Breast MRI and tumour biology predict axillary lymph node response to neoadjuvant chemotherapy for breast cancer

Al-Hattali, Samia; Vinnicombe, Sarah; Gowdh, Nazleen Muhammad; Evans, Andrew; Armstrong, Sharon; Adamson, Douglas; Purdie, Colin A.; Macaskill, E. Jane

doi:10.1186/s40644-019-0279-4

Cited by 23 publications

(30 citation statements)

References 30 publications

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“…According to tumor biology, we found significantly higher nodal pCR rates (64.3%) in patients with TN and HER2 positive disease compared with luminal disease (P = 0.0002). Results of previous studies also confirmed high pCR rates, between 50% and 62%, in patients with HER2 positive and TN tumor compared with luminal subtypes [ 1 4 24 25 ].…”

Section: Discussionsupporting

confidence: 64%

“…On the other hand, NAC can eradicate axillary metastasis in a significant proportion of patients for which post-NAC restaging of the axilla is crucial in sparing patients the morbidity of ALND. Similarly, in the majority of such series, the eradication rate of nodal metastases was reported between 33% and 45% after NAC in patients with biopsy-proven positive nodes before NAC [ 1 2 3 10 14 25 26 ]. Pathological analysis of lymph nodes after surgery revealed nodal pCR to NAC in a considerable proportion (46.9%) of our breast cancer cases.…”

Section: Discussionmentioning

confidence: 99%

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Targeted axillary biopsy and sentinel lymph node biopsy for axillary restaging after neoadjuvant chemotherapy

et al. 2021

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Purpose Accurate restaging of the axilla after neoadjuvant chemotherapy (NAC) is an important issue to ensure deescalating axillary surgery in patients with initial metastatic nodes. We aimed to present our results of targeted axillary biopsy (TAB) combined with sentinel lymph node biopsy (SLNB) for axillary restaging after NAC. Methods In 64 breast cancer patients who underwent NAC, biopsy-proven positive nodes were marked with clips before NAC, and ultrasound-guided wire localization of clip-marked nodes was performed after NAC. Patients underwent TAB and SLNB for post-NAC axilla restaging. Results Identification rates of post-NAC TAB and SLNB were 98.4% and 87.5%, respectively (P = 0.033). Histopathology revealed a nodal pathologic complete response (pCR) rate of 47% in which axillary lymph node dissection (ALND) was avoided. TAB alone and SLNB alone detected residual disease in 29 (85.3%) and 20 (58.8%) patients (P = 0.029), respectively. Whereas rates of up to 97% had been achieved with a combination of TAB and SLNB. The pCR rates after NAC were 64.3% for human epidermal growth factor receptor 2 positive and triple-negative tumors and 13.6% in luminal tumors (P = 0.0002). Conclusion Pathologic analysis following TAB combined with SLNB revealed the pCR rates to NAC in a considerable number of patients that provided de-escalation of axillary surgery. A combination of SLNB and TAB was found to be an accurate procedure in establishing residual nodal disease. This combined procedure in patients with initially positive nodes was a reliable method for post-NAC axillary restaging.

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Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Targeted axillary biopsy and sentinel lymph node biopsy for axillary restaging after neoadjuvant chemotherapy

et al. 2021

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“…According to the study by Osorio-Silla [77], 59.7% of patients with primary lesions complete response on MRI also achieved complete response on axillary lymph node, and 75.9% of patients with non-complete response of breast primary tumors on MRI had residual lymph node disease after surgery. Many studies [104,106,107] have similar results, indicating the importance of MRI complete response in primary lesion rate is an important independent for predicting of axillary pCR.…”

Section: Lymph Node Response Evaluationmentioning

confidence: 68%

“…But the post-NAT MRI obtained a high negative predictive value (94% and 97.3%, respectively), which means that negative post-NAT MRI can accurately exclude the axillary lymph node diseases [101,102]. Before and during NAT, lymph node pCR can be predicted by negative hormone receptor and positive HER2 receptor status [103][104][105], lower clinical T and N stage [103], higher histological/nuclear grade [103], and treatment response to NAT of breast primary lesions [106,107]. According to the study by Osorio-Silla [77], 59.7% of patients with primary lesions complete response on MRI also achieved complete response on axillary lymph node, and 75.9% of patients with non-complete response of breast primary tumors on MRI had residual lymph node disease after surgery.…”

