BACKGROUND: In settings with limited resources, sentinel lymph node biopsy (SNB) is only offered to breast cancer patients with small tumors and a low a priori risk of axillary metastases. OBJECTIVE: We investigated whether CancerMath, a free online prediction tool for axillary lymph node involvement, is able to identify women at low risk of axillary lymph node metastases in Malaysian women with 3-5 cm tumors, with the aim to offer SNB in a targeted, cost-effective way. METHODS: Women with non-metastatic breast cancers, measuring 3-5 cm were identified within the University Malaya Medical Centre (UMMC) breast cancer registry. We compared CancerMath-predicted probabilities of lymph node involvement between women with versus without lymph node metastases. The discriminative performance of CancerMath was tested using receiver operating characteristic (ROC) analysis. RESULTS: Out of 1,017 patients, 520 (51 %) had axillary involvement. Tumors of women with axillary involvement were more often estrogen-receptor positive, progesterone-receptor positive, and human epidermal growth factor receptor (HER)-2 positive. The mean CancerMath score was higher in women with axillary involvement than in those without (53.5 vs. 51.3, p = 0.001). In terms of discrimination, CancerMath performed poorly, with an area under the ROC curve of 0.553 (95 % confidence interval CI 0.518-0.588). Attempts to optimize the CancerMath model by adding ethnicity and HER2 to the model did not improve discriminatory performance. CONCLUSION: For Malaysian women with tumors measuring 3-5 cm, CancerMath is unable to accurately predict lymph node involvement and is therefore not helpful in the identification of women at low risk of node-positive disease who could benefit from SNB.
BACKGROUND: In settings with limited resources, sentinel lymph node biopsy (SNB) is only offered to breast cancerpatients with small tumors and a low a priori risk of axillary metastases. OBJECTIVE: We investigated whether CancerMath, a free online prediction tool for axillary lymph node involvement, is able to identify women at low risk of axillary lymph node metastases in Malaysian women with 3-5 cm tumors, with the aim to offer SNB in a targeted, cost-effective way. METHODS:Women with non-metastatic breast cancers, measuring 3-5 cm were identified within the University Malaya Medical Centre (UMMC) breast cancer registry. We compared CancerMath-predicted probabilities of lymph node involvement between women with versus without lymph node metastases. The discriminative performance of CancerMath was tested using receiver operating characteristic (ROC) analysis. RESULTS: Out of 1,017 patients, 520 (51 %) had axillary involvement. Tumors of women with axillary involvement were more often estrogen-receptor positive, progesterone-receptor positive, and humanepidermal growth factor receptor (HER)-2 positive. The mean CancerMath score was higher in women with axillary involvement than in those without (53.5 vs. 51.3, p = 0.001). In terms of discrimination, CancerMath performed poorly, with an area under the ROC curve of 0.553 (95 % confidence interval CI 0.518-0.588). Attempts to optimize the CancerMath model by adding ethnicity and HER2 to the model did not improve discriminatory performance. CONCLUSION: For Malaysian women with tumors measuring 3-5 cm, CancerMath is unable to accurately predict lymph node involvement and is therefore not helpful in the identification of women at low risk of node-positive disease who could benefit from SNB.
Authors: Nirmala Bhoo-Pathy; Cheng-Har Yip; Mikael Hartman; Nakul Saxena; Nur Aishah Taib; Gwo-Fuang Ho; Lai-Meng Looi; Awang M Bulgiba; Yolanda van der Graaf; Helena M Verkooijen Journal: Eur J Cancer Date: 2012-02-25 Impact factor: 9.162
Authors: Nirmala Bhoo Pathy; Cheng Har Yip; Nur Aishah Taib; Mikael Hartman; Nakul Saxena; Philip Iau; Awang M Bulgiba; Soo Chin Lee; Siew Eng Lim; John E L Wong; Helena M Verkooijen Journal: Breast Date: 2011-02-12 Impact factor: 4.380
Authors: James S Michaelson; L Leon Chen; Devon Bush; Allan Fong; Barbara Smith; Jerry Younger Journal: Breast Cancer Res Treat Date: 2011-02-15 Impact factor: 4.872
Authors: Armando E Giuliano; Kelly K Hunt; Karla V Ballman; Peter D Beitsch; Pat W Whitworth; Peter W Blumencranz; A Marilyn Leitch; Sukamal Saha; Linda M McCall; Monica Morrow Journal: JAMA Date: 2011-02-09 Impact factor: 56.272
Authors: Peter M Ravdin; Kathleen A Cronin; Nadia Howlader; Christine D Berg; Rowan T Chlebowski; Eric J Feuer; Brenda K Edwards; Donald A Berry Journal: N Engl J Med Date: 2007-04-19 Impact factor: 91.245
Authors: Nirmala Bhoo-Pathy; Mikael Hartman; Cheng-Har Yip; Nakul Saxena; Nur Aishah Taib; Siew-Eng Lim; Philip Iau; Hans-Olov Adami; Awang M Bulgiba; Soo-Chin Lee; Helena M Verkooijen Journal: PLoS One Date: 2012-02-21 Impact factor: 3.240