-Alfatide II T/NT may serve as the most important semi-quantitative parameter to evaluate ALN.

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Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI

Wang

Sun

et al. 2022

Magnetic Resonance Imaging

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BackgroundMultiparametric magnetic resonance imaging (MRI) is widely used in breast cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is essential for breast cancer surgery and treatment. However, there is no mature and effective discerning method for ALNM based on multiparametric MRI.PurposeTo evaluate the ALNM using T1‐weighted imaging (T1WI), T2‐weighted imaging (T2WI), and diffusion‐weighted imaging (DWI) sequences, respectively, and construct a quantitative ALNM discerning model of integrated multiparametric MRI.Study TypeRetrospective.PopulationThree‐hundred forty‐eight breast cancer patients, 163 with ALNM (99.39% females), and 185 without ALNM (100% females). The dataset was randomly divided into the training set (315 cases) and the testing set (33 cases).Field Strength/Sequence1.5 T; T1WI (VIBRANT), T2WI (FSE), and DWI (echo planar imaging [EPI]).AssessmentThe lesion region of interest images were cropped and sent to a pretrained ResNet50 network. Then, the results of different sequences were sent to a classifier for ensemble learning to construct the ALNM model of multiparametric MRI.Statistical TestsPerformance indicators such as accuracy, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were calculated. Student's t‐test, chi‐square test, Fisher's exact test, and Delong test were performed, and P < 0.05 was considered statistically significant.ResultsT2WI performed the best among the three sequences, and achieved the accuracy and AUC of 0.933/0.989 in the testing set. Compared to T1WI with the accuracy and AUC of 0.691/0.806, the increase is significant. While compared to DWI with the accuracy and AUC of 0.800/0.910, the improvement is not significant (P = 0.126). After integrating three sequences, the accuracy and AUC improved to 0.970 and 0.996.Data ConclusionT2WI performed better than DWI and T1WI in discerning ALNM in this breast cancer dataset. The proposed quantitative model of integrated multiparametric MRI could effectively help the ALNM diagnosis.Level of Evidence1Technical Efficacy Stage2

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Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

Cited by 29 publications

References 36 publications

Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI

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