Do Multidisciplinary Team (MDT) processes influence survival in patients with colorectal cancer? A population-based experience

BACKGROUND: MDT (multidisciplinary team) meetings are considered an essential component of care for patients with cancer. However there is remarkably little direct evidence that such meetings improve outcomes. We assessed whether or not MDT (multidisciplinary team) processes influenced survival in a cohort of patients with colorectal cancer. METHODS: Observational study of a population-based cohort of 586 consecutive patients with colorectal cancer diagnosed in Tayside (Scotland) during 2006 and 2007. RESULTS: Recommendations from MDT meetings were implemented in 411/586 (70.1 %) of patients, the MDT+ group. The remaining175/586 (29.9 %) were either never discussed at an MDT, or recommendations were not implemented, MDT- group. The 5-year cause-specific survival (CSS) rates were 63.1 % (MDT+) and 48.2 % (MDT-), p < 0.0001. In analysis confined to patients who survived >6 weeks after diagnosis, the rates were 63.2 % (MDT+) and 57.7 % (MDT-), p = 0.064. The adjusted hazard rate (HR) for death from colorectal cancer was 0.73 (0.53 to 1.00, p = 0.047) in the MDT+ group compared to the MDT- group, in patients surviving >6 weeks the adjusted HR was 1.00 (0.70 to 1.42, p = 0.987). Any benefit from the MDT process was largely confined to patients with advanced disease: adjusted HR ((early)) 1.32 (0.69 to 2.49, p = 0.401); adjusted HR((advanced)) 0.65 (0.45 to 0.96, p = 0.031). CONCLUSIONS: Adequate MDT processes are associated with improved survival for patients with colorectal cancer. However, some of this effect may be more apparent than real – simply reflecting selection bias. The MDT process predominantly benefits the 40 % of patients who present with advanced disease and conveys little demonstrable advantage to patients with early tumours. These results call into question the current belief that all new patients with colorectal cancer should be discussed at an MDT meeting

Survival prediction and treatment recommendation with Bayesian techniques in lung cancer.

In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms. For treatment recommendation, the Bayesian Network, whose structure was learned by the MC(3) algorithm, has marginally outperformed NB, based on producing concordant results with the recorded treatments in the dataset. We observed that in cases where the classifier recommendations were discordant with the recorded treatments, the 1-year-survival rate decreased by 15%. We also observed that discordance between the classifier and the dataset was more dominant in cases where the recorded treatment was non-curative or was not frequently encountered in the dataset

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments