The diagnostic value of ultrasound and shear wave elastography in the differentiation of benign and malignant soft tissue tumors

Öztürk, Mesut; Selçuk, Mustafa Bekir; Polat, Ahmet Veysel; Ozbalci, Aysu Basak; Barış, Yakup Sancar

doi:10.1007/s00256-020-03492-y

Cited by 16 publications

(13 citation statements)

References 30 publications

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“…Dasgeb et al found similar results to our study that all benign lesions had strain ratios ≤3.0, whereas all malignant lesions had strain ratios ≥3.9 [18]. In a study with SWE [19], the sensitivity of the detection rate of malignant soft tissue lesions was 91.9%, specificity was 72.2%, whereas in our study, the sensitivity was 91%, specificity was 89%. Quantitative analysis demonstrated that specificity in the diagnosis of malignant lesions was higher in our study.…”

Section: Discussionsupporting

confidence: 89%

Differentiation of benign and malignant superficial soft tissue lesions using real-time strain elastography

ANNAÇ,

CANYİĞİT,

TAN

et al. 2021

Turk J Med Sci

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To evaluate benign and malignant cutaneous-subcutaneous lesions using realtime strain elastography (RTSE) and to compare the findings with histopathologic results.Material Methods: Over a period of 10 months, 72 patients (38 with benign and 34 with malignant cutaneous and subcutaneous lesions) were prospectively included in this study. Elasticity patterns and strain ratios were examined for each lesion. Lesions were evaluated in 4 groups as yellow-red (soft; pattern-1), green-yellow (moderate; pattern-2), blue-green (hard; pattern-3) and blue (hardest; pattern-4). The stiffness of the lesions was displayed with strain ratios by comparing of a nearby reference tissue. The recorded images were compared with histopathologic findings.Results: On sonoelastograms, considering patterns 1-2 as benign and patterns 3-4 as malignant, the sensitivity, specificity, and positive and negative predictive values for the differentiation of malignant from benign lesions were 100%, 68.5%, 74%, and 100%, respectively. Considering a cut-off value of the strain ratio as >3.05, the sensitivity, specificity, and positive and negative predictive values were 91%, 89%, 88%, and 92%, respectively. The Area Under the Curve (AUC: 0.972) showed the excellent ability of strain elastography to differentiate benign and malignant lesions.Conclusions: RTSE is an important imaging tool to differentiate benign and malignant superficial soft tissue lesions. Our results suggest that RTSE can be used to predict malignancy since malignant lesions are more confidentially diagnosed than benign superficial soft tissue lesions on elastograms.

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Section: Discussionsupporting

confidence: 89%

Differentiation of benign and malignant superficial soft tissue lesions using real-time strain elastography

ANNAÇ,

CANYİĞİT,

TAN

et al. 2021

Turk J Med Sci

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“…Again, unlike the PNTs, the cervical carcinomas have unclear boundaries as well as an irregular shape. Regarding further studies differentiating benign and malignant tumor with ultrasound SWE, Ozturk et al [ 19 ] and Zhong [ 20 ] investigated SWE in breast lesions. Benign breast lesions showed a mean SWV of 3.30 m/s, whereas malignant lesions had a mean SWV of 2.87 m/s [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Regarding further studies differentiating benign and malignant tumor with ultrasound SWE, Ozturk et al [ 19 ] and Zhong [ 20 ] investigated SWE in breast lesions. Benign breast lesions showed a mean SWV of 3.30 m/s, whereas malignant lesions had a mean SWV of 2.87 m/s [ 19 ]. Whether or not the mean calculated SWV lies on the lower end of the spectrum or the higher end is still questionable, as there are simply not enough large-scale SWE studies in benign homogenous tumors to facilitate comparison.…”

Section: Discussionmentioning

confidence: 99%

Shear-Wave-Elastography in Neurofibromatosis Type I

et al. 2022

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Ultrasound shear wave elastography (SWE) is an increasingly used imaging modality that expands clinical ultrasound by measuring the elasticity of various tissues, such as the altered elasticity of tumors. Peripheral nerve tumors are rare, have been well-characterized by B-mode-ultrasound, but have not yet been investigated with SWE. Given the lack of studies, a first step would be to investigate homogeneous peripheral nerve tumors (PNTs), histologically neurofibromas or schwannomas, which can occur in multiple in neurofibromatosis type 1 and 2 (NF1 and 2), respectively. Hence, we measured shear wave velocity (SWV) in 30 PNTs of 11 patients with NF1 within the median nerve. The SWV in PNTs ranged between 2.8 ± 0.8 m/s and correlated with their width and approximate volume but not with their length or height. Furthermore, we determined the extent to which PNTs alter the SWV of the median nerve for three positions of the wrist joint: neutral (zero-degree), individual maximal flexion and maximal extension. Here, SWV was decreased in NF1 patients compared to age- and sex-matched controls (p = 0.029) during maximal wrist extension. We speculate that the presence of PNTs may have a biomechanical impact on peripheral nerves which has not been demonstrated yet.

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“…Medical imaging is commonly used to visualize tumor dissemination and quantify the severity, providing valuable information for diagnosis, staging and treatment decision [3] with satisfactory diagnostic accuracy. For example, the sensitivity and specificity of contrast-enhanced ultrasound (CEUS), multidetector computed tomography (MDCT), magnetic resonance imaging (MRI), and fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT in the detection of colorectal cancer liver metastasis was 80–97% [4] , which is similar in other diseases [ 5 , 6 ]. However, owing to the uncoordinated ratio of doctors to patients and the difficulty of radiological diagnosis, making a correct and timely diagnosis from medical imaging is challenging [7] .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

et al. 2021

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Background Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. Methods We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. Findings We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79–84%), specificity of 84% (82–87%) and AUC of 0·90 (0·87–0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83–90%) for machine learning and 86% (82–89%) for deep learning, and a pooled specificity of 89% (82–93%) for machine learning, and 87% (82–91%) for deep learning. Interpretation AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. Funding College students' innovative entrepreneurial training plan program .

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The diagnostic value of ultrasound and shear wave elastography in the differentiation of benign and malignant soft tissue tumors

Cited by 16 publications

References 30 publications

Differentiation of benign and malignant superficial soft tissue lesions using real-time strain elastography

Differentiation of benign and malignant superficial soft tissue lesions using real-time strain elastography

Shear-Wave-Elastography in Neurofibromatosis Type I

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

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