Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging

Cheon, Hyejin; Kim, Hye Jung; Kim, Tae Hun; Ryeom, Hunkyu; Lee, Jongmin; Kim, Gab Chul; Yuk, Jin‐Sung; Kim, Won Hwa

doi:10.1148/radiol.2017171157

Cited by 101 publications

(95 citation statements)

References 33 publications

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“…Several studies have demonstrated that the enhancement patterns of tumor-adjacent parenchyma in DCE-MRI were associated with chemotherapy response (14), local recurrence (15), and survival (16) in breast cancer. In a recent study (40) the grade of peritumoral edema identified in breast MRI has been independently associated with disease recurrence. In study by Zhou et al (41), it was demonstrated that the peritumoral stiffness assessed by ultrasound elastography of malignant breast lesions was higher than that of benign lesions.…”

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

confidence: 98%

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

confidence: 98%

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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“…However, mucinous carcinoma, necrotic cancer, and metaplastic carcinoma can have high signal intensity on T2-weighted images (2). T2-weighted imaging also allows the depiction of perifocal or prepectoral edema within the breast, which improves lesion classification (lesions with edema are more often malignant) and is a poor prognostic sign in patients with known breast cancer (37,38). Several studies have reported that T2-weighted imaging increases the specificity for differentiation of benign and malignant lesions (39,40).…”

Section: T2-weighted Imagingmentioning

confidence: 99%

Breast MRI: State of the Art

Mann¹,

Cho²,

Moy³

2019

Radiology

516

325

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reast MRI is an indispensable modality, along with mammography and US. Its main indications are staging of known cancer, screening for breast cancer in women at increased risk, and evaluation of response to neoadjuvant chemotherapy (1-3). As opposed to mammography and US, MRI is a functional technique. Heywang et al (4) and Kaiser and Zeitler (5) independently introduced this technique in the 1980s. Contrast material-enhanced MRI evaluates the permeability of blood vessels by using an intravenous contrast agent (gadolinium chelate) that shortens the local T1 time, leading to a higher signal on T1-weighted images (6). The underlying principle is that neoangiogenesis leads to formation of leaky vessels that allow for faster extravasation of contrast agents (7), thus leading to rapid local enhancement. Despite improvements in the technique of breast MRI, this principle is still the basis of all clinical MRI protocols. However, most MRI protocols nowadays are multiparametric (8,9). This review describes the current state of the art in breast MRI, with a focus on the major indications and the potential indication-based adaptations to the imaging protocol to maximize its value. Requirements for Breast MRI Breast MRI studies should be interpreted by radiologists with expertise in breast imaging, including mammographic and US studies, as these examinations are often complementary. Although empirical data on the learning curve for breast MRI are lacking, some studies showed improved performance of radiologists over time (10), and reporting breast MRI studies requires sufficient exposure to the technique. It is best practice to use a field strength of at least 1.5 T to acquire images at a sufficiently high spatial resolution (1-3). Utilization of a dedicated breast coil is mandatory to obtain images of diagnostic quality. Women lie in the prone position with the breasts hanging free in the recesses of the coil. This design allows the breast tissue to spread, which facilitates detection of abnormalities and prevents motion artifacts induced by respiration (11,12). A breast coil should have at least four channels, but modern designs

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“…L YMPHOVASCULAR INVASION (LVI), defined as the infiltration of tumor cells into lymphatic or blood vessels at the periphery of the invasive carcinoma, 1 has been widely recognized as a negative prognostic factor in invasive breast cancer. [2][3][4][5][6] Studies have revealed that LVI status facilitates the selection of an optimal therapeutic strategy for individual breast cancer patients. [7][8][9][10][11] The extent of LVI is an important factor for predicting neoadjuvant chemotherapy (NAC) efficacy.…”

mentioning

confidence: 99%

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics

Liu

Bao

et al. 2019

Magnetic Resonance Imaging

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Background Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection. Purpose To explore the use of dynamic contrast‐enhanced (DCE)‐magnetic resonance imaging (MRI)‐based radiomics for preoperative prediction of LVI in invasive breast cancer. Study Type Prospective. Population Ninety training cohort patients (22 LVI‐positive and 68 LVI‐negative) and 59 validation cohort patients (22 LVI‐positive and 37 LVI‐negative) were enrolled. Field Strength/Sequence 1.5 T and 3.0 T, T1‐weighted DCE‐MRI. Assessment Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE‐MRI. A radiomics signature was constructed in the training cohort with 10‐fold cross‐validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort. Statistical Tests Mann–Whitney U‐test, chi‐square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). Results Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone. Data Conclusion The DCE‐MRI‐based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847–857.

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Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging

Cited by 101 publications

References 33 publications

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

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

Breast MRI: State of the Art

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics

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