Breast elastography: diagnostic performance of computer-aided diagnosis software and interobserver agreement.

OBJECTIVE: To determine the best cutoff value for classifying breast masses by ultrasound elastography, using dedicated software for strain elastography, and to determine the level of interobserver agreement. MATERIALS AND METHODS: We enrolled 83 patients with 83 breast masses identified on ultrasound and referred for biopsy. After B-mode ultrasound examination, the lesions were manually segmented by three radiologists with varying degrees of experience in breast imaging, designated reader 1 (R1, with 15 years), reader 2 (R2, with 2 years), and reader 3 (R3, with 8 years). Elastography was performed automatically on the best image with computer-aided diagnosis (CAD) software. Cutoff values of 70%, 75%, 80%, and 90% of hard areas were applied for determining the performance of the CAD software. The best cutoff value for the most experienced radiologists was then compared with the visual assessment. Interobserver agreement for the best cutoff value was determined, as were the interclass correlation coefficient and concordance among the radiologists for the areas segmented. RESULTS: The best cutoff value of the proportion of hard area within a breast mass, for experienced radiologists, was found to be 75%. At a cutoff value of 75%, the interobserver agreement was excellent between R1 and R2, as well as between R1 and R3, and good between R2 and R3. The interclass concordance coefficient among the three radiologists was 0.950. When assessing the segmented areas by size, we found that the level of agreement was higher among the more experienced radiologists. CONCLUSION: The best cutoff value for a quantitative CAD system to classify breast masses was 75%.

Assessment of intima-media thickness of the carotid artery and intraluminal diameter of the brachial artery as cardiovascular risk markers in Brazilian adolescents with overweight or obesity.

Background The intima-media thickness of the carotid artery (cIMT) and endothelial dysfunction are associated with cardiovascular (CV) disease. Objectives To evaluate the correlation between cIMT, brachial intraluminal diameter and flow-mediated vasodilation on the reactive hyperemia phase in adolescents with obesity with predictors of CV risk. Methods Seventy-three pubertal patients with overweight or obesity were evaluated (45 girls) with a mean (standard deviation [SD]) age of 12.9 (2.5) years. Patients underwent anthropometric measurements and had the lipid profile, oral glucose tolerance test (oGTT) and serum intercellular adhesion molecule-1 (sICAM-1) levels analyzed. The ratios of the waist circumference (WC)/height (WHtR) and triglycerides (TG)/high-density lipoprotein cholesterol (HDL-C), homeostatic model assessment of insulin resistance (HOMA-IR), the Matsuda index and insulin area under the curve (AUC) were calculated. All patients were evaluated for cIMT and arterial blood flow velocity of the brachial artery. Results 75.3% of the patients had high cIMT values. We found a positive correlation between WHtR and cIMT (r = 0.233; p = 0.050). There was a positive correlation between sICAM-1 and insulin AUC (r = 0.323; p = 0.012) and WHtR (r = 0.258; p = 0.047). Patients with abnormal arterial dilation had higher sICAM-1 values (p = 0.02) despite having smaller WHtR (p = 0.046). Conclusions These adolescents with obesity had high cIMT values. Insulin resistance was associated with sICAM-1. Endothelial dysfunction was positively correlated with sICAM-1. There is no consensus about what the best laboratorial approach to evaluate insulin resistance in adolescents is, and the cutoff values of each method are arbitrary. So, as we saw earlier, the association between anthropometric data (WHtR) and ultrasound findings could be useful to evaluate the CV risk of these adolescents with obesity, because of its practical, direct and low-cost value.

Breast elastography: diagnostic performance of computer-aided diagnosis software and interobserver agreement / Elastografia mamária: desempenho diagnóstico de um software de sistema de diagnóstico assistido por computador e concordância interobservador

Abstract Objective: To determine the best cutoff value for classifying breast masses by ultrasound elastography, using dedicated software for strain elastography, and to determine the level of interobserver agreement. Materials and Methods: We enrolled 83 patients with 83 breast masses identified on ultrasound and referred for biopsy. After B-mode ultrasound examination, the lesions were manually segmented by three radiologists with varying degrees of experience in breast imaging, designated reader 1 (R1, with 15 years), reader 2 (R2, with 2 years), and reader 3 (R3, with 8 years). Elastography was performed automatically on the best image with computer-aided diagnosis (CAD) software. Cutoff values of 70%, 75%, 80%, and 90% of hard areas were applied for determining the performance of the CAD software. The best cutoff value for the most experienced radiologists was then compared with the visual assessment. Interobserver agreement for the best cutoff value was determined, as were the interclass correlation coefficient and concordance among the radiologists for the areas segmented. Results: The best cutoff value of the proportion of hard area within a breast mass, for experienced radiologists, was found to be 75%. At a cutoff value of 75%, the interobserver agreement was excellent between R1 and R2, as well as between R1 and R3, and good between R2 and R3. The interclass concordance coefficient among the three radiologists was 0.950. When assessing the segmented areas by size, we found that the level of agreement was higher among the more experienced radiologists. Conclusion: The best cutoff value for a quantitative CAD system to classify breast masses was 75%.

