Tired of feeling tired - The role of circulating inflammatory biomarkers and long-term cancer related fatigue in breast cancer survivors.

BACKGROUND: Low-grade inflammation has been associated with cancer related fatigue (CRF). However, most studies focused on CRF during or shortly after treatment. Longitudinal studies are rare with inconsistent results. We assessed the association of inflammatory biomarkers with total CRF and all subdomains (physical, cognitive, affective) in long-term breast cancer survivors. METHOD: Patients recruited between 2002 and 2005 provided information on CRF at first follow-up (FU1) (N = 1292) and second follow-up (FU2) (N = 1205), after a median of 6.2 years and 11.7 years, respectively. Associations of 11 inflammatory biomarkers with CRF at FU1 and at FU2 were assessed using linear regression models. Logistic regression models were used to compare patients fatigued at both time-points and those never fatigued (N = 932). RESULTS: C-reactive protein (CRP) was significantly associated with total CRF at FU1 (ß = 1.47, 95%CI = 0.62-2.31, p = 0.0007), at FU2 (ß = 1.98, 95 %CI = 0.96-2.99, p = 0.0001) and with persistent CRF (OR = 1.29, 95%CI = 1.13-1.47, p < 0.0001). IL-6 levels were associated with total CRF at FU1 (ß = 1.01, 95%CI = 0.43-1.59, p = 0.0006), but not with CRF at FU2 or persistent CRF. No association remained significant after adjustment for relevant covariates. DISCUSSION: CRP and Il-6 were associated with risk of CRF in long-term breast cancer survivors, but were not independent of other known risk factors, suggesting that currently studied inflammatory markers are not suitable to identify patients at risk of long-term CRF.

Clinically challenging mammographic artifacts: a pictorial guide.

Artifacts on mammographic images detract from the overall quality of the images and often present clinical and technical troubleshooting difficulties for the interpreting radiologist, technologist, and medical physicist and for the equipment and processor service personnel. This presentation demonstrates several types of mammographic artifacts that may pose a clinical challenge. They are arranged in the following categories: (1) particularly dangerous artifacts, (2) masses, (3) calcifications, (4) density variations, and (5) miscellaneous artifacts. Examples of such findings as summation shadows, normal anatomic variations, and incorrect positioning are also demonstrated as artifacts in this guide, because they may affect image quality or patient radiation dose. Under the Mammography Quality Standards Act, the lead interpreting physician has the responsibility for ensuring that the facility meets quality assurance requirements and is required to follow up with the technologist on poor-quality images. It is vital to recognize and correct for artifacts, whether they simulate non-existent lesions or obscure real pathology, because misinterpretation can lead to undesirable consequences.