The predictive value of the preoperative C-reactive protein–albumin ratio for early recurrence and chemotherapy benefit in patients with gastric cancer after radical gastrectomy: using randomized phase III trial data

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.

The response of the body to a cancer is not a unique mechanism but has many parallels with inflammation and wound healing. This article reviews the links between cancer and inflammation and discusses the implications of these links for cancer prevention and treatment. We suggest that the inflammatory cells and cytokines found in tumours are more likely to contribute to tumour growth, progression, and immunosuppression than they are to mount an effective host antitumour response. Moreover cancer susceptibility and severity may be associated with functional polymorphisms of inflammatory cytokine genes, and deletion or inhibition of inflammatory cytokines inhibits development of experimental cancer. If genetic damage is the "match that lights the fire" of cancer, some types of inflammation may provide the "fuel that feeds the flames". Over the past ten years information about the cytokine and chemokine network has led to development of a range of cytokine/chemokine antagonists targeted at inflammatory and allergic diseases. The first of these to enter the clinic, tumour necrosis factor antagonists, have shown encouraging efficacy. In this article we have provided a rationale for the use of cytokine and chemokine blockade, and further investigation of non-steroidal anti-inflammatory drugs, in the chemoprevention and treatment of malignant diseases.