Section: Lymph Node Response Evaluationmentioning

confidence: 99%

The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

Guo

Yang

et al. 2020

Chin J Acad Radiol

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Breast magnetic resonance imaging (MRI) is always considered to be more accurate for evaluating neoadjuvant therapy (NAT) response than mammography, ultrasound and clinical examination. Preoperative MRI aims to evaluate residual disease to facilitate surgical planning. MRI scans performed before and/or during NAT aim to predict the treatment response and thus adjust the NAT plan at an early stage. MRI accuracy depends on tumor morphology, histology, shrinkage pattern and molecular subtype. Combining multiparameter functional MRI can improve the prediction accuracy. In addition, radiomics offers high-dimensional and high-throughput signatures and were shown to predict treatment response before NAT. This article reviews the indicators of breast MRI for evaluating NAT response, including morphological MRI features, enhancement and pharmacokinetic parameters, apparent diffusion coefficient (ADC), choline concentration, etc., even radiomic signatures and illustrates their advantages, limitations and future directions.

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“…The properties of the breast cancer anomalies are diagnosed at its early stage to control spread of cancer cells and the human visual perception is quite difficult to determine the abnormalities during misclassification [3]. The detection of malignancy risk in breast image is studied by the department of American College of Radiology (ACR) using Breast Imaging Reporting and Data System (BI-RADS) dataset [4]. The imaging techniques used in the existing researches are time consuming and complex task, which requires correlation to multi-parametric and interpretation of data to accurate classification [5,6].…”

Section: Introductionmentioning

confidence: 99%

Detection of Breast Cancer on Magnetic Resonance Imaging Using Hybrid Feature Extraction and Deep Neural Network Techniques

Pullaiah¹,

Venkatasekhar²,

Venkatramana³

et al. 2020

IJIES

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Breast cancer is one of the most occurring cancers in women due to the uncontrolled growth of abnormal cells in the lobules or milk ducts. The treatment for the breast cancer at an early stage is important using Magnetic Resonance Imaging (MRI) which effectively measures the size of the cancer and also checks tumors in the opposite breast. The deposition of calcium components on the breast tissue is known as micro-calcifications. The calcium salts deposited in the breast are involved with the cancer and were not diagnosed accurately due to the low effectiveness of existing imaging technique namely Haralick feature extraction technique. The MRI breast cancer diagnosis creates problems during classification of breast image and leads to misclassifications, such as unidentified calcium deposits in the existing K-Nearest Neighbour (KNN) classifier. The misclassification issues are overcome by an accurate classification and identification of calcium salts and checks whether deposited salt on breast tissue is involved with cancer or not. Initially, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to remove the unwanted noise in the MRI and Morphological, Multilevel Otsu’s Thresholding and region growing techniques perform segmentation to mask unwanted breast tissues. The proposed Hybrid LOOP Haralick feature extraction technique is developed by combining the both Local Optimal Oriented Pattern (LOOP) and Haralick texture feature and the hybrid parameters are applied to the Stacked Auto Encoder based (SAE) to classify the breast MRI image as a Malignant or Benign. The performance of the proposed hybrid LOOP Haralick feature extraction shows significant accuracy improvement of 3.83% when compared to the Haralick feature extraction technique.

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Breast MRI and tumour biology predict axillary lymph node response to neoadjuvant chemotherapy for breast cancer

Cited by 23 publications

References 30 publications

Targeted axillary biopsy and sentinel lymph node biopsy for axillary restaging after neoadjuvant chemotherapy

Targeted axillary biopsy and sentinel lymph node biopsy for axillary restaging after neoadjuvant chemotherapy

The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

Detection of Breast Cancer on Magnetic Resonance Imaging Using Hybrid Feature Extraction and Deep Neural Network Techniques

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