Dataset Information

Machine learning to predict early recurrence after oesophageal cancer surgery.

ABSTRACT:

Background

Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This study aimed to develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multinational cohort and machine learning approaches.

Methods

Consecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in one Dutch and six UK oesophagogastric units were analysed. Using clinical characteristics and postoperative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and extreme gradient boosting (XGB). Finally, a combined (ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model.

Results

A total of 812 patients were included. The recurrence rate at less than 1 year was 29·1 per cent. All of the models demonstrated good discrimination. Internally validated areas under the receiver operating characteristic (ROC) curve (AUCs) were similar, with the ensemble model performing best (AUC 0·791 for ELR, 0·801 for RF, 0·804 for XGB, 0·805 for ensemble). Performance was similar when internal-external validation was used (validation across sites, AUC 0·804 for ensemble). In the final model, the most important variables were number of positive lymph nodes (25·7 per cent) and lymphovascular invasion (16·9 per cent).

Conclusion

The model derived using machine learning approaches and an international data set provided excellent performance in quantifying the risk of early recurrence after surgery, and will be useful in prognostication for clinicians and patients.

SUBMITTER: Rahman SA

PROVIDER: S-EPMC7299663 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundExcessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results.MethodsWe randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut-off value to define the excessive skeletal muscle loss.ResultsThe five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively.ConclusionsMachine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.