Nilai Diagnostik USG Gray Scale, Color Doppler, dan Strain Elastografi dalam Menentukan Keganasan Tumor Payudara
The study’s aim is to analize the diagnostic values of Gray Scale ultrasound, Color Doppler and strain Elastography determining malignancy of breast tumor. The research was conducted at the Radiology Division of Wahidin Sudirohusodo and Hasanuddin University hospital in Makassar from February to March 2018 by using the diagnostic value. There were 51 research samples who had the clinical symptom of breast nodule. The examination of ultrasound gray scale, color doppler and strain elastography was performed to assess the breast nodules and determine malignancy and benign based on the classification of each examination. The results were based on USG breast and the three were compared. The comparative standard used was histopathological examinations as a gold and the data were analized using the Chi-Square test.
The research results indicated that based on the histopatology examination from 51 samples, 18 samples (35,3%) had malignant tumor, and 33 samples (64,7%) had benign tumor. The gray scale revealed sensitivity of 94,4% and spesifisity of 81,8%, whereas usg color doppler based on vascular distribution revealed the sensitivity of 77,8% and the spesificity 93,9%, based on vascular amount of sensitivity 72,2% and spesificity 93,9% and resistance index sensitivity 89,4% and spesificity 87,5% with cut off value for malignant tumor ≥0,495 in 17 samples (81%), whereas benign has a resistance value index <0,495 in 28 samples (93,3%). As for the strain elastography based on Tsukuba scores, sensitivity was 94,4%, spesificity was 81,8%and based on strain ratio sensitivity was 94,4%, spesificity was 90,9% with cut off value for malignant tumor of ≥2,63, whereas benign tumor of <2,63. The highest sensitivity and spesificity were found in the examination of strain elastography based on strain ratio ,which were 94,4% dan 90,9% with the value of Area Under Curve of 0,906 with the cut-off point of 2,63.
Copyright (c) 2017 Febie Irsandy Syahruddin, Mirna Muis, Bachtiar Murtala
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