Shear wave versus strain elastography in the differentiation of benign and malignant breast lesions

Gürüf, Aykut; Öztürk, Mesut; Bayrak, İlkay Koray; Polat, Ahmet Veysel

doi:10.3906/sag-1905-15

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…Malignant tumors have been reported to be harder in consistency than benign tumors, notably breast and thyroid carcinomas [14]. The abundance of stromal brous reaction in some carcinomas, for example in breast carcinoma, papillary thyroid carcinoma or pancreatic adenocarcinoma, or the high cell density in some malignant tumors such as prostate and lung carcinomas, combined with the strong attachment of malignant cells to surrounding tumors, may explain this nding [15].…”

Section: Discussionmentioning

confidence: 99%

Performance of Sonoelastography for Predicting Malignancy in soft Tissue

Bodard

Lassalle

Larousserie

et al. 2022

Preprint

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Background: Separating benign from malignant soft-tissue masses often requires a biopsy. The objective of this study was to assess whether shear-wave elastography (SWE) helped to separate benign from malignant soft-tissue masses. Methods: In 2015–2016, we prospectively included patients with soft-tissue masses deemed by our multidisciplinary sarcoma board to require a diagnostic biopsy. All patients underwent ultrasonography (US) followed by SWE to measure elasticity. We compared benign and malignant tumors, overall and after separating tumors with vs. without a fatty component. The biopsy findings, and surgical-specimen histology when available, served as the reference standard. Results: We included 136 patients, 99 with non-fatty and 37 with fatty soft-tissue masses. Mean elasticity and T/F values were significantly lower for the benign than the malignant soft-tissue masses in the overall cohort (30.9 vs. 50.0 kPa, P=0.03; and 2.55 vs. 4.30, P=0.046) and in the non-fatty subgroup (37.8±31.9 vs. 58.9±39.1 kPa, P=0.049 and 2.89±5.25 vs. 5.07±5.41, P=0.046). Data for fatty tumors were non relevant due to lack of conclusive results. By receiver operating characteristics curve analysis, a T/F cutoff of 3.5 had 46% sensitivity and 84% specificity for separating benign and malignant soft-tissue masses. Conclusions: SWE had good specificity for separating benign from malignant soft-tissue masses.

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Section: Discussionmentioning

confidence: 99%

Performance of Sonoelastography for Predicting Malignancy in soft Tissue

Bodard

Lassalle

Larousserie

et al. 2022

Preprint

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“…US and MRI often fail to confidently separate benign from malignant in soft-tissue masses. Malignant tumors have been reported to be harder in consistency than benign tumors, notably breast and thyroid carcinomas [ 20 ]. The abundance of stromal fibrous reaction in some carcinomas, for example in breast carcinoma, papillary thyroid carcinoma or pancreatic adenocarcinoma, or the high cell density in some malignant tumors such as prostate and lung carcinomas, combined with the strong attachment of malignant cells to surrounding tumors, may explain this finding [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Performance of Sonoelastography for predicting malignancy in soft tissue

et al. 2022

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Background Separating benign from malignant soft-tissue masses often requires a biopsy. The objective of this study was to assess whether shear-wave elastography (SWE) helped to separate benign from malignant soft-tissue masses. Methods In 2015–2016, we prospectively included patients with soft-tissue masses deemed by our multidisciplinary sarcoma board to require a diagnostic biopsy. All patients underwent ultrasonography (US) followed by SWE to measure elasticity. We compared benign and malignant tumors, overall and after separating tumors with vs. without a fatty component. The biopsy findings, and surgical-specimen histology when available, served as the reference standard. Results We included 136 patients, 99 with non-fatty and 37 with fatty soft-tissue masses. Mean elasticity and tumor-to-fat elasticity ratio (T/F) values were significantly lower for the benign than the malignant soft-tissue masses in the overall cohort (30.9 vs. 50.0 kilopascals (kPa), P = 0.03; and 2.55 vs. 4.30, P = 0.046) and in the non-fatty subgroup (37.8 ± 31.9 vs. 58.9 ± 39.1 kPa, P = 0.049 and 2.89 ± 5.25 vs. 5.07 ± 5.41, P = 0.046). Data for fatty tumors were non relevant due to lack of conclusive results. By receiver operating characteristics curve analysis, a T/F cutoff of 3.5 had 46% sensitivity and 84% specificity for separating benign and malignant soft-tissue masses. Conclusions SWE had good specificity and poor sensitivity for separating benign from malignant soft-tissue masses.

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“…Therefore, a delayed diagnosis may result in an undesirable outcome during the treatment of patients [11][12][13]. The American College of Radiology (ACR) created the BI-RADS to diminish variation in the radiologists' descriptions of findings used for diagnosis [14].…”

Section: Introductionmentioning

confidence: 99%

Automated classification of BI-RADS in textual mammography reports

Boroumandzadeh¹,

Parvinnia²

2021

Turk J Elec Eng & Comp Sci

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The main purpose of this paper is to process key information in medical text records and also classify patients, per different levels of Breast Imaging-Reporting and Data system (BI-RADS). The BI-RADS is a scheme for the standardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reports supported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction from textual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosing keywords from medical text reports. Word2vec and Term Frequency Inverse Document Frequency (TFIDF) techniques are used for extracting features, finally, they're combined with the Hospital Information System (HIS) reports and called With-HIS. The different classifiers like multi-class Support Vector Machine (SVM), Naïve Bayesian (NB), Extreme Gradient Boosting (XGBoost), and Multi-Level Fuzzy Min-Max Neural Network (MLF) are used so as to compare the accuracy of With-HIS and Without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach (Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within the proposed method with MLF classifier is 0.89% but without-HIS is 0.85%.

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Shear wave versus strain elastography in the differentiation of benign and malignant breast lesions

Cited by 17 publications

References 26 publications

Performance of Sonoelastography for Predicting Malignancy in soft Tissue

Performance of Sonoelastography for Predicting Malignancy in soft Tissue

Performance of Sonoelastography for predicting malignancy in soft tissue

Automated classification of BI-RADS in textual mammography reports

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