Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy

Ren, Thomas; Lin, Stephanie K.; Huang, Pauline; Duong, Timothy Q.

doi:10.1016/j.clbc.2021.07.002

Clinical Breast Cancer

2022

DOI: 10.1016/j.clbc.2021.07.002

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Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy

Thomas Ren

Stephanie K. Lin

Pauline Huang

et al.

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Cited by 30 publications

(37 citation statements)

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“…However, small ALN may have micrometastasis in MR images, indicating subtle changes in cells and tissues. Still, these changes may not be visible to the naked eye, so they cannot be distinguished from normal lymph nodes, hindering their clinical application 14 …”

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confidence: 99%

“…With the development of artificial intelligence, deep learning has achieved great success in image classification, target detection, and other fields 35 . CNN has been used to classify ALNM using imaging features 36 or preprocessed MRI images 14,37,38 . It can also extract deep features from MR images, encode them with bag‐of‐features (BOF) and classify them with SVM 35 .…”

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confidence: 99%

“…It can also extract deep features from MR images, encode them with bag‐of‐features (BOF) and classify them with SVM 35 . CNN combined with multiparametric MRI has the potential to achieve better results 14 . Ren et al 14 studied the classification of ALNM by CNN on multiparametric MRI and established five models of T1‐weighted imaging (T1WI), T2‐weighted imaging (T2WI), DCE, T1WI + T2WI, and DCE + T2WI.…”

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confidence: 99%

“…Still, these changes may not be visible to the naked eye, so they cannot be distinguished from normal lymph nodes, hindering their clinical application. 14 Recently, radiomics and machine learning have made great progress in breast cancer diagnosis, treatment, and prognosis. 15 Most studies first preprocess the images and extract the radiomics features.…”

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confidence: 99%

“…35 CNN has been used to classify ALNM using imaging features 36 or preprocessed MRI images. 14,37,38 It can also extract deep features from MR images, encode them with bag-of-features (BOF) and classify them with SVM. 35 CNN combined with multiparametric MRI has the potential to achieve better results.…”

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confidence: 99%

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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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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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

Narongrit

Rispoli

2022

Magnetic Resonance Imaging

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Attention‐based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE‐MRI

Gao

Zhong

et al. 2022

Magnetic Resonance Imaging

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Background Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear. Purpose To investigate the potential of the proposed attention‐based DL model for the preoperative differentiation of ALN metastasis in breast cancer on dynamic contrast‐enhanced MRI (DCE‐MRI). Study Type Retrospective. Population A total of 941 breast cancer patients who underwent DCE‐MRI before surgery were included in the training (742 patients), internal test (83 patients), and external test (116 patients) cohorts. Field Strength/Sequence A 3.0 T MR scanner, DCE‐MRI sequence. Assessment A DL model containing a 3D deep residual network (ResNet) architecture and a convolutional block attention module, named RCNet, was proposed for ALN metastasis identification. Three RCNet models were established based on the tumor, ALN, and combined tumor‐ALN regions on the images. The performance of these models was compared with ResNet models, radiomics models, the Memorial Sloan‐Kettering Cancer Center (MSKCC) model, and three radiologists (W.L., H.S., and F. L.). Statistical Tests Dice similarity coefficient for breast tumor and ALN segmentation. Accuracy, sensitivity, specificity, intercorrelation and intracorrelation coefficients, area under the curve (AUC), and Delong test for ALN classification. Results The optimal RCNet model, that is, RCNet−tumor+ALN, achieved an AUC of 0.907, an accuracy of 0.831, a sensitivity of 0.824, and a specificity of 0.837 in the internal test cohort, as well as an AUC of 0.852, an accuracy of 0.828, a sensitivity of 0.792, and a specificity of 0.853 in the external test cohort. Additionally, with the assistance of RCNet−tumor+ALN, the radiologists' performance was improved (external test cohort, P < 0.05). Data Conclusion DCE‐MRI‐based RCNet model could provide a noninvasive auxiliary tool to identify ALN metastasis preoperatively in breast cancer, which may assist radiologists in conducting more accurate evaluation of ALN status. Evidence Level 3 Technical Efficacy Stage 2

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Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy

Cited by 30 publications

References 44 publications

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

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

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

Attention‐based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE‐MRI

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