Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound.
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ABSTRACT: Background
This study prospectively investigates the agreement between radial (r-US) and meander-like (m-US) breast ultrasound with regard to lesion location, lesion size, morphological characteristics and final BI-RADS classification of individual breast lesions.Methods
Each patient of a consecutive, unselected, mixed collective received a dual ultrasound examination.Results
The agreement between r-US and m-US for lesion location ranged from good (lesion to mammilla distance ICC 0.64; lesion to skin distance ICC 0.72) to substantial (clock-face localization κ 0.70). For lesion size the agreement was good (diameter ICC 0.72; volume ICC 0.69), for lesion margin and architectural distortion it was substantial (κ 0.68 and 0.70, respectively). Most importantly, there was a substantial agreement (κ 0.76) in the final BI-RADS classification between r-US and m-US.Conclusions
Our recent comparison of radial and meander-like breast US revealed that the diagnostic accuracy of the two scanning methods was comparable. In this study, we observe a high degree of agreement between m-US and r-US for the lesion description (location, size, morphology) and final BI-RADS classification. These findings corroborate that r-US is a suitable alternative to m-US in daily clinical practice. Trial registration NCT02358837. Registered January 2015, retrospectively registered https://clinicaltrials.gov/ct2/results?cond=&term=NCT02358837&cntry=&state=&city=&dist =.
SUBMITTER: Brasier-Lutz P
PROVIDER: S-EPMC8220682 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveQualification tasks in mammography and breast ultrasound were developed for the American College of Radiology Imaging Network (ACRIN) 6666 Investigators. We sought to assess the effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS feature analysis among a subset of these experienced observers.Materials and methodsAfter a 1-hour didactic session on BI-RADS: Ultrasound, an interpretive skills quiz set of 70 orthogonal sets of breast ultrasound images including 25 (36%) malignancies was presented to 100 experienced breast imaging observers. Thirty-five observers reviewed the quiz set twice: first without and then with immediate feedback of consensus feature analysis, management recommendations, and pathologic truth. Observer performance (sensitivity, specificity, area under the curve [AUC]) was calculated without feedback and with feedback. Kappas were determined for agreement on feature analysis and assessments.ResultsFor 35 observers without feedback, the mean sensitivity was 89% (range, 68-100%); specificity, 62% (range, 42-82%); and AUC, 82% (range, 73-89%). With feedback, the mean sensitivity was 93% (range, 80-100%; mean increase, 4%; range of increase, 0-12%; p < 0.0001), the mean specificity was 61% (range, 45-73%; mean decrease, 1%; range of change, -18% to 11%; p = 0.19), and the mean AUC was 84% (range, 78-90%; mean increase, 2%; range of change, -3% to 9%; p < 0.0001). Three breast imagers in the lowest quartile of initial performance showed the greatest improvement in sensitivity with no change or improvement in AUC. The kappa values for feature analysis did not change, but there was improved agreement about final assessments, with the kappa value increasing from 0.53 (SE, 0.02) without feedback to 0.59 (SE, 0.02) with feedback (p < 0.0001).ConclusionMost experienced breast imagers showed excellent breast ultrasound interpretive skills. Immediate feedback of consensus BI-RADS: Ultrasound features and histopathologic results improved performance in ultrasound interpretation across all experience variables.
| S-EPMC3891891 | biostudies-literature
Project description:BACKGROUND: The aim of this study was to assess the performance of shear wave elastography combined with BI-RADS classification of greyscale ultrasound images for benign/malignant differentiation in a large group of patients. METHODS: One hundred and seventy-five consecutive patients with solid breast masses on routine ultrasonography undergoing percutaneous biopsy had the greyscale findings classified according to the American College of Radiology BI-RADS. The mean elasticity values from four shear wave images were obtained. RESULTS: For mean elasticity vs greyscale BI-RADS, the performance results against histology were sensitivity: 95% vs 95%, specificity: 77% vs 69%, Positive Predictive Value (PPV): 88% vs 84%, Negative Predictive Value (NPV): 90% vs 91%, and accuracy: 89% vs 86% (all P>0.05). The results for the combination (positive result from either modality counted as malignant) were sensitivity 100%, specificity 61%, PPV 82%, NPV 100%, and accuracy 86%. The combination of BI-RADS greyscale and shear wave elastography yielded superior sensitivity to BI-RADS alone (P=0.03) or shear wave alone (P=0.03). The NPV was superior in combination compared with either alone (BI-RADS P=0.01 and shear wave P=0.02). CONCLUSION: Together, BI-RADS assessment of greyscale ultrasound images and shear wave ultrasound elastography are extremely sensitive for detection of malignancy.
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Project description:Radiomics reflects the texture and morphological features of tumours by quantitatively analysing the grey values of medical images. We aim to develop a nomogram incorporating radiomics and the Breast Imaging Reporting and Data System (BI-RADS) for predicting breast cancer in BI-RADS ultrasound (US) category 4 or 5 lesions. From January 2017 to August 2018, a total of 315 pathologically proven breast lesions were included. Patients from the study population were divided into a training group (n = 211) and a validation group (n = 104) according to a cut-off date of March 1st, 2018. Each lesion was assigned a category (4A, 4B, 4C or 5) according to the second edition of the American College of Radiology (ACR) BI-RADS US. A radiomics score was generated from the US image. A nomogram was developed based on the results of multivariate regression analysis from the training group. Discrimination, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed in the validation group. The radiomics score included 9 selected radiomics features. The radiomics score and BI-RADS category were independently associated with breast malignancy. The nomogram incorporating the radiomics score and BI-RADS category showed better discrimination (area under the receiver operating characteristic curve [AUC]: 0.928; 95% confidence interval [CI]: 0.876, 0.980) between malignant and benign lesions than either the radiomics score (P = 0.029) or BI-RADS category (P = 0.011). The nomogram demonstrated good calibration and clinical usefulness. In conclusion, the nomogram combining the radiomics score and BI-RADS category is potentially useful for predicting breast malignancy in BI-RADS US category 4 or 5 lesions.
| S-EPMC6695380 | biostudies-literature
Project description:BackgroundUltrasound elastography (UE) is a novel imaging method. The purpose of this study was to determine the utility of Virtual Touch tissue imaging in the evaluation of solid breast lesions.Patients and methods209 breast solid lesions in 192 patients that had been evaluated using ultrasound (US) and UE were reviewed and analyzed.ResultsThe sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for UE, US, and US plus UE in the differentiation of malignant from benign breast lesions were 80.8, 75.6, 77.9, 73.1, and 82.8% for UE, 87.2, 86.1, 86.6, 83.7, and 89.2% for US, and 92.5, 86.9, 89.5, 85.3, and 93.4% for US plus UE. There were significant differences between UE and US plus UE (all p < 0.05). Except for accuracy, there were no significant differences between UE and US or US and US plus UE (all p > 0.05). The area under the curve obtained from the ROC curve for UE, US, and US plus UE in differentiating malignant from benign lesions was 0.845, 0.884, and 0.922, respectively.ConclusionThe UE scoring system is not superior to BI-RADS in the sonographic evaluation of solid breast lesions. Combined use may improve the performance.
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Project description:PurposeIncreased levels of stromal tumor-infiltrating lymphocytes (sTILs) have recently been considered a favorable independent prognostic and predictive biomarker in triple-negative breast cancer (TNBC). The purpose of this study was to determine the relationship between BI-RADS (Breast Imaging Reporting and Data System) ultrasound lexicon descriptors and sTILs in TNBC.Materials and methodsPatients with stage I-III TNBC were evaluated within a single-institution neoadjuvant clinical trial. Two fellowship-trained breast radiologists used the BI-RADS ultrasound lexicon to assess pretreatment tumor shape, margin, echo pattern, orientation, posterior features, and vascularity. sTILs were defined as low <20 or high ≥20 on the pretreatment biopsy. Fisher's exact tests were used to assess the association between lexicon descriptors and sTIL levels.ResultsThe 284 patients (mean age 52 years, range 24-79 years) were comprised of 68% (193/284) with low-sTIL tumors and 32% (91/284) with high-sTIL tumors. TNBC tumors with high sTILs were more likely to have the following features: (1) oval/round shape than irregular shape (p = 0.003), (2) circumscribed or microlobulated margins than spiculated, indistinct, or angular margins (p = 0.0005); (3) complex cystic and solid pattern than heterogeneous pattern (p = 0.006); and (4) posterior enhancement than shadowing (p = 0.002). There was no significant association between sTILs and descriptors for orientation and vascularity (p = 0.06 and p = 0.49, respectively).ConclusionBI-RADS ultrasound descriptors of the pretreatment appearance of a TNBC tumor can be useful in discriminating between tumors with low and high sTIL levels. Therefore, there is a potential use of ultrasound tumor characteristics to complement sTILs when used as stratification factors in treatment algorithms for TNBC.
| S-EPMC8755852 | biostudies-literature
Project description:Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P?=?0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.
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Project description:Background Classification of lung cancer screening CT scans depends on measurement of lung nodule size. Information about interobserver agreement is limited. Purpose To assess interobserver agreement in the measurements and American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung cancer screening using manual measurements of average diameter and computer-aided semiautomated measurements of average diameter and volume (CT volumetry). Materials and Methods Two radiologists and one radiology resident retrospectively measured lung nodules from screening CT scans obtained between September 2016 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical setting. Average manual diameter and semiautomated computer-aided diameter and volume measurements were converted to the corresponding Lung-RADS categories. Interobserver agreement in raw measurements was assessed using intraclass correlation and Bland-Altman indexes, and interobserver agreement in Lung-RADS classification was assessed using bi-rater κ. Results One hundred twenty patients (mean age, 63 years ± 6 [standard deviation]; 67 women) were evaluated. All manual, semiautomated diameter, and semiautomated volume measurements were obtained by all three readers in 120 of 147 nodules (82%). Intraclass correlation coefficients were greater than or equal to 0.95 for all reader pairs using all measurement methods and were highest using volumetry. Bias and 95% limits of agreement for average diameter were smaller with semiautomated measurements than with manual measurements. κ values across all Lung-RADS classifications were greater than or equal to 0.81, with the lowest being for manual measurements and the highest being for volumetric measurements. Forty-three of 120 (36%) of the nodules were classified into a lower Lung-RADS category on the basis of volumetry compared with using manual diameter measurements by at least one reader, whereas the reverse occurred for four of 120 (3%) of the nodules. Conclusion Interobserver agreement was high with manual diameter measurements and increased with semiautomated CT volumetric measurements. Semiautomated CT volumetry enabled classification of more nodules into lower Lung CT Screening Reporting and Data System categories than manual or semiautomated diameter measurements. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.
