Artificial neural networks - A method for prediction of survival following liver resection for colorectal cancer metastases

作者：Spelt L; Nilsson J; Andersson R; Andersson B^*

来源：European Journal of Surgical Oncology, 2013, 39(6): 648-654.

DOI：10.1016/j.ejso.2013.02.024

• Colorectal cancer
• Liver metastases
• Artificial neural network
• Long-term survival
• Prognosis

Objective: To construct an artificial neural network (ANN) model to predict survival after liver resection for colorectal cancer (CRC) metastases. %26lt;br%26gt;Background: CRC liver metastases are fatal if untreated and resection can possibly be curative. Predictive models stratify patients into risk categories to predict prognosis and select those who can benefit from aggressive multidisciplinary treatment and intensive follow-up. Standard linear models assume proportional hazards, whereas more flexible non-linear survival models based on ANNs may better predict individual long-term survival. %26lt;br%26gt;Methods: Clinicopathological and perioperative data on patients who underwent liver resection for CRC metastases between 1994 and 2009 were studied retrospectively. A five-fold cross-validated ANN model was constructed. Risk variables were ranked and minimised through calibrated ANNs. Time dependent hazard ratio (HR) was calculated using the ANN. Performance of the ANN model and Cox regression were analysed using Harrell%26apos;s C-index. %26lt;br%26gt;Results: 241 patients with a median age of 66 years were included. There were no perioperative deaths and median survival was 56 months. Of 28 potential risk variables, the ANN selected six: age, preoperative chemotherapy, size of largest metastasis, haemorrhagic complications, preoperative CEA-level and number of metastases. The C-index was 0.72 for the ANN model and 0.66 for Cox regression. %26lt;br%26gt;Conclusion: For the first time ANNs were used to successfully predict individual long-term survival for patients following liver resection for CRC metastases. In the future, more complex prognostic factors can be incorporated into the ANN model to increase its predictive ability.

• 出版日期2013-6