MRI‐based predictive factors of axillary lymph node status in breast cancer

Atallah, David; Moubarak, Malak; Arab, Wissam; Kassis, Nadine El; Chahine, Georges; Salem, C.

doi:10.1111/tbj.14089

Cited by 22 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A considerable proportion of patients could be misclassified according to the macro appearance of ALN images, consistent with several previous studies [ 24 , 25 ]. It has been reported that metastatic ALNs accounted for 80.77% of ALNs with short diameters ≥10 mm and 45.28% of ALNs with short diameters of 4–9 mm [ 26 ]. Therefore, traditional imaging tests are of limited value in assessing changes in the tumor microenvironment, especially in predicting ALN status in early BC with no changes in size, morphology, and signal but infiltration of cancer cells.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE‐MRI Radiomics Nomogram

Liu

Zhu

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Objective. To investigate the value of preoperative prediction of breast cancer axillary lymph node metastasis based on intratumoral and peritumoral dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) radiomics nomogram. Material and Methods. In this study, a radiomics model was developed based on a training cohort involving 250 patients with breast cancer (BC) who had undergone axillary lymph node (ALN) dissection between June 2019 and January 2021. The intratumoral and peritumoral radiomics features were extracted from the second postcontrast images of DCE-MRI. Based on filtered radiomics features, the radiomics signature was built by using the least absolute shrinkage and selection operator method. The Support Vector Machines (SVM) learning algorithm was used to construct intratumoral, periatumoral, and intratumoral combined periatumoral models for predicting axillary lymph node metastasis (ALNM) in BC. Nomogram performance was determined by its discrimination, calibration, and clinical value. Multivariable logistic regression was adopted to establish a radiomics nomogram. Results. The intratumoral combined peritumoral radiomics signature, which was composed of fifteen ALN status-related features, showed the best predictive performance and was associated with ALNM in both the training and validation cohorts ( P < 0.001 ). The prediction efficiency of the intratumoral combined peritumoral radiomics model was higher than that of the intratumoral radiomics model and the peritumoral radiomics model. The AUCs of the training and verification cohorts were 0.867 and 0.785, respectively. The radiomics nomogram, which incorporated the radiomics signature, MR-reported ALN status, and MR-reported maximum diameter of the lesion, showed good calibration and discrimination in the training (AUC = 0.872) and validation cohorts (AUC = 0.863). Conclusion. The intratumoral combined peritumoral radiomics model derived from DCE-MRI showed great predictive value for ALNM and may help to improve clinical decision-making for BC.

show abstract

Section: Discussionmentioning

confidence: 99%

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE‐MRI Radiomics Nomogram

Liu

Zhu

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…Moreover, the Color Doppler allows for the visualization of intranodal vascular pattern and the abnormal cortical blood flow to help further increase the detection rates ( 17 ). MRI has a minor role in the diagnosis of breast cancer and metastatic lymph node in a clinical setting, mostly because of its high price and time-consuming features ( 18 , 19 ). According to the literature, the pooled diagnostic sensitivity and specificity of MRI to detect axillary lymph node metastasis in patients with breast cancer were 75%-80% and 89%-91% respectively ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Optimal Selection of Imaging Examination for Lymph Node Detection of Breast Cancer With Different Molecular Subtypes

et al. 2022

View full text Add to dashboard Cite

ObjectiveAxillary lymph node management is an important part of breast cancer surgery and the accuracy of preoperative imaging evaluation can provide adequate information to guide operation. Different molecular subtypes of breast cancer have distinct imaging characteristics. This article was aimed to evaluate the predictive ability of imaging methods in accessing the status of axillary lymph node in different molecular subtypes.MethodsA total of 2,340 patients diagnosed with primary invasive breast cancer after breast surgery from 2013 to 2018 in Jiangsu Breast Disease Center, the First Affiliated Hospital with Nanjing Medical University were included in the study. We collected lymph node assessment results from mammography, ultrasounds, and MRIs, performed receiver operating characteristic (ROC) analysis, and calculated the sensitivity and specificity of each test. The C-statistic among different imaging models were compared in different molecular subtypes to access the predictive abilities of these imaging models in evaluating the lymph node metastasis.ResultsIn Her-2 + patients, the C-statistic of ultrasound was better than that of MRI (0.6883 vs. 0.5935, p=0.0003). The combination of ultrasound and MRI did not raise the predictability compared to ultrasound alone (p=0.492). In ER/PR+HER2- patients, the C-statistic of ultrasound was similar with that of MRI (0.7489 vs. 0.7650, p=0.5619). Ultrasound+MRI raised the prediction accuracy compared to ultrasound alone (p=0.0001). In ER/PR-HER2- patients, the C-statistics of ultrasound was similar with MRI (0.7432 vs. 0.7194, p=0.5579). Combining ultrasound and MRI showed no improvement in the prediction accuracy compared to ultrasound alone (p=0.0532).ConclusionFrom a clinical perspective, for Her-2+ patients, ultrasound was the most recommended examination to assess the status of axillary lymph node metastasis. For ER/PR+HER2- patients, we suggested that the lymph node should be evaluated by ultrasound plus MRI. For ER/PR-Her2- patients, ultrasound or MRI were both optional examinations in lymph node assessment. Furthermore, more new technologies should be explored, especially for Her2+ patients, to further raise the prediction accuracy of lymph node assessment.

show abstract

“…12 However, in recent studies, there are conflicting data on ADC value in ALN status. Atallah et al 26 revealed that in breast cancer patients, the difference in ADC values between ipsilateral and contralateral lymph nodes is not significantly associated with ALN positivity and Ramírez-Galván et al 27 stated that the ADC value of metastatic ALN was significantly higher than non-metastatic ALN in 44 cases. In line with the previous meta-analysis study, our study showed that metastatic ALN had lower mean ADC values than non-metastatic ALN and the difference was statistically significant.…”

Section: Discussionmentioning

confidence: 99%

Diffusion tensor imaging and diffusion-weighted imaging on axillary lymph node status in breast cancer patients

Kurt¹,

Kurt²,

Gulsaran³

et al. 2022

Diagn Interv Radiol

View full text Add to dashboard Cite

This article will examine the usefulness of diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) on the assessment of axillary lymph nodes (ALN) of breast cancer patients. METHODSAxillary lymph nodes in 66 breast cancer patients were examined by DTI and DWI, and the largest lymph node with increased cortical thickness in axilla was selected. Morphological features, apparent diffusion coefficient (ADC), volume anisotropy, and fractional anisotropy values were measured by using a special software. Imaging findings and histopathological results were recorded.

show abstract

MRI‐based predictive factors of axillary lymph node status in breast cancer

Cited by 22 publications

References 33 publications

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE‐MRI Radiomics Nomogram

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE‐MRI Radiomics Nomogram

Optimal Selection of Imaging Examination for Lymph Node Detection of Breast Cancer With Different Molecular Subtypes

Diffusion tensor imaging and diffusion-weighted imaging on axillary lymph node status in breast cancer patients

Contact Info

Product

Resources

About