Predicting outcomes of pelvic exenteration using machine learning

PelvEx Collaborative

Research output: Contribution to journalArticlepeer-review

Abstract

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods.

Original languageEnglish (US)
Pages (from-to)1933-1940
Number of pages8
JournalColorectal Disease
Volume22
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • Artificial intelligence
  • artificial neural network
  • colorectal surgery
  • machine learning
  • pelvic exenteration

ASJC Scopus subject areas

  • Gastroenterology

Fingerprint Dive into the research topics of 'Predicting outcomes of pelvic exenteration using machine learning'. Together they form a unique fingerprint.

Cite this