Predictive biomarkers such as oestrogen (ER) and progesterone (PR) receptors and c-erbB-2 oncoprotein have become a staple in breast cancer reports in the country as they increasingly play an important role in the treatment and prognosis of women with breast cancers. This study reviews the practice of histopathology reporting of these biomarkers in a Malaysian tertiary hospital setting. Retrospective data on demographic, pathological and biomarker profi les of patients with invasive ductal carcinoma who had undergone mastectomy or lumpectomy with axillary node clearance from 2005 to 2006 were retrieved from the Department of Pathology, Penang Hospital and analysed. The prevalence of ER positivity (55.8%), PR positivity (52.5%), c-erbB-2 oncoprotein overexpression (24%) and triple negativity (ER negative, PR negative, c-erbB-2 negative) (15%) by immunohistochemistry were comparable with other studies. Notably, c-erbB-2 overexpression was equivocal (2+) in 15% of cases. Since about a quarter of equivocal (2+) cases usually show amplifi cation by FISH, a small but certain percentage of patients would miss the benefi t of anti-cerbB- 2 antibody therapy if FISH is not performed. New ASCO/CAP guidelines on the quantitation of ER and PR will probably increase the prevalence of ER/PR positivity, invariably leading to signifi cant ramifi cations on the management of patients as more patients would be deemed eligible for endocrine therapy, as well as categorisation of triple negative breast cancers.

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.
Adult ; *Algorithms ; Biomarkers, Tumor/*blood ; CA-125 Antigen/blood ; Decision Support Techniques ; Diagnosis, Differential ; Female ; Humans ; Menopause ; Middle Aged ; Ovarian Neoplasms/*diagnosis/ultrasonography ; Predictive Value of Tests ; Prospective Studies ; Proteins/*analysis ; ROC Curve ; Risk Assessment/methods ; Sensitivity and Specificity