Relation of peritumoral, prepectoral and diffuse edema with histopathologic findings of breast cancer in preoperative 3T magnetic resonance imaging

Gemici, Aysegul Akdogan; Ozal, Safiye Tokgoz; Hocaoğlu, Elif; Arslan, Gözde; Şen, Ebru; Altınay, Serdar; İnci, Ercan

doi:10.28982/josam.512779

Cited by 5 publications

(10 citation statements)

References 22 publications

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“…Edema occurs when excessive fluid accumulates in tissues, either within cells (cellular edema) or within the collagen-mucopolysaccharide matrix distributed in the interstitial space (interstitial edema) [ 13 ]. Uematsu [ 8 ] divided focal breast edema into peritumoral, prepectoral, and subcutaneous types. However, few studies have reported on the pathophysiology of each subtype of focal breast edema.…”

Section: Discussionmentioning

confidence: 99%

“…Several authors have reported that peritumoral edema may arise from neoangiogenesis during tumor growth [ 8 14 ]. Increased vascular permeability due to leaky basilar membranes in newly formed tumor vessels can lead to peritumoral proteolysis, resulting in transudation from the intravascular space to the extracellular space, and contributing to peritumoral edema.…”

Section: Discussionmentioning

confidence: 99%

“…Peritumoral edema was defined as T2 hyperintensity in the adjacent breast tissue compared to the contralateral breast parenchyma signal. We classified edema into three types [ 8 10 ]: 1) peritumoral edema, defined as edema present in the area ≤ 1 cm from the main mass ( Figure 1 ), 2) prepectoral edema, defined as edema between breast parenchyma tissue and the chest wall ( Figure 2 ), and 3) subcutaneous edema, defined as edema in the subcutaneous layer located superficially from normal breast tissue ( Figure 3 ). There was an average gap of 18.74 days between the biopsy and MRI, minimizing the possibility of misdiagnosing biopsy-related changes as peritumoral edema.…”

Section: Methodsmentioning

confidence: 99%

“…Unlike diffuse breast edema, focal edema is commonly associated with malignancy, although it is currently not accepted as a significant prognostic factor in current guidelines [ 7 ]. In 2015, Uematsu divided focal breast edema into three types: peritumoral, prepectoral, and subcutaneous [ 8 ]. Several studies have shown a relationship among focal breast edema, breast cancer biology, and prognosis [ 8 9 10 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…In 2015, Uematsu divided focal breast edema into three types: peritumoral, prepectoral, and subcutaneous [ 8 ]. Several studies have shown a relationship among focal breast edema, breast cancer biology, and prognosis [ 8 9 10 11 ]. For instance, significant associations have been reported between peritumoral edema on MRI and histopathologic findings, including lymphovascular invasion (LVI) [ 9 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Prognostic Implication of Focal Breast Edema on Preoperative Breast Magnetic Resonance Imaging in Breast Cancer Patients

Sung,

Lee,

Cheun

et al. 2023

J Breast Cancer

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Purpose In this study, we investigated the prognostic implications of focal breast edema on preoperative breast magnetic resonance imaging (MRI) in patients with breast cancer. Methods Data of 899 patients with breast cancer at a single institution were retrospectively analyzed. The patients were divided into an edema-positive group (EPG) and an edema-negative group (ENG) based on the presence of peritumoral, prepectoral, or subcutaneous edema. Two radiologists evaluated the presence or absence of focal edema and its subtypes on preoperative breast MRI. Clinicopathologic characteristics and survival outcomes were compared between the two groups and among the three subtypes using Pearson’s χ 2 test, Kaplan–Meier estimator, and Cox proportional hazards model. Results There were 399 (44.4%) and 500 (55.6%) patients in the EPG and ENG, respectively. The EPG showed significantly higher rates of axillary lymph node metastasis (55.6% vs. 19.2%, p < 0.001) and lymphovascular invasion (LVI) (57.9% vs. 12.6%, p < 0.001) than the ENG. Patients in the EPG showed significantly worse overall survival (OS) rate (log-rank p < 0.001; hazard ratio [HR], 4.83; 95% confidence interval [CI], 2.56–9.11) and recurrence-free survival rate (log-rank p < 0.001; HR, 3.00; 95% CI, 1.94–4.63) than those in the ENG. After adjusting for other variables, focal breast edema remained a significant factor affecting the OS rate, regardless of the edema type. Specifically, the presence of subcutaneous edema emerged as the strongest predictor for OS with the highest HR ( p < 0.001; HR, 9.10; 95% CI, 3.05–27.15). Conclusion Focal breast edema on preoperative breast MRI implies a higher possibility of LVI and axillary lymph node metastasis, which can lead to a poor prognosis. A detailed description of focal breast edema, especially subcutaneous edema, on preoperative breast MRI may provide prognostic predictions. More intensive surveillance is required for patients with breast cancer and focal preoperative breast edema.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prognostic Implication of Focal Breast Edema on Preoperative Breast Magnetic Resonance Imaging in Breast Cancer Patients

