Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging

Hoffmann, Rudolf; Reich, Christoph; Skerl, Katrin

doi:10.1007/s11548-022-02737-6

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Preliminary data show that artificial intelligence can be used to optimize the interpretation of the data produced by ultrasound and SWE. [33][34][35][36][37] Recently published data showed sensitivity as well as specificity beyond 90% respectively when analyzing B-mode ultrasound images and SWE by neuronal network models.…”

Section: Discussionmentioning

confidence: 99%

Intra‐ and Interobserver Reliability of Shear Wave Elastography in Breast Cancer Diagnosis

Togawa,

Pfob,

Büsch

et al. 2023

J of Ultrasound Medicine

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ObjectivesShear wave elastography (SWE) is increasingly used in breast cancer diagnostics. However, large, prospective, multicenter data evaluating the reliability of SWE is missing. We evaluated the intra‐ and interobserver reliability of SWE in patients with breast lesions categorized as BIRADS 3 or 4.MethodsWe used data of 1288 women at 12 institutions in 7 countries with breast lesions categorized as BIRADS 3 to 4 who underwent conventional B‐mode ultrasound and SWE. 1243 (96.5%) women had three repetitive conventional B‐mode ultrasounds as well as SWE measurements performed by a board‐certified senior physician. 375 of 1288 (29.1%) women received an additional ultrasound examination with B‐mode and SWE by a second physician. Intraclass correlation coefficients (ICC) were calculated to examine intra‐ and interobserver reliability.ResultsICC for intraobserver reliability showed an excellent correlation with ICC >0.9, while interobserver reliability was moderate with ICC of 0.7. There were no clinically significant differences in intraobserver reliability when SWE was performed in lesions categorized as BI‐RADS 3 or 4 as well as in histopathologically benign or malignant lesions.ConclusionReliability of additional SWE was evaluated on a study cohort consisting of 1288 breast lesions categorized as BI‐RADS 3 and 4. SWE shows an excellent intraobserver reliability and a moderate interobserver reliability in the evaluation of solid breast masses.

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Section: Discussionmentioning

confidence: 99%

Intra‐ and Interobserver Reliability of Shear Wave Elastography in Breast Cancer Diagnosis

Togawa,

Pfob,

Büsch

et al. 2023

J of Ultrasound Medicine

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“…91 The current review extracted and organized the data in tabular form and summarized the application of US images in breast cancer diagnosis (Table 2). 6,8,80,83,90,92–118…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

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Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

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“…Early detection of breast cancer has moved into the very focus of primary health care. Breast ultrasonography (US) is an invaluable tool for the detection of breast lesions [1][2][3]. The standardized lexicon for breast US was published in 2013 by the American College of Radiology [4].…”

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confidence: 99%

The Value of VTTQ Combined with B-mode US for Distinguishing Benign from Malignant Breast Masses by Comparing with SE: A Clinical Research

Lujing,

Zuofeng

2023

Advanced Ultrasound in Diagnosis and Therapy

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The purpose of this study was to compare the diagnostic performance of virtual touch tissue quantification (VTTQ) combined with B-mode ultrasonograpgy (US), strain elastography (SE) combined with B-mode US and B-mode US alone in differentiating the properties of breast lesions.Methods: A retrospective database was queried for 283 healthy subjects and 100 consecutive patients with 130 breast lesions.All the cases were examined by B-mode US, VTTQ and SE. Histological diagnosis was used as the reference standard. The area under the receiver operating curve (AUC) values of each data set was compared.Results: Twenty-two lesions were determined as malignant and 108 as benign. The best cutoff point of VTTQ was 7.82 m/s. The AUC of B-mode US combined with VTTQ or SE was greater than that of B-mode US alone (0.913 or 0.918 vs. 0.797) (P = 0.007 and 0.012). Conclusion:Both VTTQ and SE could give help to B-mode US in distinguishing benign from malignant breast lesions about elastography values. There was no difference between them.

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Evaluating different combination methods to analyse ultrasound and shear wave elastography images automatically through discriminative convolutional neural network in breast cancer imaging

Cited by 3 publications

References 11 publications

Intra‐ and Interobserver Reliability of Shear Wave Elastography in Breast Cancer Diagnosis

Intra‐ and Interobserver Reliability of Shear Wave Elastography in Breast Cancer Diagnosis

Machine learning and new insights for breast cancer diagnosis

The Value of VTTQ Combined with B-mode US for Distinguishing Benign from Malignant Breast Masses by Comparing with SE: A Clinical Research

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