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Arch Gynecol Obstet ; 289(3): 595-600, 2014 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-23995674

RESUMEN

OBJECTIVE: We wish to develop a CT scan-based scoring system which estimates the probability of adnexal mass malignancy. METHODS: Patients (324) undergoing adnexal mass surgery were recruited into the study from June 1, 2002, to January 1, 2009. All study patients had a preoperative CT scan and serum CA-125 test. CT scan abnormalities included any solid tumor components, ascites, and pelvic or abdominal lymphadenopathy and omental caking. RESULTS: There were 225 (70%) benign and 99 (30%) malignant ovarian masses. Using logistic regression with the area under the curve of the receiver operating curve of 82%, the cancer probability was determined by the equation. e(-3.6372+0.0306*(A)+0.001*(C)+1.551*(D)+1.7377*(E)+2.76*(F)) / 1+e(-3.6372+0.0306*(A)+0.001*(B)+0.876*(C)+1.551*(D)+1.7377*(E)+2.76*(F)) where A = age, B = CA-125, C = solid adnexal mass is 1 and cystic is 0, D = ascites is 1, E = omental caking is 1 and absence is 0, F = node size ≥1 cm is 1 and <1 cm is 0 value. The natural logarithm e is a constant [2.718281828]. For example, for a woman of age 60, CA-125 = 50 U/mL, with solid adnexal mass, ascites, omental caking, and lymphadenopathy, the probability is 0.994. Hence, this woman has a 99.4% probability of having cancer. CONCLUSION: The computed tomography adnexal mass score combines CT scan findings, CA-125, and patient age into an equation to predict the malignant probability of an adnexal mass.


Asunto(s)
Neoplasias Ováricas/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Enfermedades de los Anexos/diagnóstico por imagen , Enfermedades de los Anexos/patología , Enfermedades de los Anexos/cirugía , Adolescente , Adulto , Anciano , Antígeno Ca-125/sangre , Medios de Contraste , Diagnóstico Diferencial , Femenino , Humanos , Persona de Mediana Edad , Neoplasias Ováricas/patología , Neoplasias Ováricas/cirugía , Valor Predictivo de las Pruebas , Probabilidad , Medición de Riesgo , Sensibilidad y Especificidad
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