Biweekly CHOP therapy improves therapeutic effect in the non-GCB subtype of diffuse large B-cell lymphoma

Different decision tree induction strategies for a medical decision problem

The paper presents a comparative study of selected recognition methods for the medical decision problem -acute abdominal pain diagnosis. We consider if it is worth using expert knowledge and learning set at the same time. The article shows two groups of decision tree approaches to the problem under consideration. The first does not use expert knowledge and generates classifier only on the basis of learning set. The second approach utilizes expert knowledge for specifying the decision tree structure and learning set for determining mode of decision making in each node based on Bayes decision theory. All classifiers are evaluated on the basis of computer experiments.

Systematic review of efficacy of dose-dense versus non-dose-dense chemotherapy in breast cancer, non-Hodgkin lymphoma, and non-small cell lung cancer

Randomized controlled trials (RCTs) have suggested a potential advantage of dose-dense chemotherapy in improving disease-free and overall survival in patients with certain malignancies. This systematic review summarizes the literature on the efficacy of dose-dense chemotherapy across various cancers (breast cancer, non-Hodgkin lymphoma [NHL], and non-small cell lung cancer) and chemotherapy regimens. Among the 17 trials identified, few reported statistically significant differences between dose-dense and standard chemotherapy, and most were small with limited statistical power. Statistically significant differences in overall survival favoring dose-dense schedules were apparent among large RCTs in potentially curative settings such as early-stage breast cancer and NHL. Clinical and treatment heterogeneity demonstrated the flexibility of the dose-dense paradigm but also precluded quantitative meta-analysis of results. Further study of dose-dense schedules based on large RCTs is needed to demonstrate the consistency and generalizability of these findings.