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Objective: To develop and validate a risk prediction model of 90-day mortality (90DM) using machine learning in a large multicenter cohort of patients undergoing gastric cancer resection with curative intent. Background: The 90DM rate after gastrectomy for cancer is a quality of care indicator in surgical oncology. There is a lack of well-validated instruments for personalized prognosis of gastric cancer. Methods: Consecutive patients with gastric adenocarcinoma who underwent potentially curative gastrectomy between 2014 and 2021 registered in the Spanish EURECCA Esophagogastric Cancer Registry database were included. The 90DM for all causes was the study outcome. Preoperative clinical characteristics were tested in four 90DM predictive models: Cross Validated Elastic regularized logistic regression method (cv-Enet), boosting linear regression (glmboost), random forest, and an ensemble model. Performance was evaluated using the area under the curve by 10-fold cross-validation. Results: A total of 3182 and 260 patients from 39 institutions in 6 regions were included in the development and validation cohorts, respectively. The 90DM rate was 5.6% and 6.2%, respectively. The random forest model showed the best discrimination capacity with a validated area under the curve of 0.844 [95% confidence interval (CI): 0.841-0.848] as compared with cv-Enet (0.796, 95% CI: 0.784-0.808), glmboost (0.797, 95% CI: 0.785-0.809), and ensemble model (0.847, 95% CI: 0.836-0.858) in the development cohort. Similar discriminative capacity was observed in the validation cohort. Conclusions: A robust clinical model for predicting the risk of 90DM after surgery of gastric cancer was developed. Its use may aid patients and surgeons in making informed decisions.

Extreme liver surgery carries a high rate of morbidity and mortality that seem to increase with age and with higher tumor volumes, according to the literature. It is a therapeutic option to consider in patients with low comorbidity suffering from malignant neoplasms that involve the hepatocaval confluence, when no other treatment with curative intention can be performed.

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Background The ideal extent of lymphadenectomy (LAD) in esophageal oncological surgery is debated. There is no evidence for improved survival after standardized paratracheal lymph node resection performing oncological esophagectomy. Lymph nodes from the lower paratracheal station are not standardly resected during 2-field Ivor-Lewis esophagectomy for esophageal cancer. The objective of this study was to evaluate the impact of lower paratracheal lymph node (LPL) resection on perioperative outcome during esophagectomy for cancer and analyze its relevance. Methods Retrospectively, we identified 200 consecutive patients operated in our center for esophageal cancer from January 2017 – December 2019. Patients with and without lower paratracheal LAD were compared regarding demographic data, tumor characteristics, operative details, postoperative complications, tumor recurrence and overall survival. Results 103 out of 200 patients received lower paratracheal lymph node resection. On average, five lymph nodes were resected in the paratracheal region and cancer infiltration was found in two patients. Those two patients suffered from neuroendocrine carcinoma and melanoma respectively. Cases with lower paratracheal lymph node yield had significantly less overall complicated procedures (p = 0.026). Regarding overall survival and recurrence rate no significant difference could be detected between both groups (p = 0.168 and 0.371 respectively). Conclusion The resection of lower paratracheal lymph nodes during esophagectomy remains debatable for distal squamous cell carcinoma or adenocarcinoma of the esophagus. Tumor infiltration was only found in rare cancer entities. Since resection can be performed safely, we recommend LPL resection on demand.