A comparative study of 18F-FDG PET/CT and ultrasonography in the diagnosis of breast cancer and axillary lymph node metastasis

Shim

American Journal of Clinical Oncology

2021

This study aimed to compare diagnostic performances of 8 different imaging modalities for preoperative detection of axillary lymph node (LN) metastasis in patients with breast cancer by performing a network meta-analysis (NMA) using direct comparison studies with 2 or more imaging techniques.Materials and Methods: PubMed, Cochrane, and Embase were searched for the studies evaluating the performances of 8 different imaging modalities for preoperative axillary LN staging in patients with breast cancer. The NMA was performed in patient-based analyses. The consistency was evaluated by examining the agreement between direct and indirect treatment effects, and publication bias was assessed by funnel plot asymmetry tests. The surface under the cumulative ranking curve (SUCRA) values were obtained to calculate the probability of each imaging modality being the most effective diagnostic method.Results: A total of 2197 patients from 22 direct comparison studies using 8 different imaging modalities for preoperative detection of axillary LN metastasis in patients with breast cancer were included. For preoperative detection of axillary LN metastasis of breast cancer, elastography showed the highest SUCRA values of sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and diagnostic odds ratio. In addition, fluorine-18 fluorodeoxyglucose positron emission tomography (PET) or PET/computed tomography, fluorine-18 fluorodeoxyglucose PET/magnetic resonance, and contrastenhanced computed tomography showed high SUCRA values. Conclusion:Elastography showed the highest SUCRA values. Seven imaging modalities showed the complementary diagnostic roles for preoperative detection of axillary LN metastasis in patients with breast cancer, except mammography.

Section: Baseline Characteristics Of Included Studiesmentioning

confidence: 99%

“…[30][31][32][33][43][44][45]47 Other 14 studies were performed in a retrospective manner. [26][27][28][29][34][35][36][37][38][39][40][41][42]46 The principal characteristics of the 22 studies are summarized in Table 1.…”

Section: Baseline Characteristics Of Included Studiesmentioning

confidence: 99%

Diagnostic Values of 8 Different Imaging Modalities for Preoperative Detection of Axillary Lymph Node Metastasis of Breast Cancer

Shim

American Journal of Clinical Oncology

2021

“…Therefore, mammography is generally not the preferred tool for clinical evaluation of axillary LN status. Compared with its role in patients with advanced breast cancer, PET-CT is less valuable for evaluating the axillary LNs in ESBC patients (16,17). In addition, due to its high cost and low coverage, PET-CT is typically not applied in the pre-operative evaluation of axillary LN status in China.…”

Section: Discussionmentioning

confidence: 99%

Establishing a prediction model of axillary nodal burden based on the combination of CT and ultrasound findings and the clinicopathological features in patients with early-stage breast cancer

Sun¹,

Zhou²,

Niu³

et al. 2021

Gland Surg

Background: Axillary lymph node (ALN) management in early-stage breast cancer (ESBC) patients has become less invasive during the past decades. Here, we tried to explore whether high nodal burden (HNB) in ESBC patients could be predicted preoperatively, so as to avoid unnecessary sentinel lymph node biopsy (SLNB). Methods:The clinicopathological and imaging data of patients with early invasive breast cancer (cT 1-2 N 0 M 0 ) were analyzed retrospectively. Univariate and multivariate analyses were performed for the risk factors of axillary HNB in ESBC patients, and a risk prediction model of HNB was established.Results: HNB was identified in 105 (8.0%) of 1,300 ESBC patients. Multivariate analysis showed that estrogen receptors (ER) status, human epidermal growth factor receptor 2 (HER2) status, number of abnormal lymph nodes (LNs) on computed tomography (CT), and axillary score on ultrasound (US) were the risk factors of HNB (all P<0.05). The area under the receiver operating characteristic (ROC) curve in the prediction model was 0.914, with the sensitivity being 85.7% and the specificity being 82.4%. The calibration curve showed that the prediction model had good performance.Conclusions: As a valuable tool for predicting HNB in ESBC patients, this newly established model helps clinicians to make reasonable axillary surgery decisions and thus avoid unnecessary SLNB.

“…Ultrasound, mammogram, PET/CT, and MRI have been used to diagnose ALNMs during breast cancer staging. Ultrasonography showed a sensitivity and a specificity of 33–86.2% and 40.5–96.2%, respectively [ 8 – 13 ]. The sensitivity and specificity of mammogram procedures were 21% and 99.5%, respectively [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…The sensitivity and specificity of mammogram procedures were 21% and 99.5%, respectively [ 11 ]. The overall sensitivities and specificities of PET/CT were reported to be 20–80% and 88.6–97%, respectively [ 8 – 10 , 13 , 14 ]. In addition, ultrasonography is convenient but is also dependent on operator experience.…”

Section: Introductionmentioning

confidence: 99%

A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients

et al. 2021

Background Despite that machine learning (ML)-based MRI has been evaluated for diagnosis of axillary lymph node metastasis (ALNM) in breast cancer patients, diagnostic values they showed have been variable. In this study, we aimed to assess the use of ML to classify ALNM on MRI and to identify potential covariates that might influence the diagnostic performance of ML. Methods A systematic research of PubMed, Embase, Web of Science, and the Cochrane Library was conducted until 27 December 2020 to collect the included articles. Subgroup analysis was also performed. Findings Fourteen studies assessing a total of 2247 breast cancer patients were included in the analysis. The overall AUC for ML in the validation set was 0.80 (95% confidence interval [CI] 0.76–0.83) with a negative predictive value of 0.83. The pooled sensitivity and specificity were 0.79 (95% CI 0.74–0.84) and 0.77 (95% CI 0.73–0.81), respectively. In the subgroup analysis of the validation set, T1-weighted contrast-enhanced (T1CE) imaging with ML yielded a higher sensitivity (0.80 vs. 0.67 vs. 0.76) than the T2-weighted fat-suppressed (T2-FS) imaging and diffusion-weighted imaging (DWI). Support vector machines (SVMs) had a higher specificity than linear regression (LR) and linear discriminant analysis (LDA) (0.79 vs. 0.78 vs. 0.75), whereas LDA showed a higher sensitivity than LR and SVM (0.83 vs. 0.70 vs. 0.77). Interpretation MRI sequences and algorithms were the main factors that affect the diagnostic performance of ML. Although its results were encouraging with the pooled sensitivity of around 0.80, it meant that 1 in 5 women that would go with undetected metastases, which may have a detrimental effect on the overall survival for 20% of patients with positive SLN status. Despite that a high NPV of 0.83 meant that ML could potentially benefit those with negative SLN, it might also translate to 1 in 5 tests being false negative. We would like to suggest that ML may not be yet usable in clinical routine especially when patient survival is used as a primary measurement of its outcome.