Abstract
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/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.
MeSH Keywords: BRCA1 Protein, BRCA2 Protein, Breast
Background
Breast cancer (BC) is a malignant tumor derived from breast gland epithelium and is the most common cancer in women [1–3]. Most BC patients are already at advanced or terminal stage at the time of primary diagnosis. Due to rapid progression and high metastasis and recurrence rates, treatment efficacy is not satisfactory. Distal metastasis toward other vital organs an important cause of mortality in BC patients [4,5]. Therefore, effective screening of BC and timely identification of BC patients can reduce BC morbidity and mortality and improve treatment efficiency, thus having important clinical implications.
The feasibility of large-scale screening for BC is limited by medical resources and clinical technique. Therefore, the development of rapid, easy-to-use, and effective screening tools for BC has become the critical challenge for BC prevention and treatment. To date, various BC screening models have been developed, including the Gail, Tyrer-Cuzick, Couch, Frank, BRCAPRO, and Claus models. Among these, the Couch, Frank, BRCAPRO, and Claus models focus more on high-risk populations and special situations, thus having limited usefulness [6–8]. The Gail model was initially proposed in 1989, and has advantages of rapid progress, easy manipulation, low cost, and effectiveness, and thus has become the most widely used and standard risk evaluation model for BC, with wide clinical application [9–11]. The Gail model was derived from the USA, and mainly targets white females, with less study in other regions, thus making its applicability in Chinese women unclear. Compared to the Gail model, the Tyrer-Cuzick model has not been widely assessed in large-population studies. The Tyrer-Cuzick model includes multiple factors such as age at menopause, body-mass index (BMI), benign disease of breast gland, BC in relatives, family history, and hormonal replacement therapy into a comprehensive investigation [7,12,13]. A previous study found the Tyrer-Cuzick model has relatively higher accuracy [14]. The present study took place in Jiangxi province, China and evaluated the utility of the Tyrer-Cuzick and Gail models in BC screening.
Material and Methods
Research objects
This study used a risk evaluation model to analyze BC risk among 35–69-year-old women. The disease group consisted of infiltrative BC patients who received ultrasound or X-ray examination with pathology confirmation at the Affiliated Jiujiang Hospital of Nanchang University (N=150, average age=52.6±11.8 years). The control group consisted of healthy women who received breast ultrasound or X-film examination in our hospital (N=130, averaged age=54.3±12.6 years). No significant difference was found in age distribution, economic status, occupation, ethnic group, or education level between the 2 groups (p>0.05).
This study was approved by Ethics Committee of the Affiliated Jiujiang Hospital of Nanchang University and all enrolled objects signed informed consent.
Research methodology
Well-trained investigators were employed to perform retrospective information collection using a uniform questionnaire to collect general information in the control and disease groups, including age, history of breast gland disease, family history, menstrual age, pregnancy age, biopsy of breast gland, and ethnic group. Age and breast gland biopsy information were collected based on recall of the last 5 years. Other information regarding risk factors of BC pathogenesis for Gail model were also collected.
Well-trained investigators were employed to perform retrospective information collection using a uniform questionnaire to collect general information in the control and disease groups for the Tyrer-Cuzick model, including age, height, body weight, menopause age, menstrual age, pregnancy age, history of benign breast gland disease, estrogen exposure experience, family member with BC, and number of bilateral BCs. All information and data related to BC risk factors in the Tyrer-Cuzick model were collected. Personal information was input based on the questionnaire to calculate 5-year BC risk prediction value.
Statistical analysis
All questionnaire data were input into computer software. Using a diagnostic trial approach, sensitivity and specificity of the Gail or Tyrer-Cuzick model in BC risk evaluation were calculated. The 5-year BC risk was estimated using both models for disease and control groups to calculate the ROC AUC, along with the paired chi-square test. A statistically significant difference was defined when p<0.05.
Results
Evaluation results using Gail model
Gail model analysis was performed among all 280 individuals included. High BC risk was defined as higher than 1.2% cancer risk, and other scenarios were defined as low BC risk (boundary level of risk was the upper limit of the ROC curve). The Gail model had 53.33% sensitivity, 77.69% specificity, 73.39% positive prediction value, 59.06% negative prediction value, 64.64% accuracy, and 0.31 Jordon index (Table 1). The difference was statistically significant by paired chi-square test (χ2=16.980, p<0.001, Table 2).
Table 1.
