Dataset Information

Risk assessment model for invasive breast cancer in Hong Kong women.

ABSTRACT: No risk assessment tool is available for identifying high risk population of breast cancer (BCa) in Hong Kong. A case-control study including 918 BCa cases and 923 controls was used to develop the risk assessment model among Hong Kong Chinese women.Each participant received an in-depth interview to obtain their lifestyle and environmental risk factors. Least absolute shrinkage and selection operator (LASSO) selection model was used to select the optimal risk factors (LASSO-model). A risk score system was constructed to evaluate the cumulative effects of selected factors. Bootstrap simulation was used to test the internal validation of the model. Model performance was evaluated by receiver-operator characteristic curves and the area under the curve (AUC).Age, number of parity, number of BCa cases in 1st-degree relatives, exposure to light at night, and sleep quality were the common risk factors for all women. Alcohol drinking was included for premenopausal women; body mass index, age at menarche, age at 1st give birth, breast feeding, using of oral contraceptive, hormone replacement treatment, and history of benign breast diseases were included for postmenopausal women. The AUCs were 0.640 (95% CI, 0.598-0.681) and 0.655 (95% CI, 0.621-0.653) for pre- and postmenopausal women, respectively. Further subgroup evaluation revealed that the model performance was better for women aged 50 to 70 years or ER-positive.This BCa risk assessment tool in Hong Kong Chinese women based on LASSO selection is promising, which shows a slightly higher discriminative accuracy than those developed in other populations.

SUBMITTER: Wang F

PROVIDER: S-EPMC4985325 | biostudies-other | 2016 Aug

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Risk assessment model for invasive breast cancer in Hong Kong women.

Wang Feng F Dai Juncheng J Li Mengjie M Chan Wing-Cheong WC Kwok Carol Chi-Hei CC Leung Siu-Lan SL Wu Cherry C Li Wentao W Yu Wai-Cho WC Tsang Koon-Ho KH Law Sze-Hong SH Lee Priscilla Ming-Yi PM Wong Carmen Ka-Man CK Shen Hongbing H Wong Samuel Yeung-Shan SY Yang Xiaohong R XR Tse Lap Ah LA

Medicine 20160801 32

No risk assessment tool is available for identifying high risk population of breast cancer (BCa) in Hong Kong. A case-control study including 918 BCa cases and 923 controls was used to develop the risk assessment model among Hong Kong Chinese women.Each participant received an in-depth interview to obtain their lifestyle and environmental risk factors. Least absolute shrinkage and selection operator (LASSO) selection model was used to select the optimal risk factors (LASSO-model). A risk score s ...[more]

PMID: 27512870

Similar Datasets

Project description:BACKGROUND:There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. METHODS:We used data from 40,158 women aged 50-69?years (via the lifepool cohort) participating in Australia's BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. RESULTS:Over a median of 4.3?years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O)?=?1.09, 95% confidence interval (CI) 1.00-1.18). There was good agreement across decile groups of Gail scores (?2?=?7.1, p?=?0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O?=?1.65, 95% CI 1.33-2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73-3.02, p?<?0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06-1.70, p?=?0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44-0.79, p?<?0.0001). Similar patterns were observed separately for women aged 50-59 and 60-69?years. The model's overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56-0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. CONCLUSIONS:This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50-69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Dataset Information

Risk assessment model for invasive breast cancer in Hong Kong women.

Publications

Risk assessment model for invasive breast cancer in Hong Kong women.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets