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RISS 인기검색어
- 검색키워드
- 저자 : Subathra Sabaratnam (검색결과 2 건)
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Khoo, Joon-Joon,Ng, Chen-Siew,Sabaratnam, Subathra,Arulanantham, Sarojah Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.3
Background: Examination of sentinel lymph node (SLN) biopsies provides accurate nodal staging for breast cancer and plays a key role in patient management. Procurement of SLNs and the methods used to process specimens are equally important. Increasing the level of detail in histopathological examination of SLNs increases detection of metastatic tumours but will also increase the burden of busy laboratories and thus may not be carried out routinely. Recommendation of a reasonable standard in SLN examination is required to ensure high sensitivity of results while maintaining a manageable practice workload. Materials and Methods: Twenty-four patients with clinically node-negative breast cancer were recruited. Combined radiotracer and blue dye methods were used for identification of SLNs. The nodes were thinly sliced and embedded. Serial sectioning and immunohistochemical (IHC) staining against AE1/AE3 were performed if initial H&E sections of the blocks were negative. Results: SLNs were successfully identified in all patients. Ten cases had nodal metastases with 7 detected in SLNs and 3 detected only in axillary nodes (false negative rate, FNR=30%). Some 5 out of 7 metastatic lesions in the SLNs (71.4%) were detected in initial sections of the thinly sliced tissue. Serial sectioning detected the remaining two cases with either micrometastases or isolated tumour cells (ITC). Conclusions: Thin slicing of tissue to 3-5mm thickness and serial sectioning improved the detection of micro and macro-metastases but the additional burden of serial sectioning gave low yield of micrometastases or ITC and may not be cost effective. IHC validation did not further increase sensitivity of detection. Therefore its use should only be limited to confirmation of suspicious lesions. False negative cases where SLNs were not involved could be due to skipped metastases to non-sentinel nodes or poor technique during procurement, resulting in missed detection of actual SLNs.
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Sarikapan Wilailak,Karen KL Chan,Chi-An Chen,남주현,Kazunori Ochiai,Tar-Choon Aw,Subathra Sabaratnam,Sudarshan Hebbar,Jaganathan Sickan,Beth A Schodin,Chuenkamon Charakorn,Walfrido W Sumpaico 대한부인종양학회 2015 Journal of Gynecologic Oncology Vol.26 No.1
Results: A total of 414 women with a pelvic mass were enrolled in the study, of which 328 had documented ultrasound findings. The risk prediction model that contained HE4, menopausal status, and ultrasound findings exhibited the best performance compared to models with CA-125 alone, or a combination of CA-125 and HE4. This model classified 77.2% of women with ovarian cancer as medium or high risk, and 86% of women with benign disease as very-low, low, or medium-low risk. This model exhibited better sensitivity than ROMA, but ROMA exhibited better specificity. Both models performed better than CA-125 alone. Objective: The purpose of this study was to develop a risk prediction score for distinguishing benign ovarian mass from malignant tumors using CA-125, human epididymis protein 4 (HE4), ultrasound findings, and menopausal status. The risk prediction score was compared to the risk of malignancy index and risk of ovarian malignancy algorithm (ROMA). Methods: This was a prospective, multicenter (n=6) study with patients from six Asian countries. Patients had a pelvic mass upon imaging and were scheduled to undergo surgery. Serum CA-125 and HE4 were measured on preoperative samples, and ultrasound findings were recorded. Regression analysis was performed and a risk prediction model was developed based on the significant factors. A bootstrap technique was applied to assess the validity of the HE4 model. Results: A total of 414 women with a pelvic mass were enrolled in the study, of which 328 had documented ultrasound findings. The risk prediction model that contained HE4, menopausal status, and ultrasound findings exhibited the best performance compared to models with CA-125 alone, or a combination of CA-125 and HE4. This model classified 77.2% of women with ovarian cancer as medium or high risk, and 86% of women with benign disease as very-low, low, or medium-low risk. This model exhibited better sensitivity than ROMA, but ROMA exhibited better specificity. Both models performed better than CA-125 alone. Conclusion: Combining ultrasound with HE4 can improve the sensitivity for detecting ovarian cancer compared to other algorithms.
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