Estimation of Gail model diagnostic trial.
| Index | Calculated value | 95%CI |
|---|---|---|
| Sensitivity | 53.33% | 45.62~61.04% |
| Specificity | 77.69% | 69.28~86.10% |
| Positive prediction value | 73.39% | 65.51~81.27% |
| Negative prediction value | 59.06% | 51.08~67.04% |
| Jordon index | 0.31 | 0.27~0.35 |
| Accuracy | 64.64% | 59.16~70.12% |
Table 2.
Gail model diagnostic trial results.
| Risk | Disease | Control | χ2 | P | ||
|---|---|---|---|---|---|---|
| N | % | N | % | |||
| High | 80 | 53.3 | 29 | 22.3 | 16.980 | <0.001 |
| Low | 70 | 46.7 | 101 | 77.7 | ||
| Total | 150 | 100.0 | 130 | 100.0 | ||
Tyrer-Cuzick model evaluation results
Tyrer-Cuzick model analysis was performed among all 280 individuals included. High BC risk was defined as higher than 1.59% cancer risk, and other scenarios were defined as low BC risk (boundary level of risk was the upper limit of the ROC curve). 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 (Table 3). The difference was statistical significant by paired chi-square test (χ2=17.000, p<0.001, Table 4).
Table 3.
Estimation of diagnostic trial parameters in Tyrer-cuzick model.
| Index | Calculated value | 95%CI |
|---|---|---|
| Sensitivity | 66.00% | 59.68~72.32% |
| Specificity | 86.92% | 81.55~92.29% |
| Positive prediction value | 85.34% | 78.59~92.09% |
| Negative prediction value | 68.90% | 61.73~76.07% |
| Jordon index | 0.53 | 0.48~0.58 |
| Accuracy | 75.71% | 71.06~80.36% |
Table 4.
Tyrer-cuzick model diagnostic trial results.
| Risk | Disease | Control | χ2 | P | ||
|---|---|---|---|---|---|---|
| N | % | N | % | |||
| High | 99 | 66.0 | 17 | 13.1 | 17.000 | <0.001 |
| Low | 51 | 34.0 | 113 | 86.9 | ||
| Total | 150 | 100.0 | 130 | 100.0 | ||
ROC curve analysis for diagnostic values of Gail and Tyrer-Cuzick model
ROC curves were plotted for Gail and Tyrer-Cuzick model to evaluate their predicted risk values for BC diagnosis. ROC analysis showed the area under the curve (AUC) was 0.665 for the Gail model (95% CI: 0.629~0.701, Figure 1A) and 0.786 for the Tyrer-Cuzick model (95% CI: 0.757~0.815, Figure 1B).
Figure 1.
ROC curve analysis for diagnostic value of Gail and Tyrer-Cuzick models.
Discussion
The incidence for BC is increasing since 1970s worldwide. It is estimated that newly discovered BC cases account for about 25% of all cancers in women, and BC-induced mortality accounts for 15% cancer-related death in women [15]. There are about 1 500 000 newly diagnosed BC patients every year worldwide and about 500 000 (one-third) of these women died from BC [16]. Therefore, BC has become the primary morality factor threatening women’s health. In both developed and developing countries, BC is the primary cause of death among malignant tumors in women [17]. Compared to Western countries, BC incidence in China is relatively lower. However, in recent years, with lifestyle and dietary changes, increased mental and work pressure, environmental pollution, and endocrine disruption, BC incidence in China is rapidly increasing. Currently, BC has become the most common malignant tumor in Chinese women [18–20]. The early pathogenesis of BC is relatively insidious, frequently without typical symptoms or body signs. Therefore, misdiagnosis and neglect are frequent. In the early stage, BC progresses rapidly and is prone to metastasis toward peripheral tissues and major organs, including bone, lung, lymph node, skin, and brain, leading to high rates of recurrence and mortality rates. Therefore, timely deployment of BC screening and discovery and diagnosis of BC patients can effectively decrease the threat of BC to women’s health and improve treatment efficiency, thus having clinical importance.
