کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6169869 | 1251182 | 2014 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
One-step nucleic acid amplification assay for intraoperative prediction of non-sentinel lymph node metastasis in breast cancer patients with sentinel lymph node metastasis
ترجمه فارسی عنوان
تست آمپلیفیک اسید نوکلئیک تک مرحله ای برای پیشگیری از عمل جراحی متاستاز گره لنفاوی غیرواقعی در بیماران مبتلا به سرطان پستان با متاستاز گره لنفاوی حامل
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موضوعات مرتبط
علوم پزشکی و سلامت
پزشکی و دندانپزشکی
زنان، زایمان و بهداشت زنان
چکیده انگلیسی
The aim of the present study was to construct the intra-operative prediction model of non-sentinel lymph node (non-SLN) metastasis in breast cancer patients with SLN metastasis using one-step nucleic acid amplification (OSNA). Of 833 breast cancer patients (T1-T2, N0) who underwent SLN biopsy and had their SLNs examined intra-operatively with the OSNA assay, 161 with SLN metastasis and treated with completion axillary lymph node dissection (cALND) were randomly divided into a training (n = 81) and a validation (n = 80) cohort. Non-SLN metastasis of the training cohort was associated with the number of positive SLNs (P = 0.001), CK19 mRNA copy number (P = 0.001), and clinical tumor size (P = 0.055). These parameters were used to construct the intra-operative prediction model of non-SLN metastasis. Its diagnostic accuracy (AUC of ROC curve) was 0.809 and 0.704 for the training and validation cohorts, respectively. The intra-operative prediction model using OSNA may have a diagnostic accuracy of non-SLN metastasis comparable to that of the conventional, post-operative prediction model, indicating that it might help decide the indication for cALND.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: The Breast - Volume 23, Issue 5, October 2014, Pages 579-585
Journal: The Breast - Volume 23, Issue 5, October 2014, Pages 579-585
نویسندگان
Atsuko Teramoto, Kenzo Shimazu, Yasuto Naoi, Atsushi Shimomura, Masafumi Shimoda, Naofumi Kagara, Naomi Maruyama, Seung Jin Kim, Katsuhide Yoshidome, Masahiko Tsujimoto, Yasuhiro Tamaki, Shinzaburo Noguchi,