The association of infrared imaging findings of the breast with hormone receptor and human epidermal growth factor receptor 2 status of breast cancer

The association of infrared imaging findings of the breast with prognosis in breast cancer patients: an observational cohort study

Background

To evaluate whether infrared (IR) imaging findings are associated with prognosis in patients with invasive breast carcinomas.

Methods

This study was approved by the institutional review board of the research ethics committee of our hospital, and all participants gave written informed consent. From March 2005 to June 2007, we enrolled 143 patients with invasive breast cancer that underwent preoperative IR imaging. We used five IR signs to interpret breast IR imaging. Cox proportional hazards model was used to evaluate the effect of IR signs on long-term mortality.

Results

During a median follow-up of 2451 days (6.7 years), 31 patients died. Based on the Cox Proportional Hazards Model, IR1 sign (the temperature of cancer site minus that of the contralateral mirror imaging site) was positively associated with mortality in the univariate analysis (overall mortality hazard ratio [HR], 2.29; p = 0.03; disease-specific mortality HR, 2.57; p = 0.04) as well as the multivariate analysis after controlling for clinicopathological factors (overall mortality HR, 3.85; p = 0.01; disease-specific mortality HR, 3.91, p = 0.02). In patients with clinical stage I and II disease, IR1 was also positively associated with mortality (overall mortality HR, 3.76; p = 0.03; disease-specific mortality HR, 4.59; p = 0.03). Among patients with node-negative disease, IR1 and IR5 (asymmetrical thermographic pattern) were associated with mortality (p = 0.04 for both IR1 and IR5, chi-squared test).

Conclusion

Breast IR findings are associated with mortality in patients with invasive breast carcinomas. The association remained in patients with node-negative disease.

Trial registration

NCT00166998.

Evaluation of the diagnostic power of thermography in breast cancer using Bayesian network classifiers

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool.