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: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.
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Project description:PurposeTo assess the impact of adjunctive ultrasound guided diffuse optical tomography (US-guided DOT) on BI-RADS assessment in women undergoing US-guided breast biopsy.MethodThis prospective study enrolled women referred for US-guided breast biopsy between 3/5/2019 and 3/19/2020. Participants underwent US-guided DOT immediately before biopsy. The US-guided DOT acquisition generated average maximum total hemoglobin (HbT) spatial maps and quantitative HbT values. Four radiologists blinded to histopathology assessed conventional imaging (CI) to assign a CI BI-RADS assessment and then integrated DOT information in assigning a CI&DOT BI-RADS assessment. HbT was compared between benign and malignant lesions using an ANOVA test and Tukey's test. Benign biopsies were tabulated, deeming BI-RADS ≥ 4A as positive. Reader agreement was assessed.ResultsAmong 61 included women (mean age 48 years), biopsy demonstrated 15 (24.6%) malignant and 46 (75.4%) benign lesions. Mean HbT was 55.3 ± 22.6 µM in benign lesions versus 85.4 ± 15.6 µM in cancers (p < .001). HbT threshold of 78.5 µM achieved sensitivity 80% (12/15) and specificity 89% (41/46) for malignancy. Across readers and patients, 197 pairs of CI BI-RADS and CI&DOT BI-RADS assessments were assigned. Adjunctive US-guided DOT achieved a net decrease in 23.5% (31/132) of suspicious (CI BI-RADS ≥ 4A) assessments of benign lesions (34 correct downgrades and 3 incorrect upgrades). 38.3% (31/81) of 4A assessments were appropriately downgraded. No cancer was downgraded to a non-actionable assessment. Interreader agreement analysis demonstrated kappa = 0.48-0.53 for CI BI-RADS and kappa = 0.28-0.44 for CI&DOT BI-RADS.ConclusionsIntegration of US-guided DOT information achieved a 23.5% reduction in suspicious BI-RADS assessments for benign lesions. Larger studies are warranted, with attention to improved reader agreement.
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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.
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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.
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