Sung,

Lee,

Cheun

et al. 2023

J Breast Cancer

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Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Nastase,

Moldovanu,

Biswas

et al. 2024

Sci Rep

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Accurate and unbiased classification of breast lesions is pivotal for early diagnosis and treatment, and a deep learning approach can effectively represent and utilize the digital content of images for more precise medical image analysis. Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses. Based on the different ways in which benign and malignant tumors affect neighboring tissues, i.e., the pattern of growth and border irregularities, the penetration degree of the adjacent tissue, and tissue-level changes, we investigated the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep learning classification. The novelty of the proposed approach lies in considering the features extracted from the tissue inside the tumor (by performing an erosion operation) and from the lesion and surrounding tissue (by performing a dilation operation) for classification. This study uses these new features and three pre-trained deep neuronal networks to address the challenge of breast lesion classification in ultrasound images. To improve the classification accuracy and interpretability of the model, the proposed model leverages transfer learning to accelerate the training process. Three modern pre-trained CNN architectures (MobileNetV2, VGG16, and EfficientNetB7) are used for transfer learning and fine-tuning for optimization. There are concerns related to the neuronal networks producing erroneous outputs in the presence of noisy images, variations in input data, or adversarial attacks; thus, the proposed system uses the BUS-BRA database (two classes/benign and malignant) for training and testing and the unseen BUSI database (two classes/benign and malignant) for testing. Extensive experiments have recorded accuracy and AUC as performance parameters. The results indicate that the proposed system outperforms the existing breast cancer detection algorithms reported in the literature. AUC values of 1.00 are calculated for VGG16 and EfficientNet-B7 in the dilation cases. The proposed approach will facilitate this challenging and time-consuming classification task.

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Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography

Guo

Li²,

Gao³

et al. 2022

Front. Oncol.

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ObjectiveTo assess the significance of peritumoral features based on deep learning in classifying non-spiculated and noncalcified masses (NSNCM) on mammography.MethodsWe retrospectively screened the digital mammography data of 2254 patients who underwent surgery for breast lesions in Harbin Medical University Cancer Hospital from January to December 2018. Deep learning and radiomics models were constructed. The classification efficacy in ROI and patient levels of AUC, accuracy, sensitivity, and specificity were compared. Stratified analysis was conducted to analyze the influence of primary factors on the AUC of the deep learning model. The image filter and CAM were used to visualize the radiomics and depth features.ResultsFor 1298 included patients, 771 (59.4%) were benign, and 527 (40.6%) were malignant. The best model was the deep learning combined model (2 mm), in which the AUC was 0.884 (P < 0.05); especially the AUC of breast composition B reached 0.941. All the deep learning models were superior to the radiomics models (P < 0.05), and the class activation map (CAM) showed a high expression of signals around the tumor of the deep learning model. The deep learning model achieved higher AUC for large size, age >60 years, and breast composition type B (P < 0.05).ConclusionCombining the tumoral and peritumoral features resulted in better identification of malignant NSNCM on mammography, and the performance of the deep learning model exceeded the radiomics model. Age, tumor size, and the breast composition type are essential for diagnosis.

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Relation of peritumoral, prepectoral and diffuse edema with histopathologic findings of breast cancer in preoperative 3T magnetic resonance imaging

Cited by 5 publications

References 22 publications

Prognostic Implication of Focal Breast Edema on Preoperative Breast Magnetic Resonance Imaging in Breast Cancer Patients

Prognostic Implication of Focal Breast Edema on Preoperative Breast Magnetic Resonance Imaging in Breast Cancer Patients

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography

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