Format

Send to

Choose Destination
Gastroenterol Res Pract. 2019 Jul 9;2019:1285931. doi: 10.1155/2019/1285931. eCollection 2019.

Artificial Neural Network Individualised Prediction of Time to Colorectal Cancer Surgery.

Author information

1
Department of Surgery and Cancer, Imperial College London, Level 10, St. Mary's Hospital, Praed Street, London W2 1NY, UK.
2
Department of General Surgery, Yeovil District Hospital NHS Foundation Trust, Higher Kingston, Yeovil BA21 4AT, UK.
3
Faculty of Health and Life Sciences, University of Liverpool, Brownlow Hill, Liverpool L69 7ZX, UK.
4
Department of Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
5
Faculty of Science, University of Bath, Wessex House 3.22, Bath BA2 7AY, UK.

Abstract

Aim:

Colorectal cancer pathway targets mandate prompt treatment although practicalities may mean patients wait for surgery. This variable period could be utilised for patient optimisation; however, there is currently no reliable predictive system for time to surgery. If individualised surgical waits were prospectively known, tailored prehabilitation could be introduced.

Methods:

A dedicated, prospectively populated elective laparoscopic surgery for colorectal cancer with a curative intent database was utilised. Primary endpoint was the prediction of the individualised waiting time for surgery. A multilayered perceptron artificial neural network (ANN) model was trained and tested alongside uni- and multivariate analyses.

Results:

668 consecutive patients were included. 8.5% underwent neoadjuvant chemoradiotherapy. The mean time from diagnosis to surgery was 53 days (95% CI 48.3-57.8). ANN correctly identified those having surgery in <8 (97.7% and 98.8%) and <12 weeks (97.1% and 98.8%) of the training and testing cohorts with area under the receiver operating curves of 0.793 and 0.865, respectively. After neoadjuvant treatment, an ASA physical status score was the most important potentially modifiable risk factor for prolonged waits (normalised importance 64%, OR 4.9, 95% CI 1.5-16). The ANN findings were accurately cross-validated with a logistic regression model.

Conclusion:

Artificial neural networks using demographic and diagnostic data successfully predict individual time to colorectal cancer surgery. This could assist the personalisation of preoperative care including the incorporation of prehabilitation interventions.

Supplemental Content

Full text links

Icon for Hindawi Limited Icon for PubMed Central
Loading ...
Support Center