| S-EPMC7706890 | biostudies-literature
Project description:ObjectiveTo evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures.MethodsIn this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured. The influence of the Big Five personality traits was analyzed using the Pearson correlation.ResultsDiagnostic accuracy was significantly improved by AI-based assistance (an increase of 2.8% ± 2.3%, 95 %-CI 1.5 to 4.0 %, p = 0.045) and trust in the algorithm was generated primarily by the certainty of the prediction (100% of participants). Different human-AI interactions were observed ranging from nearly no interaction to humanization of the algorithm. High scores in neuroticism were correlated with higher persuasibility (Pearson's r = 0.98, p = 0.02), while higher consciousness and change of accuracy showed an inverse correlation (Pearson's r = -0.96, p = 0.04).ConclusionTrust in the algorithm's performance was mostly dependent on the certainty of the predictions in combination with a plausible heatmap. Human-AI interaction varied widely and was influenced by personality traits.Key points• AI-based assistance significantly improved the diagnostic accuracy of radiologists in classifying BI-RADS 4 mammography lesions. • Trust in the algorithm's performance was mostly dependent on the certainty of the prediction in combination with a reasonable heatmap. • Personality traits seem to influence human-AI collaboration. Radiologists with specific personality traits were more likely to change their classification according to the algorithm's prediction than others.
| S-EPMC9935738 | biostudies-literature
Project description:OBJECTIVES:To determine the applicability of a computer-aided diagnostic system strain elastography system for the classification of breast masses diagnosed by ultrasound and scored using the criteria proposed by the breast imaging and reporting data system ultrasound lexicon and to determine the diagnostic accuracy and interobserver variability. METHODS:This prospective study was conducted between March 1, 2016, and May 30, 2016. A total of 83 breast masses subjected to percutaneous biopsy were included. Ultrasound elastography images before biopsy were interpreted by 3 radiologists with and without the aid of computer-aided diagnostic system for strain elastography. The parameters evaluated by each radiologist results were sensitivity, specificity, and diagnostic accuracy, with and without computer-aided diagnostic system for strain elastography. Interobserver variability was assessed using a weighted ? test and an intraclass correlation coefficient. The areas under the receiver operating characteristic curves were also calculated. RESULTS:The areas under the receiver operating characteristic curve were 0.835, 0.801, and 0.765 for readers 1, 2, and 3, respectively, without computer-aided diagnostic system for strain elastography, and 0.900, 0.926, and 0.868, respectively, with computer-aided diagnostic system for strain elastography. The intraclass correlation coefficient between the 3 readers was 0.6713 without computer-aided diagnostic system for strain elastography and 0.811 with computer-aided diagnostic system for strain elastography. CONCLUSION:The proposed computer-aided diagnostic system for strain elastography system has the potential to improve the diagnostic performance of radiologists in breast examination using ultrasound associated with elastography.
| S-EPMC5882047 | biostudies-literature
Project description:PURPOSE:The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer. MATERIALS AND METHODS:This retrospective, single-institution analysis of 219 patients involved two cohorts using one of two FDA approved transcriptome-based tests that were performed as part of the clinical care of breast cancer patients at Harbor-UCLA Medical Center between April 2008 and January 2013. BI-RADS descriptive terminology was collected from the corresponding ultrasound reports for each patient in conjunction with transcriptomic test results. Recursive partitioning and regression trees were used to test and validate classification of the two cohorts. RESULTS:The area under the curve (AUC) of the receiver operator curves (ROC) for the regression classifier between the two FDA approved tests and ultrasound features were 0.77 and 0.65, respectively; they employed the 'margins', 'retrotumoral', and 'internal echoes' feature descriptors. Notably, the 'retrotumoral' and mass 'margins' features were used in both classification trees. The identification of sonographic correlates of gene tests provides added value to the ultrasound exam without incurring additional procedures or testing. CONCLUSIONS:The predictive capability using structured language from diagnostic ultrasound reports (BI-RADS) was moderate for the two tests, and provides added value from ultrasound imaging without incurring any additional costs. Incorporation of additional measures, such as ultrasound contrast enhancement, with validation in larger, prospective studies may further substantiate these results and potentially demonstrate even greater predictive utility.
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