Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer

Zhang, Xiang; Yang, Zehong; Cui, Wenju; Zheng, Chushan; Li, Haojiang; Li, Yudong; Lu, Liejing; Mao, Jiaji; Zeng, Weike; Yang, Xiaodong; Zheng, Jian; Shen, Jun

doi:10.1007/s00330-020-07674-z

Cited by 49 publications

(29 citation statements)

References 31 publications

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“…However, the highest AUC of the prediction model established by these clinicopathological factors was only 0.81, lower than the prediction model based on radiomic features alone established in this study. Therefore, some scholars (17,32,33) efficiency of the combined model in this study, while the prediction efficiency of other scholars' models is lower than this study. The combined model established in this study is simpler than the model established by Yu et al and has high predictive performance, providing a more convenient and feasible prediction model for clinical practice.…”

Section: Discussionmentioning

confidence: 55%

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

2021

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PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

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Section: Discussionmentioning

confidence: 55%

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

2021

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“…Radiomics could quantify heterogeneity of inter-tumor and intra-tumor by extracting high-throughput data from MR images ( 17 , 18 ). Previously, MRI-based radiomics of the primary breast cancer has been used to predict the ALN metastasis with an area under the curve (AUC) ranging from 0.81 to 0.92 in training and 0.74 to 0.90 in the validation datasets ( 19 – 22 ), and the SLN burden with a reported AUC of 0.82, 0.81, and 0.81 in the training, validation, and test dataset, respectively ( 23 ). However, whether MRI-based radiomics could be applied to predict the NSLN metastasis in breast cancer patients with positive SLNs remains to be determined.…”

Section: Introductionmentioning

confidence: 99%

MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer

Qiu

Zhang

et al. 2022

Front. Oncol.

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BackgroundOvertreatment of axillary lymph node dissection (ALND) may occur in patients with axillary positive sentinel lymph node (SLN) but negative non-SLN (NSLN). Developing a magnetic resonance imaging (MRI)-based radiomics nomogram to predict axillary NSLN metastasis in patients with SLN-positive breast cancer could effectively decrease the probability of overtreatment and optimize a personalized axillary surgical strategy.MethodsThis retrospective study included 285 patients with positive SLN breast cancer. Fifty five of them had metastatic NSLNs and 230 had non-metastatic NSLNs. MRI-based radiomic features of primary tumors were extracted and MRI morphologic findings of the primary tumor and axillary lymph nodes were assessed. Four models, namely, a radiomics signature, an MRI-clinical nomogram, and two MRI-clinical-radiomics nomograms were established based on MRI morphologic findings, clinicopathologic characteristics, and MRI-based radiomic features to predict the NSLN status. The optimal predictors in each model were selected using the 5-fold cross-validation (CV) method. Their predictive performances were determined by the receiver operating characteristic (ROC) curves analysis. The area under the curves (AUCs) of different models was compared by the Delong test. Their discrimination capability, calibration curve, and clinical usefulness were also assessed.ResultsThe 5-fold CV analysis showed that the AUCs ranged from 0.770 to 0.847 for the radiomics signature, from 0.720 to 0.824 for the MRI-clinical nomogram, from 0.843 to 0.932 for the MRI-clinical-radiomics nomogram. The optimal predictive factors in the radiomics signature, MRI-clinical nomogram, and MRI-clinical-radiomics nomogram were one texture feature of diffusion-weighted imaging (DWI), two clinicopathologic features together with one MRI morphologic finding, and the DWI-based texture feature together with the two clinicopathologic features plus the one MRI morphologic finding, respectively. The MRI-clinical-radiomics nomogram with CA 15-3 included achieved the highest AUC compared with the radiomics signature (0.868 vs. 0.806, P <0.001) and MRI-clinical nomogram (0.868 vs. 0.761; P <0.001). In addition, the MRI-clinical-radiomics nomogram without CA 15-3 showed a higher performance than that of the radiomics signature (AUC, 0.852 vs. 0.806, P = 0.016) and the MRI-clinical nomogram (AUC, 0.852 vs. 0.761, P = 0.007). The MRI-clinical-radiomics nomograms showed good discrimination and good calibration. Decision curve analysis demonstrated that the MRI-clinical-radiomics nomograms were clinically useful.ConclusionThe MRI-clinical-radiomics nomograms developed in our study showed high predictive performance, which can be used to predict the axillary NSLN status in SLN-positive breast cancer patients before surgery.

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“…5 Zhang et al verified that a multiparametric MRI-based radiomics nomogram incorporating the radiomics signature, and MRI-determined axillary lymph node burden had a favorable performance in predicting the SLN burden. 6 Although these techniques have high accuracy, prospective clinical studies including a larger number of patients are needed to confirm detection efficiency of this techniques in clinical practice. SLNB is still the standard method for axillary lymph node staging in early-stage breast cancer with clinical negative axilla.…”

Section: Introductionmentioning

confidence: 99%

The Guiding Significance of the Number of Positive Sentinel Lymph Nodes in Frozen Section for Intraoperative Axillary Dissection in Early Breast Cancer

Liang¹,

Li²,

Zhu³

et al. 2021

CMAR

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Purpose The results of large randomised trials have changed the treatment strategy of axillary lymph nodes. Axillary lymph node dissection (ALND) can be avoided in some patients with one to two sentinel lymph nodes (SLNs) metastasis based on final paraffin section (FPS) results which called into question the need for intraoperative frozen section (FS). This study aims to assess the guiding value of the number of positive SLN detected via FS for intraoperative ALND. Patients and Methods This study retrospectively analyzed data from 3303 patients with breast cancer who underwent SLN biopsy between 2015 and 2019. Combined with the FPS results, FS sensitivity, specificity, and false negative rate (FNR) were calculated and the difference in the number of positive SLNs between FS and FPS was analyzed. Results The overall FNR of FS was 23.21%, which was 76.47% in isolated tumor cells, 62.28% in micrometastasis, and 12.09% in macrometastatic disease. The size of SLN metastasis were significantly associated with a higher FNR (p<0.001). The accuracy rate of the number of positive SLNs detected via FS was 92.62%. Human epidermal growth factor receptor 2 (HER2) (p<0.03) and Ki67 (p<0.02) were significant factors affecting the accuracy rate. Conclusion FS is a effective method for SLN biopsy, ALND can be avoided in patients with one or two positive SLNs detected via FS.

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Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer

Cited by 49 publications

References 31 publications

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer

The Guiding Significance of the Number of Positive Sentinel Lymph Nodes in Frozen Section for Intraoperative Axillary Dissection in Early Breast Cancer

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