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. 2020 Jul;107(8):1042-1052.
doi: 10.1002/bjs.11461. Epub 2020 Jan 30.

Machine learning to predict early recurrence after oesophageal cancer surgery

S A Rahman  1 , R C Walker  1 , M A Lloyd  1 , B L Grace  1 , G I van Boxel  2 , B F Kingma  2 , J P Ruurda  2 , R van Hillegersberg  2 , S Harris  3 , S Parsons  4 , S Mercer  5 , E A Griffiths  6 , J R O'Neill  7 , R Turkington  8 , R C Fitzgerald  9 , T J Underwood  1 ; OCCAMS Consortium
Collaborators, Affiliations

Machine learning to predict early recurrence after oesophageal cancer surgery

S A Rahman et al. Br J Surg. 2020 Jul.

Abstract
in English, Spanish

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.

ANTECEDENTES: la recidiva precoz del cáncer tras esofaguectomía es un problema frecuente con una incidencia del 20-30% a pesar del uso generalizado del tratamiento neoadyuvante. La cuantificación de este riesgo es difícil y los modelos actuales funcionan mal. Este estudio se propuso desarrollar un modelo predictivo para la recidiva precoz después de la cirugía para el adenocarcinoma de esófago utilizando una gran cohorte multinacional y enfoques con aprendizaje automático. MÉTODOS: Se analizaron pacientes consecutivos sometidos a esofaguectomía por adenocarcinoma y que recibieron tratamiento neoadyuvante en 6 unidades de cirugía esofagogástrica del Reino Unido y 1 de los Países Bajos. Con la utilización de características clínicas y la histopatología postoperatoria se generaron modelos mediante regresión de red elástica (elastic net regression, ELR) y métodos de aprendizaje automático Random Forest (RF) y XG boost (XGB). Finalmente, se generó un modelo combinado (Ensemble) de dichos métodos. La importancia relativa de los factores respecto al resultado se calculó como porcentaje de contribución al modelo. RESULTADOS: En total se incluyeron 812 pacientes. La tasa de recidiva a menos de 1 año fue del 29,1%. Todos los modelos demostraron una buena discriminación. Las áreas bajo la curva ROC (AUC) validadas internamente fueron similares, con el modelo Ensemble funcionando mejor (ELR = 0,791, RF = 0,801, XGB = 0,804, Ensemble = 0,805). El rendimiento fue similar cuando se utilizaba validación interna-externa (validación entre centros, Ensemble AUC = 0,804). En el modelo final, las variables más importantes fueron el número de ganglios linfáticos positivos (25,7%) y la invasión linfovascular (16,9%). CONCLUSIÓN: El modelo derivado con la utilización de aproximaciones con aprendizaje automático y un conjunto de datos internacional proporcionó un rendimiento excelente para cuantificar el riesgo de recidiva precoz tras la cirugía y será útil para clínicos y pacientes a la hora de establecer un pronóstico.

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Figures

Figure 1
Figure 1
Study flow diagram
Figure 2
Figure 2
Model discrimination via 0·632 bootstrap Receiver operating characteristic (ROC) curves for a elastic net regression (area under the curve (AUC) 0·791, 95 per cent c.i. 0·757 to 0·826), b random forest (AUC 0·801, 0·769 to 0·834), c XG boost (AUC 0·804, 0·772 to 0·836) and d ensemble (AUC 0·805, 0·790 to 0·819). The shaded area represents the 95 per cent confidence interval.
Figure 3
Figure 3
Ensemble model calibration before and after adjustment a Unscaled calibration (intercept 0·395, slope 1·574) and b scaled calibration (intercept 0·143, slope 0·988). The shaded area represents two standard errors.
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