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Background The optimal treatment in patients with gastric cancer and peritoneal disease remains controversial. Some guidelines indicate palliative treatment only, while others consider surgical treatment in case of positive lavage cytology (CY+) or limited peritoneal disease. Here, we analyzed the role of peritoneal disease in patients with gastric cancer, and the prognostic relevance of response to neoadjuvant therapy. Methods In this retrospective cohort analysis, we analyzed patients with adenocarcinoma of the stomach or esophago-gastric junction from a single center operated between 2011 and 2019. According to histology and lavage cytology, patients were classified into four risk groups: (A) no peritoneal disease, (B) CY+ who converted to negative lavage cytology (CY−) after neoadjuvant chemotherapy, (C) CY+ without conversion after chemotherapy, and (D) patients with visible peritoneal metastasis. Results Overall, n = 172 patients were included. At initial presentation, n = 125 (73%) had no peritoneal disease, and about a third of patients (n = 47, 27%) had microscopic or macroscopic peritoneal disease. Among them, n = 14 (8%) were CY+ without visible peritoneal metastasis, n = 9 converted to CY− after chemotherapy, and in n = 5 no conversion was observed. Median overall survival was not reached in patients who had initially no peritoneal disease and in patients who converted after chemotherapy, resulting in 3-year survival rates of 65% and 53%. In contrast, median overall survival was reduced to 13 months (95% CI 8.7–16.7) in patients without conversion and was 16 months (95% CI 12–20.5) in patients with peritoneal metastasis without difference between the two groups (p = .364). The conversion rate from CY+ to CY− was significantly higher after neoadjuvant treatment with FLOT (5-fluorouracil plus leucovorin, oxaliplatin, and docetaxel) compared to ECF (epirubicin, cisplatin, and 5-fluorouracil) (p = 0.027). Conclusion Conversion of CY+ to CY− after neoadjuvant chemotherapy with FLOT is a significant prognostic factor for a better overall survival. Surgical treatment in well-selected patients should therefore be considered. However, peritoneal recurrence remains frequent despite conversion, urging for a better local control.

Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms manifest themselves only in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population case, the causal tail coefficient is shown to reveal the causal structure if the distribution follows a linear structural causal model. This holds even in the presence of latent common causes that have the same tail index as the observed variables. Based on a consistent estimator of the causal tail coefficient, we propose a computationally highly efficient algorithm that infers causal structure from finitely many data. We prove that our method consistently estimates the causal order and compare it to other well-established and non-extremal approaches in causal discovery on synthetic data. The code is available as an open-access R package on Github.

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