Cistos No Interior De Nódulo Mamários Benignos: Risco De Malignidade

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The objective of this study is to assess whether the largest cyst diameter is useful for BI-RADS ultrasonography classification of predominantly solid breast masses with an oval shape, circumscribed margins, and largest axis parallel to the skin, which, except for the cystic component, would be likely classified as benign. Methods This study received approval fromthe local institutional review board. From March 2009 to August 2014, we prospectively biopsied 170 breast masses from 164 women. We grouped the largest cyst and mass diameters according to histopathological diagnoses. We used Student’s t-test, linear regression, and the area under the receiver operating characteristic curve (AUC) for statistical assessment. Results Histopathological examination revealed 143 (84%) benign and 27 (16%)malignant masses. The mean largest mass diameter was larger among malignant (mean ± standard deviation, 34.1 ± 16.6 mm) than benign masses (24.7 ± 16.7 mm) (P < 0.008). The mean largest cyst diameter was also larger among malignant (9.9 ± 7.1 mm) than benign masses (4.6 ± 3.6 mm) (P < 0.001). Agreement between measurements of the largest mass and cyst diameters was low (R2 = 0.26). AUC for the largest cyst diameter (0.78) was similar to the AUC for the largest mass diameter (0.69) (p = 0.2). A largest cyst diameter < 3, ≥ 3 to < 11, and ≥ 11 mm had a positive predictive value of 0, 15, and 52%, respectively. Conclusion A largest cystic component < 3 mm identified within breast masses that show favorable characteristics may be considered clinically inconsequential in ultrasonography characterization. Conversely, masses with a largest cystic component ≥ 3 mm should be classified as BI-RADS-US category 4. © 2016 by Thieme Publicações Ltda, Rio de Janeiro, Brazil.3841701762012/15059-8, FAPESP, Fundação de Amparo à Pesquisa do Estado de São PauloFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Simple Rules For Ultrasonographic Subcategorization Of Bi-rads ®-us 4 Breast Masses

Objectives To evaluate an objective method for ultrasonographic (US) subcategorization of BI-RADS®-US 4 breast masses based on clear and simple rules in order for woman to benefit from a more complete and homogeneous breast mass analysis. Methods In this cross-sectional study, we selected 330 women, with 339 US breast masses, classified as BI-RADS ®-US 4. Three physicians experienced in breast imaging independently reviewed all US images, assessing mass shape, margins, orientation, echo texture and vascularity. These experts further subdivided the masses into subcategories 4a, 4b and 4c, according to simple US rules. Inter-observer agreement was calculated for US features categories and for final subcategory assessment. We also estimated the positive predictive value (PPV) for BI-RADS®-US subcategories 4a, 4b and 4c assigned by each of the three observers. Results Pathological examination of all masses confirmed 144 (42%) malignant and 195 (58%) benign tumors. Moderate agreement was obtained for mass shape, margins, vascularity and for final BI-RADS®-US 4 subcategory. Substantial agreement was obtained for the description of mass orientation and echo texture. The PPV for subcategories 4a, 4b and 4c were, 17%, 45% and 85%, respectively, for the first observer and 20%, 38% and 79% and 17%, 40% and 85% for the other two observers. Conclusion Standardization of a US subcategorization of BI-RADS®-US 4 breast masses seems to be feasible, with substantial inter-observer agreement and progressive increase in the PPV in the subcategories 4a, 4b and 4c, provided that clear and simple classification rules are defined. © 2013 Elsevier Ireland Ltd.82812311235Chala, L., Endo, E., Kim, S., Gray-scale sonography of solid breast masses diagnosis of probably benign masses and