Resumo Objetivo: Determinar o melhor valor de corte para classificar os nódulos mamários pela elastografia por ultrassom, usando um software dedicado para elastografia por deformação, e determinar o nível de concordância interobservadores. Materiais e Métodos: Foram incluídos no estudo 83 pacientes com 83 massas mamárias identificadas no ultrassom e encaminhados para biópsia. Após o exame ultrassonográfico no modo B, as lesões foram manualmente segmentadas por três radiologistas com diferentes graus de experiência em imagem da mama: leitor 1 (R1, com 15 anos de experiência), leitor 2 (R2, com 2 anos de experiência) e leitor 3 (R3, com 8 anos de experiência). A classificação pela elastografia foi realizada automaticamente com base na melhor imagem com o software diagnóstico auxiliado por computador (DAC). Valores de corte de 70%, 75%, 80% e 90% das áreas duras foram aplicados para determinar o desempenho do software DAC. O melhor valor de corte para os radiologistas foi comparado com a avaliação visual. A concordância interobservadores para o melhor valor de corte foi determinada, assim como o coeficiente de correlação interclasses e a concordância entre os radiologistas para as áreas segmentadas. Resultados: O melhor valor de corte da proporção de área dura dentro de um nódulo mamário foi de 75% para os radiologistas mais experientes. Com um valor de corte de 75%, a concordância interobservadores foi excelente entre R1 e R2 e entre R1 e R3, e boa entre R2 e R3. O coeficiente de concordância interclasses entre os três radiologistas foi de 0,950. Ao avaliar as áreas segmentadas por tamanho, constatamos que o nível de concordância foi maior entre os radiologistas mais experientes. Conclusão: O melhor valor de corte para um sistema quantitativo de DAC para classificar as massas mamárias foi de 75%.

Evaluation of a Computer-Aided Diagnosis System in the Classification of Lesions in Breast Strain Elastography Imaging.

Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant and 52 benign lesions. A radiologist who was blinded to the diagnosis performed the visual analysis of the lesions. After six months with no eye contact on the breast images, the same radiologist and other two radiologists manually drew the contour of the lesions in B-mode ultrasound, which was masked in the elastography image. In order to measure the amount of hard tissue in a lesion, we developed a CAD system able to identify the amount of hard tissue, represented by red color, and quantify its predominance in a lesion, allowing classification as soft, intermediate, or hard. The data obtained with the CAD system were compared with the visual analysis. We calculated the sensitivity, specificity, and area under the curve (AUC) for the classification using the CAD system from the manual delineation of the contour by each radiologist. Results: The performance of the CAD system for the most experienced radiologist achieved sensitivity of 70.97%, specificity of 88.46%, and AUC of 0.853. The system presented better performance compared with his visual diagnosis, whose sensitivity, specificity, and AUC were 61.29%, 88.46%, and 0.829, respectively. The system obtained sensitivity, specificity, and AUC of 67.70%, 84.60%, and 0.783, respectively, for images segmented by Radiologist 2, and 51.60%, 92.30%, and 0.771, respectively, for those segmented by the Resident. The intra-class correlation coefficient was 0.748. The inter-observer agreement of the CAD system with the different contours was good in all comparisons. Conclusions: The proposed CAD system can improve the radiologist performance for classifying breast masses, with excellent inter-observer agreement. It could be a promising tool for clinical use.

The Feasibility of Classifying Breast Masses Using a Computer-Assisted Diagnosis (CAD) System Based on Ultrasound Elastography and BI-RADS Lexicon.

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 κ 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.

Appearance of breast masses on sonoelastography with special focus on the diagnosis of fibroadenomas.

The aim of this study was to show and correlate the imaging features of breast masses, especially fibroadenomas, using sonoelastography. Two hundred thirty-five patients with 302 breast lesions referred for core needle biopsy participated in the study. All lesions appearing as solid masses on conventional US were included. Out of the included lesions (270), 115 (42.6%) corresponded to histologically confirmed fibroadenomas and 155 (57.4%) to lesions with histologically confirmed diagnoses other than fibroadenomas. These were further subdivided into fibrocystic changes, lesions with low malignancy potential, and malignant lesions. Fibroadenomas were also divided according to histological presentation into three subgroups to allow comparative study based on elastographic scores. All lesions were classified using a four-point scoring system based on ultrasound elastography imaging characteristics. Different presentations were observed for elastographic scores according to histological presentation of fibroadenomas, whereby fibroadenomas with benign characteristics tended to have elastographic classification similar to fibrocystic changes, and complex and hypercellular fibroadenomas had classifications similar to harder lesions. Fibroadenomas are generally classified as category 3 in the BI-RADS lexicon and are the most commonly found lesions in breast biopsies. Sonoelastography can provide additional information to conventional studies and be used as an auxiliary tool in assessing these masses in clinical practice.