Currently, various developed countries have initiated early screening programs for BC. For example, the USA has begun early screening of BC in community-wide physical exams. In China, BC screening is mainly achieved by large-scale screening in major hospitals, in the form of breast gland X-ray, ultrasound, or even biopsy. These approaches are difficult and have poor patient compliance, making them hard to promoted widely. The Gail model was proposed by Gail in 1989. Researchers analyzed clinical data from more than 1 000 000 women and predicted the 5-year BC rate and lifetime risk. It is also used for recommendation for further screening or drug prevention in high-risk populations. The Gail model was co-designed by the National Cancer Institute (NCI) and the National Surgical Adjuvant Breast and Bowel Project (NSABP), and is accredited by the National Comprehensive Cancer Network (NCCN) as a strategy for prevention and diagnosis [21,22]. Compared to other screening approaches, the Gail model is easy to carry out. However, due to its origin in the USA and because it mainly targets white women, the sensitivity and accuracy of BC screening in China still require further investigation. Compared to the Gail model, the Tyrer-Cuzick model mainly targets British, Australians, and New Zealanders, and incorporated menopause age, BMI, history of breast gland benign disease, family history of BC, and hormonal replacement therapy, thus increasing its accuracy [7,12,13]. In sharp contrast with the Gail model, the Tyrer-Cuzick model has not been widely studied in large-population investigations, so its sensitivity and accuracy are unclear.
Results of our study show that the Gail model had 53.33% sensitivity and 77.69% specificity, with 73.39% positive prediction value, 59.06% negative prediction value, 64.64% accuracy, 0.31 Jordon index, and 0.665 ROC AUC. These results reveal that the Gail model has relatively higher sensitivity and specificity in predicting BC in a high-risk population of Jiangxi women. However, the Gail model does not consider information such as family history, lack of secondary relative, contralateral BC information, relative disease onset age, lactation or abortion, or history of hormonal replacement therapy, all of which can form risk factors of BC onset, leading to its relatively lower predictive value. Therefore, the present study used the Tyrer-Cuzick model to investigate its value in screening of female BC. Results showed that the Tyrer-Cuzick model had 66.00% sensitivity, 86.92% specificity, 85.34% positive prediction value, 68.90% negative prediction value, 75.71% accuracy, 0.53 Jordon index, and 0.786 ROC AUC. Warwick et al. [12] showed the ROC AUC was 0.62 using the Tyrer-Cuzick model in female BC screening, similar to our results. Our results showed that the Tyrer-Cuzick model had higher sensitivity, specificity, and diagnostic value for Jiangxi female BC screening than the Gail model. Brentnall et al. [7] observed higher diagnostic value of the Tyrer-Cuzick model compared to the Gail model, further supporting our results. However, in contrast to Boughey et al. [13], who recruited nearly 10 000 cases, our study had a relatively small sample size, compromising its representativeness. Moreover, this was a retrospective case study, and may involve memory bias for information collected 5 years ago. Therefore, large-sample prospective studies are required for to compare the Gail and Tyrer-Cuzick models in risk evaluation of BC. In summary, we found that the Gail model had relatively lower accuracy in evaluating BC risk of Jiangxi women, while the Tyrer-Cuzick model had higher accuracy for evaluating BC risk in our province.
Conclusions
The Gail model has relatively lower accuracy in evaluating BC risk in women in Jiangxi province and the Tyrer-Cuzick model has higher accuracy in evaluating BC risk among Jiangxi women.
Footnotes
Source of support: This work was supported by a project of the Jiangxi Science and Technology Department (No. 20151BBG70151)
Conflict of interest
None.
