The influence of breast cancer subtypes on axillary ultrasound accuracy: A retrospective single center analysis of 583 women

Helfgott, Ruth; Mittlböck, Martina; Miesbauer, Maria; Moinfar, Farid; Haim, Silke; Mascherbauer, Maria; Schlagnitweit, Paul; Heck, Dietmar; Knauer, Michael; Fitzal, Florian

doi:10.1016/j.ejso.2018.10.001

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…In addition, our data indicated that false negative patients were more likely to occur in younger than 50 years old, Ki-67 high expression (> 20%), larger primary tumor size (> 2 cm) and higher histological grade, which was consistent with the results of Yan' na Zhang and other studies [27]. Therefore, in the high-risk subgroups that can lead to high false negative rate, we should carefully consider the results of ultrasound, and combine other examination methods, such as various kinds of tumor puncture biopsy, to improve the accuracy of preoperative diagnosis [28][29][30][31].…”

Section: Discussionsupporting

confidence: 88%

The Value of Ultrasound Diagnosed Axillary Lymph Node in Avoiding Axillary Surgery in Early Breast Cancer Patients

Liu

Wang

et al. 2020

Preprint

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Background: Axillary lymph node dissection can be avoided in early stage breast cancer patients with negative sentinel lymph node biopsy. However, the possibility of avoiding axillary surgery in patients without axillary lymph node metastasis (ALNM) by preoperative imaging is still under exploration. Thus, the objectives of this study were to investigate the high-risk factors of false negative of ALNM diagnosed by preoperative ultrasound (US) and to find out who could be avoided axillary surgery in the US negative ALNM patients.Methods: This study retrospectively analyzed 3,361 patients with primary early breast cancer diagnosed in the Breast Center of the Fourth Hospital of Hebei Medical University from January 2010 to December 2012. All patients had undergone routine preoperative US and then axillary lymph node dissected. This study investigated the clinicopathological features of axillary lymph node (ALN) negative patients diagnosed by preoperative US and its correlation with prognosis. The follow-up data for disease-free survival (DFS) and overall survival (OS) were obtained from 2,357 patients. Results: The sensitivity, specificity and accuracy of axillary US in this cohort were 66.24%, 76.62% and 73.87%. The proportion of patients in the false negative group was higher than that in true negative in the group of age < 50 years old (P = 0.002), tumor size > 2cm (P = 0.008), estrogen receptor (ER) positive (P = 0.005), progesterone receptor (PR) high expression (P = 0.007), nuclear-associated antigen Ki-67 (Ki-67) >20% (P = 0.030), visible vascular tumor thrombus (P < 0.001) and histological grade>2 (P < 0.001). Prognostic analysis of false negative and true negative ultrasonographic diagnosis of ALN metastasis: when ALNM was not found by preoperative ultrasound, there was no significant difference in patients with ALNM≤3 compared with patients without lymph node metastasis in patients of age ≥ 50 years old, tumor size ≤ 2cm, Ki-67 ≤ 20%, or histological grade ≤ 2. Conclusion: The surgery of ALN may be avoided for the preoperative US diagnosed ALNs negative in early breast cancer patients who had advanced age, small tumor size, low expression of Ki-67 and low histological grade.

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Section: Discussionsupporting

confidence: 88%

The Value of Ultrasound Diagnosed Axillary Lymph Node in Avoiding Axillary Surgery in Early Breast Cancer Patients

Liu

Wang

et al. 2020

Preprint

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“…As palpation is inaccurate (29), AUS is performed to provide more relevant information. AUS alone has a reported sensitivity of 39-60%, specificity of 90-96%, PPV of 80-91%, and NPV of 75-83% (6,30,31). This implied that despite of an acceptable specificity above 90%, prior to surgery about 40-60% of nodal metastases cannot be found by AUS and about 20-25% of patients with a negative AUS have been assessed as modal metastases after surgery.…”

Section: P-values Were Calculated By Using χ 2 Test or Wilcoxon Rank-mentioning

confidence: 99%

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

et al. 2020

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Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods:We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra-and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Sun et al. Ultrasound-CNN Predicted Breast Cancer MetastasisConclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

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“…However, the sensitivity of US (71%) for prediction of residual nodal metastatic disease was higher than that of clinical examination and MRI/ PET in most studies [139]. Limited data have suggested that the sensitivity of axillary US is higher for some subtypes such as triple negative breast cancer (69%) and HER2 positive breast cancer (71%) [140,141].…”

Section: Us Axillamentioning

confidence: 97%

ACR Appropriateness Criteria® Imaging of the Axilla

Le-Petross¹,

Slanetz²,

Lewin³

et al. 2022

Journal of the American College of Radiology

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The influence of breast cancer subtypes on axillary ultrasound accuracy: A retrospective single center analysis of 583 women

Cited by 12 publications

References 28 publications

The Value of Ultrasound Diagnosed Axillary Lymph Node in Avoiding Axillary Surgery in Early Breast Cancer Patients

The Value of Ultrasound Diagnosed Axillary Lymph Node in Avoiding Axillary Surgery in Early Breast Cancer Patients

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

ACR Appropriateness Criteria® Imaging of the Axilla

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