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      Machine learning to predict early recurrence after oesophageal cancer surgery

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          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.

          Abstract

          Early recurrence after surgery for adenocarcinoma of the oesophagus is common. A risk prediction model was derived using modern machine learning methods that accurately predicts risk of early recurrence using postoperative pathology.

          Machine learning may help

          Translated abstract

          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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          Most cited references50

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          Random Forests

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            Regularization and variable selection via the elastic net

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              Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer.

              A regimen of epirubicin, cisplatin, and infused fluorouracil (ECF) improves survival among patients with incurable locally advanced or metastatic gastric adenocarcinoma. We assessed whether the addition of a perioperative regimen of ECF to surgery improves outcomes among patients with potentially curable gastric cancer. We randomly assigned patients with resectable adenocarcinoma of the stomach, esophagogastric junction, or lower esophagus to either perioperative chemotherapy and surgery (250 patients) or surgery alone (253 patients). Chemotherapy consisted of three preoperative and three postoperative cycles of intravenous epirubicin (50 mg per square meter of body-surface area) and cisplatin (60 mg per square meter) on day 1, and a continuous intravenous infusion of fluorouracil (200 mg per square meter per day) for 21 days. The primary end point was overall survival. ECF-related adverse effects were similar to those previously reported among patients with advanced gastric cancer. Rates of postoperative complications were similar in the perioperative-chemotherapy group and the surgery group (46 percent and 45 percent, respectively), as were the numbers of deaths within 30 days after surgery. The resected tumors were significantly smaller and less advanced in the perioperative-chemotherapy group. With a median follow-up of four years, 149 patients in the perioperative-chemotherapy group and 170 in the surgery group had died. As compared with the surgery group, the perioperative-chemotherapy group had a higher likelihood of overall survival (hazard ratio for death, 0.75; 95 percent confidence interval, 0.60 to 0.93; P=0.009; five-year survival rate, 36 percent vs. 23 percent) and of progression-free survival (hazard ratio for progression, 0.66; 95 percent confidence interval, 0.53 to 0.81; P<0.001). In patients with operable gastric or lower esophageal adenocarcinomas, a perioperative regimen of ECF decreased tumor size and stage and significantly improved progression-free and overall survival. (Current Controlled Trials number, ISRCTN93793971 [controlled-trials.com].). Copyright 2006 Massachusetts Medical Society.
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                Author and article information

                Contributors
                tju@soton.ac.uk
                Journal
                Br J Surg
                Br J Surg
                10.1002/(ISSN)1365-2168
                BJS
                The British Journal of Surgery
                John Wiley & Sons, Ltd (Chichester, UK )
                0007-1323
                1365-2168
                30 January 2020
                July 2020
                : 107
                : 8 ( doiID: 10.1002/bjs.v107.8 )
                : 1042-1052
                Affiliations
                [ 1 ] Cancer Sciences Unit University of Southampton Southampton UK
                [ 2 ] Department of Public Health Sciences and Medical Statistics University of Southampton Southampton UK
                [ 3 ] Department of Surgery Nottingham University Hospitals NHS Trust Nottingham UK
                [ 4 ] Department of Surgery Portsmouth Hospitals NHS Trust Portsmouth UK
                [ 5 ] Department of Upper Gastrointestinal Surgery University Hospitals Birmingham NHS Foundation Trust Birmingham UK
                [ 6 ] Cambridge Oesophagogastric Centre Addenbrookes Hospital, Cambridge University Hospitals Foundation Trust Cambridge UK
                [ 7 ] Hutchison/Medical Research Council Cancer Unit University of Cambridge Cambridge UK
                [ 8 ] Centre for Cancer Research and Cell Biology Queen's University Belfast Belfast UK
                [ 9 ] Department of Surgery University Medical Centre Utrecht the Netherlands
                Author notes
                [*] [* ] Correspondence to: Professor T. J. Underwood, Cancer Sciences Unit, University of Southampton, Tremona Road, Southampton SO16 6YD, UK (e‐mail: tju@ 123456soton.ac.uk ; @TimTheSurgeon, @SaqRahman, @Robwalker27, @uoscares, @HeartburnCancer)
                [†]

                Members of the OCCAMS Consortium are co‐authors of this study and are listed in Appendix S1 (supporting information)

                Author information
                https://orcid.org/0000-0001-9489-0021
                Article
                BJS11461
                10.1002/bjs.11461
                7299663
                31997313
                330ee12c-5be5-402a-999a-e1f747c407c4
                © 2020 The Authors. BJS published by John Wiley & Sons Ltd on behalf of BJS Society Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 August 2019
                : 11 October 2019
                : 13 November 2019
                Page count
                Figures: 3, Tables: 5, Pages: 11, Words: 5809
                Funding
                Funded by: Programme Grant from Cancer Research UK
                Award ID: RG81771
                Award ID: RG84119
                Funded by: Cancer Research UK and Royal College of Surgeons of England Advanced Clinician Scientist Fellowship
                Award ID: A23924
                Categories
                Upper GI
                Original Article
                Original Articles
                Custom metadata
                2.0
                July 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.4 mode:remove_FC converted:26.06.2020

                Surgery
                Surgery

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