Stiffness of the surrounding tissue of breast lesions evaluated by ultrasound elastography

Zhou, Jianqiao; Zhan, Wei; Dong, Yijie; Yang, Zhifang; Zhou, Chun

doi:10.1007/s00330-014-3152-7

Cited by 43 publications

(42 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These cut off points yielded the highest sensitivities of 83.3, 77.7 and 95.8% for UE, MRI and UE+MRI, respectively. Previous studies reported similar results, observing that the sensitivity of UE ranged between 87.1 and 95.0% in the diagnosis of breast cancer (19,21). Furthermore, the present study revealed that the accuracy rates of differential diagnosis were 82.5, 75.5 and 95.3% for UE, MRI and UE+MRI, respectively.…”

Section: Discussionsupporting

confidence: 63%

“…MRI is another noninvasive diagnostic tool that is useful in distinguishing soft tissue from other tissues. Therefore, the combination of UE and MRI may be more beneficial to identify the likelihood of benign or malignant breast lesions based on the hardness of the lesions (18,19). As a novel and effective imaging tool, the combination of UE and MRI methods may significantly improve diagnostic accuracy and be more objective.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of the diagnostic efficacy between ultrasound elastography and magnetic resonance imaging for breast masses

Cheng¹,

Li²,

Ji³

et al. 2017

Exp Ther Med

View full text Add to dashboard Cite

Abstract. The present study compared the efficacy of ultrasound elastography (UE), magnetic resonance imaging (MRI) and the combination of the two methods (UE+MRI) in the differential diagnosis of benign and malignant breast tumors. In total, 86 patients with breast masses were recruited and evaluated by UE, MRI and UE+MRI. Strain ratios of UE were calculated for the breast mass and adjacent normal tissues. In addition, the receiver operating characteristic (ROC) curve was obtained, while the sensitivity and specificity were calculated to determine the optimal cut-off point for the differential diagnosis. The area under the ROC curve (AUC) was also calculated to evaluate the diagnostic performance of these methods. The results indicated that the diagnostic accuracy of UE+MRI was significantly higher compared with the UE or MRI methods in the differential diagnosis of invasive ductal, invasive lobular, intraductal papillary, medullary and mucinous carcinomas (all P<0.05). The optimal cut-off points of ROC curve of the Strain Ratio in the diagnosis of breast lesions were 2.81, 3.76 and 3.42 for UE, MRI and UE+MRI, respectively. Furthermore, the AUC values were 86.7, 79.2 and 91.4%, while the diagnostic accuracy rates were 82.5, 75.5 and 95.3%, for UE, MRI and UE+MRI, respectively. Accuracy rate differences between UE and MRI or between UE and UE+MRI were statistically significant (P<0.05), whereas no significant difference existed between MRI and UE+MRI (P>0.05). Finally, the diagnostic consistency of the UE+MRI method with the pathological diagnosis was higher compared with UE or MRI alone. In conclusion, the combination of UE and MRI is superior to the use of UE or MRI alone in the differential diagnosis of benign and malignant breast masses.

show abstract

Section: Discussionsupporting

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Comparison of the diagnostic efficacy between ultrasound elastography and magnetic resonance imaging for breast masses

Cheng¹,

Li²,

Ji³

et al. 2017

Exp Ther Med

View full text Add to dashboard Cite

show abstract

“…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. A 2017 study (17) was the first attempt to extract radiomics features from both intratumoral and peritumoral regions in breast DCE-MRI, where the features successfully predicted the pathological complete response to neoadjuvant chemotherapy.…”

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

confidence: 97%

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

View full text Add to dashboard Cite

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.

show abstract

“…Ultrasonography has become an important tool in neoplasm evaluation and, when combined with mammography, can aid in the diagnosis, differentiation, and prediction of malignancy in mammary tumors in human medicine [3,4,5]. …”

Section: Introductionmentioning

confidence: 99%

Ultrasonography methods for predicting malignancy in canine mammary tumors

et al. 2017

View full text Add to dashboard Cite

The aim of this study was to evaluate and compare the efficacy of B-mode, Doppler, contrast-enhanced ultrasonography (CEUS), and Acoustic Radiation Force Impulse (ARFI) elastography in predicting malignancy in canine mammary masses. This was a prospective cohort study from 2014 to 2016, which included 153 bitches with one or more mammary masses. A total of 300 masses were evaluated by ultrasonography (B-mode, Doppler, CEUS, and ARFI) and subsequently classified as benign or malignant by histopathology. Each ultrasound parameters studied were compared between benign and malignant masses by Chi-square or Student tests and differences were considered significant when P < 0.01. For the variables that proved significant differences were estimated the cut-off point, sensitivity, specificity, accuracy, and area under curve (AUC) by receiver-operating characteristic curve (ROC) analysis in a logistic regression model using histopathological classification as reference, to assess and compare diagnostic performance of each technique. Out of 300 mammary masses evaluated 246 were classified as malignant and 54 as benign. B-mode measurements showed sensitivity 67.9%, and specificity 67.6% as malignancy predictors on canine mammary masses; Doppler indexes systolic (>21.2 m/s) and diastolic velocity (>4.8 m/s) sensitivity 79.2% and specificity 70.8%; CEUS wash-out time (<80.5 s) sensitivity 80.2% and specificity 16.7%; and ARFI elastography shear velocity (SWV > 2.57 m/s) sensitivity 94.7% and specificity 97.2% In conclusion B-mode and Doppler ultrasound evaluations may assist in malignancy prediction of canine mammary masses with moderate sensitivity and specificity, already the SWV was an great accurate predictor. Therefore, ARFI elastography exam inclusion in veterinary clinic oncology and research is highly recommended, since it allows fast, non-invasive, and complication-free malignancy prediction of canine mammary masses.

show abstract

Stiffness of the surrounding tissue of breast lesions evaluated by ultrasound elastography

Abstract: • Stiffness of the surrounding tissue of malignant breast lesions was increased. • Stiffness of the surrounding tissue correlated with stiffness of breast lesions. • Stiffness of the surrounding tissue has potential use in diagnosis of breast lesions.

Cited by 43 publications

References 61 publications

Comparison of the diagnostic efficacy between ultrasound elastography and magnetic resonance imaging for breast masses

Comparison of the diagnostic efficacy between ultrasound elastography and magnetic resonance imaging for breast masses

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

Ultrasonography methods for predicting malignancy in canine mammary tumors

Contact Info

Product

Resources

About