reduction of the number of biopsies (2007) Journal of Clinical Ultrasound, 35 (1), pp. 9-19Lazarus, E., Mainiero, M.B., Schepps, B., BI-RADS®-US Lexicon for US and mammography (2006) Interobserver Variability and Positive Predictive Value Radiology, 239, pp. 385-391Mendelson, E.B., Baum, J.K., Berg, W.A., BI-RADS®-US: Ultrasound (2003) Breast Imaging Reporting and Data System: ACR BI- RADS - Breast Imaging Atlas, , C.J. D'Orsi, E.B. Mendelson, D.M. Ikeda, 1st edition American College of Radiology Reston, VAOrel, S.G., Kay, N., Reynolds, C., BI-RADS®-US categorization as a predictor of malignancy (1999) Radiology, 211, pp. 845-850Kim, E.K., Ko, K.H., Oh, K.K., Clinical application of the BI-RADS final assessment to breast sonography in conjunction with mammography (2008) AJR. American Journal of Roentgenology, 190, pp. 1209-1215Lai, X.J., Zhu, Q.L., Jiang, Y.X., Inter-observer variability in Breast Imaging Reporting and Data System (BI-RADS®-US) ultrasound final assessments (2011) European Journal of Radiology, , 10.1016/j.ejrad.2011.04.069Park, C.S., Lee, J.H., Yim, H.W., Observer agreement using the ACR Breast Imaging Reporting and Data System (BI-RADS®-US)-ultrasound, First Edition (2003) (2007) Korean Journal of Radiology, 8 (5), pp. 397-402Abdullah, N., Mesurolle, B., El-Khoury, M., Breast imaging reporting and data system lexicon for US: Interobserver agreement for assessment of breast masses (2009) Radiology, 252 (3), pp. 665-672Kundel, H.L., Polansky, M., Measurement of observer agreement (2003) Radiology, 228, pp. 303-308. , AUGUST 2Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data (1977) Biometrics, 33 (1), pp. 159-174Berg, W.A., Blume, J.D., Cormack, J.B., Mendelson, E.B., Training the ACRIN 6666 investigators and effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS ultrasound feature analysis (2012) AJR. American Journal of Roentgenology, 199, pp. 224-235. , JULY 1Lee, H.J., Kim, E.K., Kim, M.J., Youk, J.H., Lee, J.Y., Kang, D.R., Oh, K.K., Observer variability of Breast Imaging Reporting and Data System (BI-RADS) for breast ultrasound (2008) European Journal of Radiology, 65, pp. 293-298. , FEBRUARY 2Tozaki, M., Fukuma, E., Does power Doppler ultrasonography improve the BI-RADS category assessment and diagnostic accuracy of solid breast lesions? (2011) Acta Radiologica, 52, pp. 706-710. , SEPTEMBER 7Raza, S., Goldkamp, A.L., Chikarmane, S.A., US of breast masses categorized as BI-RADS®-US 3, 4, and 5: Pictorial review of factors influencing clinical management (2010) Radiographics, 30 (5), pp. 1199-1213Yoon, J.H., Kim, M.J., Moon, H.J., Kwak, J.Y., Kim, E.K., Subcategorization of ultrasonographic BI-RADS category 4: Positive predictive value and clinical factors affecting it (2011) Ultrasound in Medicine and Biology, 37, pp. 693-699. , MAY

Polycystic Ovary Syndrome And Chronic Autoimmune Thyroiditis

Introduction: Polycystic ovary syndrome (PCOS) has been associated with an autoimmune origin, either per se or favoring the onset of autoimmune diseases, from a stimulatory action on the inflammatory response. Thus, autoimmune thyroiditis (AIT) could be more prevalent among women with PCOS. Objective: To evaluate the prevalence of AIT in women with PCOS. Study design: It was a cross-sectional study, in a tertiary center, including 65 women with PCOS and 65 women without this condition. Clinical and laboratory parameters were evaluated and a thyroid ultrasound scan was performed. Levels of thyroid-stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), anti-thyroid peroxidase (anti-TPO) antibodies, anti-thyroglobulin (anti-TG) antibodies, and thyroid ultrasound findings were evaluated. Results: The prevalence of subclinical hypothyroidism (SCH) in women with PCOS was 16.9% and 6.2% in the non-PCOS group. AIT was more common in the PCOS group compared with the non-PCOS group (43.1% versus 26.2%). But, when it was adjusted