Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions

Park, Chang Suk; Kim, Sung Hun; Jung, Na Young; Choi, Jae Jung; Kang, Bong Joo; Jung, Hoon

doi:10.1007/s12282-013-0465-3

Cited by 49 publications

(36 citation statements)

References 13 publications

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“…SWE is differentiated from conventional static elastography in automatically induced mechanical vibration via the radiation force of US beams (1,2,7,8,22). So, it less affected by the individual compression skills than static elastography (1,2,7,8,22).…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have reported intra-and interobserver reproducibility of SWE (7,9,22). Cosgrove et al (9) reported that intraobserver reliability for maximum and mean elasticity was almost perfect (ICC = 0.84 and 0.87) and was substantial for the ratio of mass-to-fat elasticity (ICC = 0.77).…”

Section: Discussionmentioning

confidence: 99%

“…The shear wave carries information associated with the local viscoelastic properties of the tissue, facilitating a quantitative approach to the tissue (2,7,8). Shear waves move faster in stiffer area and slowly in softer area (9).…”

Section: Intra-and Interobserver Reproducibility Of Shear Wave Elastomentioning

confidence: 99%

“…Moreover, static elastography is easily affected by the operator' s compression power, resulting in observer variability (7,8). To be a reliable diagnostic method, elastography must not be easily affected by operators or interpreters.…”

Section: Introductionmentioning

confidence: 99%

“…In SWE, estimation of the velocity of shear wave is involved in tissue elasticity imaging (1,(9)(10)(11)(12). The mechanical vibration is remote- ly produced by using acoustic radiation force produced by a focused US beam (1,7,8). The radiation force rearranges tissue, and the rearrangement causes a transient shear wave (2,8,13).…”

Section: Introductionmentioning

confidence: 99%

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Intra- and Interobserver Reproducibility of Shear Wave Elastography for Evaluation of the Breast Lesions

Hong

Kim

2017

J Korean Soc Radiol

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Purpose: To evaluate reproducibility of shear wave elastography (SWE) for breast lesions within and between observers and compare the reproducibility of SWE features. Materials and Methods: For intraobserver reproducibility, 225 masses with 208 patients were included; and two consecutive SWE images were acquired by each observer. For interobserver reproducibility, SWE images of the same mass were obtained by another observer before surgery in 40 patients. Intraclass correlation coefficients (ICC) were used to determine intra-and interobserver reproducibility. Results: Intraobserver reliability for mean elasticity (Emean) and maximum elasticity (Emax) were excellent (ICC = 0.803, 0.799). ICC for SWE ratio and minimum elasticity (Emin) were fair to good (ICC = 0.703, 0.539). Emean showed excellent ICC regardless of histopathologic type and tumor size. Emax, SWE ratio and Emin represented excellent or fair to good reproducibility based on histopathologic type and tumor size. In interobserver study, ICC for Emean, Emax and SWE ratio were excellent. Emean, Emax and SWE ratio represented excellent ICC irrespective of histopathologic type. ICC for Emean was excellent regardless of tumor size. SWE ratio and Emax showed fair to good interobserver reproducibility based on tumor size. Emin represented poor interobserver reliability. Conclusion: Emean in SWE was highly reproducible within and between observers. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Index terms

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Intra-and Interobserver Reproducibility Of Shear Wave Elastomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intra- and Interobserver Reproducibility of Shear Wave Elastography for Evaluation of the Breast Lesions

Hong

Kim

2017

J Korean Soc Radiol

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CR‐SSL: A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation

Mishra

Roy

Bandyopadhyay

et al. 2021

Int J Imaging Syst Tech

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Breast ultrasound (BUS) tumor segmentation can help with the early detection of breast cancer; however, the lack of human-labeled training data is a big problem in this area. To deal with this, we propose a novel self-supervised learning approach (CR-SSL), where several related pretext tasks like unsupervised segmentation and edge detection are firstly learned, followed by the target tumor segmentation based fine-tuning. Learning such related pretext tasks ensures better representation learning. Experimental study shows that CR-SSL can improve the mean Dice and Jaccard scores by more than 4-5%, with ≥0.6 scores while having access to only 20-50 human-labeled training samples. The best Dice scores of 0.7303 and 0.8207, and Jaccard scores of 0.7082 and 0.8015 are obtained by CR-SSL on the test datasets. Compared with the No-SSL baseline, CR-SSL can achieve (10-20)% improvements in segmentation quality while working in small-sized training dataset scenarios, suggesting its high potential practical utility.

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The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

Lyu,

Zhang,

Zhang

et al. 2023

J of Clinical Ultrasound

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BackgroundFemale breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging.ObjectiveThe purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC.Methods302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray‐scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad‐score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model.ResultsWhen we selected 10 radiomics features to calculate the rad‐score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value.ConclusionThe radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

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Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions

Abstract: Elasticity score showed a higher level of interobserver agreement for the diagnosis of breast lesions than BI-RADS lexicons and final category.

Cited by 49 publications

References 13 publications

Intra- and Interobserver Reproducibility of Shear Wave Elastography for Evaluation of the Breast Lesions

Intra- and Interobserver Reproducibility of Shear Wave Elastography for Evaluation of the Breast Lesions

CR‐SSL: A closely related self‐supervised learning based approach for improving breast ultrasound tumor segmentation

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

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