References
- 1.Winters S, Martin C, Murphy D, Shokar NK. Breast cancer epidemiology, prevention, and screening. Prog Mol Biol Transl Sci. 2017;151:1–32. doi: 10.1016/bs.pmbts.2017.07.002. [DOI] [PubMed] [Google Scholar]
- 2.Rojas K, Stuckey A. Breast cancer epidemiology and risk factors. Clin Obstet Gynecol. 2016;59(4):651–72. doi: 10.1097/GRF.0000000000000239. [DOI] [PubMed] [Google Scholar]
- 3.Ghoncheh M, Pournamdar Z, Salehiniya H. Incidence and mortality and epidemiology of breast cancer in the world. Asian Pac J Cancer Prev. 2016;17(S3):43–46. doi: 10.7314/apjcp.2016.17.s3.43. [DOI] [PubMed] [Google Scholar]
- 4.Pessina F, Navarria P, Cozzi L, et al. Outcome evaluation of HER2 breast cancer patients with limited brain metastasis. Anticancer Res. 2017;37(12):7057–62. doi: 10.21873/anticanres.12177. [DOI] [PubMed] [Google Scholar]
- 5.Passhak M, Shachar SS, Bar-Sela G, Fried G, et al. Breast cancer in young women aged 35 and under: Patterns of care and outcome. Breast J. doi: 10.1111/tbj.12966. [Epub ahead of print] [DOI] [PubMed] [Google Scholar]
- 6.Arun BK, Gong Y, Liu D, et al. Phase I biomarker modulation study of atorvastatin in women at increased risk for breast cancer. Breast Cancer Res Treat. 2016;158(1):67–77. doi: 10.1007/s10549-016-3849-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Brentnall AR, Harkness EF, Astley SM, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015;17(1):147. doi: 10.1186/s13058-015-0653-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Subramanian J, Karmegam A, Papageorgiou E, et al. An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. Comput Methods Programs Biomed. 2015;118(3):280–97. doi: 10.1016/j.cmpb.2015.01.001. [DOI] [PubMed] [Google Scholar]
- 9.Seyednoori T, Pakseresht S, Roushan Z. Risk of developing breast cancer by utilizing Gail model. Women Health. 2012;52(4):391–402. doi: 10.1080/03630242.2012.678476. [DOI] [PubMed] [Google Scholar]
- 10.Micallef S, Micallef D, Schembri-Wismayer P, et al. Chemoprevention of breast cancer among women at elevated risk as defined by Gail Score. Minerva Ginecol. 2015;67(4):335–52. [PubMed] [Google Scholar]
- 11.Erbil N, Dundar N, Inan C, Bolukbas N. Breast cancer risk assessment using the Gail model: A Turkish study. Asian Pac J Cancer Prev. 2015;16(1):303–6. doi: 10.7314/apjcp.2015.16.1.303. [DOI] [PubMed] [Google Scholar]
- 12.Warwick J, Birke H, Stone J, et al. Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: Findings from the placebo arm of the International Breast Cancer Intervention Study I. Breast Cancer Res. 2014;16(5):451. doi: 10.1186/s13058-014-0451-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Boughey JC, Hartmann LC, Anderson SS, et al. Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) model for breast cancer risk prediction in women with atypical hyperplasia. J Clin Oncol. 2010;28(22):3591–96. doi: 10.1200/JCO.2010.28.0784. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Jacobi CE, de Bock GH, Siegerink B, van Asperen CJ, et al. Differences and similarities in breast cancer risk assessment models in clinical practice: Which model to choose? Breast Cancer Res Treat. 2009;115(2):381–90. doi: 10.1007/s10549-008-0070-x. [DOI] [PubMed] [Google Scholar]
- 15.Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. Cancer J Clin. 2015;65(2):87–108. doi: 10.3322/caac.21262. [DOI] [PubMed] [Google Scholar]
- 16.Liu FC, Lin HT, Kuo CF, et al. Epidemiology and survival outcome of breast cancer in a nationwide study. Oncotarget. 2017;8(10):16939–50. doi: 10.18632/oncotarget.15207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ban KA, Godellas CV. Epidemiology of breast cancer. Surg Oncol Clin N Am. 2014;23(3):409–22. doi: 10.1016/j.soc.2014.03.011. [DOI] [PubMed] [Google Scholar]
- 18.Li T, Tang L, Gandomkar Z, et al. Mammographic density and other risk factors for breast cancer among women in China. Breast J. :2017. doi: 10.1111/tbj.12967. [Epub ahead of print] [DOI] [PubMed] [Google Scholar]
- 19.Dinegde NG, Xuying L. Awareness of breast cancer among female care givers in tertiary cancer hospital, China. Asian Pac J Cancer Prev. 2017;18(7):1977–83. doi: 10.22034/APJCP.2017.18.7.1977. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zuo TT, Zheng RS, Zeng HM, et al. Female breast cancer incidence and mortality in China, 2013. Thorac Cancer. 2017;8(3):214–18. doi: 10.1111/1759-7714.12426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Al Otaibi HH. Breast cancer risk assessment using the gail model and it’s predictors in Saudi women. Asian Pac J Cancer Prev. 2017;18(11):2971–75. doi: 10.22034/APJCP.2017.18.11.2971. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Khazaee-Pool M, Majlessi F, Nedjat S, et al. Assessing breast cancer risk among iranian women using the gail model. Asian Pac J Cancer Prev. 2016;17(8):3759–62. [PubMed] [Google Scholar]