by weight and insulin resistance, the difference in the thyroiditis risk was not observed (OR 0.78, CI 0.28-2.16). Conclusion: AIT risk was similar in the PCOS and the non-PCOS group. SCH are more common in women with PCOS, highlighting a need for periodic monitoring of thyroid function.3114851Dunaif, A., Insulin resistance and the polycystic ovary syndrome: Mechanism and implications for pathogenesis (1997) Endocr Rev, 18, pp. 774-800Gleicher, N., Barad, D., Weghofer, A., Functional autoantibodies, a new paradigm in autoimmunity? (2007) Autoimmun Rev, 7, pp. 42-45Mueller, A., Schöfl, C., Dittrich, R., Thyroid-stimulating hormone is associated with insulin resistance independently of body mass index and age in women with polycystic ovary syndrome (2009) Hum Reprod, 24, pp. 2924-2930Janssen, O.E., Mehlmauer, N., Hahn, S., High prevalence of autoimmune thyroiditis in patients with polycystic ovary syndrome (2004) Eur J Endocrinol, 150, pp. 363-369Benetti-Pinto, C.L., Berini Piccolo, V.R., Garmes, H.M., Teatin Juliato, C.R., Subclinical hypothyroidism in young women with polycystic ovary syndrome: An analysis of clinical, hormonal, and metabolic parameters (2013) Fertil Steril, 99, pp. 588-592Cooper, D.S., Biondi, B., Subclinical thyroid disease (2012) Lancet, 379, pp. 1142-1154Dayan, C.M., Daniels, G.H., Chronic autoimmune thyroiditis (1996) N Engl J Med, 335, pp. 99-107Garelli, S., Masiero, S., Plebani, M., High prevalence of chronic thyroiditis in patients with polycystic ovary syndrome (2013) Eur J Obstet Gynecol Reprod Biol, 169, pp. 248-251Anaforoglu, I., Topbas, M., Algun, E., Relative associations of polycystic ovarian syndrome vs metabolic syndrome with thyroid function, volume, nodularity and autoimmunity (2011) J Endocrinol Invest, 34, pp. e259-e264Kachuei, M., Jafari, F., Kachuei, A., Keshteli, A.H., Prevalence of autoimmune thyroiditis in patients with polycystic ovary syndrome (2012) Arch Gynecol Obstet, 285, pp. 853-856Ganie, M.A., Marwaha, R.K., Aggarwal, R., Singh, S., High prevalence of polycystic ovary syndrome characteristics in girls with euthyroid chronic lymphocytic thyroiditis: A case-control study (2010) Eur J Endocrinol, 162, pp. 1117-1122Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (pcos (2004) Hum Reprod, 19, pp. 41-47. , Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop GroupTreloar, A.E., Boynton, R.E., Behn, B.G., Brown, B.W., Variation of the human menstrual cycle through reproductive life (1967) Int J Fertil, 12, pp. 77-126Archer, J.S., Chang, R.J., Hirsutism and acne in polycystic ovary syndrome (2004) Best Pract Res Clin Obstet Gynaecol, 18, pp. 737-754Babson, A.L., The immulite automated immunoassay system (1991) J Clin Immunoassay, 14, pp. 83-88Matthews, D.R., Hosker, J.P., Rudenski, A.S., Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man (1985) Diabetologia, 28, pp. 412-419Surks, M.I., Ortiz, E., Daniels, G.H., Subclinical thyroid disease: Scientific review and guidelines for diagnosiSand management (2004) Jama, 291, pp. 228-238Pedersen, O.M., Aardal, N.P., Larssen, T.B., The value of ultrasonography in predicting autoimmune thyroid disease (2000) Thyroid, 10, pp. 251-259Sheth, S., Role of ultrasonography in thyroid disease (2010) Otolaryngol Clin North Am, 43, pp. 239-255Pocock, S.J., Clinical trials with multiple outcomes: A statistical perspective on their design, analysis, and interpretation (1997) Control Clin Trials, 18, pp. 530-545Ganie, M.A., Laway, B.A., Wani, T.A., Association of subclinical hypothyroidism and phenotype, insulin resistance, and lipid parameters in young women with polycystic ovary syndrome (2011) Fertil Steril, 95, pp. 2039-2043Petríková, J., Lazúrová, I., Ovarian failure and polycystic ovary syndrome (2012) Autoimmun Rev, 11, pp. A471-A478Petríková, J., Lazúrová, I., Yehuda, S., Polycystic ovary syndrome and autoimmunity (2010) Eur J Intern Med, 21, pp. 369-37