Identification of Axillary Lymph Node Metastasis in Patients With Breast Cancer Using Dual-Phase FDG PET/CT

Sasada, Shinsuke; Masumoto, Norio; Kimura, Yuri; Kajitani, Keiko; Emi, Akiko; Kadoya, Takayuki; Okada, Morihito

doi:10.2214/ajr.19.21373

Cited by 20 publications

(15 citation statements)

References 37 publications

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“…In this study, visual assessment ndings showed that the false negative rate of 18 F-Alfatide II (5/17) was similar to that of 18 F-FDG (6/17) for detecting metastatic ALN, whereas the false positive rate of 18 and high speci city (90.00%) of 18 F-FDG in our study were consistent with the numbers reported in previous studies (24)(25)(26)(27)(28)(29)(30)(31). Compared with 18 F-FDG, 18 F-Alfatide II had the same high speci city (90.00%) and relatively higher sensitivity (70.59%).…”

Section: Semi-quantitative Assessment Findingssupporting

confidence: 92%

18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG

Tian

Zhang

et al. 2021

Preprint

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18F-Alfatide II has been translated into clinical use and been proven to have good performance in identifying breast cancer. In this study, we investigated 18F-Alfatide II for evaluation of axillary lymph nodes (ALN) in breast cancer patients and compared the performances with 18F-FDG. Methods: A total of 44 female patients with clinically suspected breast cancer were enrolled and underwent 18F-Alfatide II and 18F-FDG PET/CT within a week. Tracer uptakes in ALN were evaluated by visual analysis, semiquantitative analysis with maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and SUVmax ratio of target/non-target (T/NT). Results: Among 44 patients, 37 patients were pathologically diagnosed with breast cancer with metastatic (17 cases) or non-metastatic (20 cases) ALN. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of visual analysis were 70.59%, 90.00%, 81.08%, 85.71% and 78.26% for 18F-Alfatide II, 64.71%, 90.00%, 78.38%, 84.62% and 75.00% for 18F-FDG, respectively. By combining 18F-Alfatide II and 18F-FDG, the sensitivity significantly increased to 82.35%, the specificity was 85.00%, the accuracy increased to 83.78%, the PPV was 82.35% and the NPV significantly increased to 85.00%. Three cases of luminal B subtype were false negative for both 18F-Alfatide II and 18F-FDG. The other 2 false negative cases of 18F-Alfatide II were triple-negative subtype and 3 false negative cases of 18F-FDG were luminal B subtype too. The AUCs of three semi-quantitative parameters (SUVmax, SUVmean, T/NT) for 18F-Alfatide II were between 0.8 and 0.9, whereas those for 18F-FDG were more than 0.9. 18F-Alfatide II T/NT had the highest Youden index (76.5%), specificity (100%), accuracy (89.19%) and PPV (100%) among these semi-quantitative parameters. 18F-Alfatide II uptake as well as 18F-FDG uptake in metastatic axillary lymph nodes (MALN) was significantly higher than that in benign axillary lymph nodes (BALN). Both 18F-Alfatide II and 18F-FDG did not show difference in primary tumor uptake irrespective of ALN status. Conclusion: 18F-Alfatide II can be used in breast cancer patients to detect metastatic ALN, however, like 18F-FDG, with high specificity but relatively low sensitivity. The combination of 18F-Alfatide II and 18F-FDG can significantly improve sensitivity and NPV. 18F-Alfatide II T/NT may serve as the most important semi-quantitative parameter to evaluate ALN.

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Section: Semi-quantitative Assessment Findingssupporting

confidence: 92%

18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG

Tian

Zhang

et al. 2021

Preprint

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“…In particular, breast MRI, which is widely used in the preoperative assessment of ALN status in breast cancer patients, exhibits better performance than some other techniques such as US and mammography (26). Compared with CT, MRI and PET/CT display improved sensitivity and specificity in the assessment of ALNM, with the sensitivities ranging from 47.1-98.5% and 48.1-85.7% and the specificities ranging from 57.8-98.0% and 61.5-94.7%, respectively (27)(28)(29)(30)(31)(32). However, the diagnostic accuracy of MRI and PET/CT varies depending on the study.…”

Section: Discussionmentioning

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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“…In clinical practice, LVI is usually postoperatively diagnosed by histopathology, but LNM can be detected by imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)/CT, and ultrasound (27)(28)(29). PET/ CT in particular offers high accuracy in the diagnosis of LNM (30)(31)(32). Therefore, we wished to use LNM to predict LVI, and finally found that LNM was significantly related to LVI in patients with SESCC (Table 3).…”

Section: Discussionmentioning

confidence: 99%

A Nomogram for Predicting Lymphovascular Invasion in Superficial Esophageal Squamous Cell Carcinoma

et al. 2021

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The lymphovascular invasion (LVI) status facilitates the determination of the optimal therapeutic strategy for superficial esophageal squamous cell carcinoma (SESCC), but in clinical practice, LVI must be confirmed by postoperative pathology. However, studies of the risk factors for LVI in SESCC are limited. Consequently, this study aimed to identify the risk factors for LVI and use these factors to establish a prediction model. The data of 516 patients who underwent radical esophagectomy between January 2007 and September 2019 were retrospectively collected (training set, n=361, January 2007 to May 2015; validation set, n=155, June 2015 to September 2019). In the training set, least absolute shrinkage and selection operator (LASSO) regression and multivariate analyses were utilized to identify predictive factors for LVI in patients with SESCC. A nomogram was then developed using these predictors. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the efficiency, accuracy, and clinical utility of the model. LASSO regression indicated that the tumor size, depth of invasion, tumor differentiation, lymph node metastasis (LNM), sex, circumferential extension, the presence of multiple lesions, and the resection margin were correlated with LVI. However, multivariate analysis revealed that only the tumor size, depth of invasion, tumor differentiation, and LNM were independent risk factors for LVI. Incorporating these four variables, model 1 achieved an AUC of 0.817 in predicting LVI. Adding circumferential extension to model 1 did not appreciably change the AUC and integrated discrimination improvement, but led to a significant increase in the net reclassification improvement (p=0.011). A final nomogram was constructed by incorporating tumor size, depth of invasion, tumor differentiation, LNM, and circumferential extension and showed good discrimination (training set, AUC=0.833; validation set, AUC=0.819) and good calibration in the training and validation sets. Decision curve analysis demonstrated that the nomogram was clinically useful in both sets. Thus, it is possible to predict the status of LVI using this nomogram scoring system, which can aid the selection of an appropriate treatment plan.

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Identification of Axillary Lymph Node Metastasis in Patients With Breast Cancer Using Dual-Phase FDG PET/CT

Cited by 20 publications

References 37 publications

18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG

18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG

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

A Nomogram for Predicting Lymphovascular Invasion in Superficial Esophageal Squamous Cell Carcinoma

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