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09 August 2018 : Clinical Research  

Use of Receiver Operating Characteristic (ROC) Curve Analysis for Tyrer-Cuzick and Gail in Breast Cancer Screening in Jiangxi Province, China

Le Zhang1BDF, Zhigang Jie2ABG*, Shengxi Xu1CDE, Liqun Zhang1DFG, Xiangqu Guo1ADG

DOI: 10.12659/MSM.910108

Med Sci Monit 2018; 24: CLR5528-5532


BACKGROUND: Breast cancer is a malignant tumor derived from breast gland epithelium. The screening and early diagnosis of breast cancer in high-risk populations can effectively suppress its threat to women’s health and improve treatment efficiency, and thus has critical importance. Using various evaluation models, the present study evaluated cancer risk in 35–69-year-old women, and the usefulness of models in breast cancer prevention was compared.

MATERIAL AND METHODS: A total of 150 infiltrative breast cancer patients who were diagnosed with breast cancer at our hospital were recruited, along with 130 healthy women as the control group. A retrospective study was performed to collect information. The 5-year risk of breast cancer was evaluated using the Gail and Tyrer-Cuzick models. Diagnostic results were analyzed to plot ROC curves for comparing the value for screening between Gail and Tyrer-Cuzick models.

RESULTS: The Gail model has 53.33% sensitivity and 77.69% specificity, with 73.39% positive prediction value, 59.06% negative prediction value, 64.64% accuracy, and 0.31 Jordon index. The Tyrer-Cuzick model had 66.00% sensitivity, 86.92% specificity, 85.34% positive prediction value, 68.90% negative prediction value, 75.71% accuracy, and 0.53 Jordon index. The area under the curve (AUC) was 0.665 for the Gail model (95% CI: 0.629~0.701) and 0.786 for the Tyrer-Cuzick model (95% CI: 0.757~0.815).

CONCLUSIONS: Both Gail model and Tyrer-Cuzick models can be used to evaluate breast cancer risk. The Gail model has relatively lower accuracy in evaluating breast cancer risk in Jiangxi province of China and the Tyrer-Cuzick model had relatively higher accuracy.

Keywords: BRCA2 Protein, Breast

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Dinah V. Parums

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Med Sci Monit 2024; 30:e945315


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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750