Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer

Chen, Chunfa; Zhang, Yuling; Cai, Ze-Long; Sun, Shuming; Lu, Xiaofeng; Lin, Hui‐Yi; Liang, Weiquan; Yuan, Ming-Heng; Zeng, De

doi:10.3389/fonc.2018.00666

Cited by 21 publications

(28 citation statements)

References 46 publications

(60 reference statements)

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“…These findings could be explained by the limited ability to obtain a complete visualization of the axilla on breast MRI without dedicated axillary protocol [ 26 ]. Prior studies have also demonstrated a high accuracy of MDCT in predicting axillary metastases in breast cancer [ 27 , 28 , 29 ]. Recently, Chen et al evaluated 148 breast cancer patients and found that the most important predictors of axillary lymph node metastasis on MDCT were cortical thickness > 3 mm and non-fatty hilum.…”

Section: Discussionmentioning

confidence: 99%

Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study

et al. 2020

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Background: The aim of this study was to demonstrate the feasibility of performing multidetector computed tomography (MDCT) with a dedicated protocol for locoregional staging in breast cancer patients. Methods: This prospective single-center study included newly diagnosed breast cancer patients submitted to contrast-enhanced chest MDCT and breast magnetic resonance imaging (MRI). MDCT was performed in prone position and using subtraction techniques. Fleiss’ Kappa coefficient (K) and intraclass correlation coefficient (ICC) were used to assess agreement between MRI, MDCT, and pathology, when available. Results: Thirty-three patients were included (mean age: 47 years). Breast MRI and MDCT showed at least substantial agreement for evaluation of tumor extension (k = 0.674), presence of multifocality (k = 0.669), multicentricity (k = 0.857), nipple invasion (k = 1.000), skin invasion (k = 0.872), and suspicious level I axillary lymph nodes (k = 0.613). MDCT showed higher number of suspicious axillary lymph nodes than MRI, especially on levels II and III. Both methods had similar correlation with tumor size (MRI ICC: 0.807; p = 0.008 vs. MDCT ICC: 0.750; p = 0.020) and T staging (k = 0.699) on pathology. Conclusions: MDCT with dedicated breast protocol is feasible and showed substantial agreement with MRI features in stage II or III breast cancer patients. This method could potentially allow one-step locoregional and systemic staging, reducing costs and improving logistics for these patients.

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Section: Discussionmentioning

confidence: 99%

Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study

et al. 2020

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“…However, these are invasive methods, with potential complications of arm pain, hematoma, seroma, lymphedema, and infection ( 6 ). Traditional imaging methods, including ultrasonography (US) ( 7 ), mammography ( 8 ), computed tomography (CT) ( 9 ), positron emission tomography/computed tomography (PET/CT) ( 10 ), and magnetic resonance imaging (MRI) ( 11 ), have all been used as noninvasive methods for evaluating the status of ALN in patients with breast cancer. However, some drawbacks to their use exist.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

et al. 2021

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BackgroundThe use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.PurposeThe aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.MethodsWe retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).ResultsThe radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.ConclusionThe radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

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“…Chen et al. ( 7 ) have demonstrated in their cohort of 148 cases of patients with primary breast cancer undergoing axillary surgery that cortical thickness of more than 3 mm and a non-fatty hilum were independent predictors for metastases in the axillary nodes.…”

Section: Introductionmentioning

confidence: 99%

Commentary: Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients with Breast Cancer

Cox

Spratt

2021

Front. Oncol.

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Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer

Cited by 21 publications

References 46 publications

Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study

Multidetector Computed Tomography with Dedicated Protocol for Breast Cancer Locoregional Staging: Feasibility Study

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Commentary: Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients with Breast Cancer

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