A pilot machine learning study for preoperative prediction of pathological lymph node status in PIPAC-treated gastric cancer in Kazakhstan: A single-center study
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Kathiresan Sivakumar 1 2 , Aidana Rakhmankulova 3 , Altay Kerimkulov 4 , Tomiris Sarina 4 , Aliya Kuanyshpekova 4 , Abduzhappar Gaipov 5 6 *
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1 School of Computing and Artificial Intelligence, Nazarbayev University, Astana, KAZAKHSTAN2 Private Institution “Nazarbayev University Research Administration”, Nazarbayev University, Astana, KAZAKHSTAN3 Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, KAZAKHSTAN4 Multidisciplinary Department, National Research Oncology Center, Astana, KAZAKHSTAN5 Department of Medicine, School of Medicine, Nazarbayev University, Astana, KAZAKHSTAN6 Department of Internal Medicine, Clinical Academic, University Medical Center, Astana, KAZAKHSTAN* Corresponding Author

Abstract

Pathological lymph-node status after neoadjuvant treatment and surgery (ypN) is prognostic in gastric cancer but unavailable before resection. We developed a pilot prediction model to estimate ypN risk from seven variables recorded before radical surgery in patients treated within a single-center PIPAC program; the timing of blood counts relative to neoadjuvant treatment could not be established. Of 62 patients, 47 with recorded ypN were analyzed (18 ypN-positive). Predictors were clinical T and N stage, tumor grade, age, body-mass index, white-cell count, and platelet count. Ridge logistic regression (primary), random forest, and Firth logistic regression were evaluated by cross-validation. Out-of-fold AUCs were 0.701 (95% confidence interval 0.539-0.851), 0.667 (0.488-0.831), and 0.703 (0.532-0.851), respectively; Brier scores were 0.227, 0.227, and 0.222 (prevalence-only reference 0.236). Performance was modest and imprecise; because the predictor panel was selected using the same cross-validation, estimates may be optimistic. These findings are an exploratory signal requiring independent validation.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Original Article

ELECTRON J GEN MED, Volume 23, Issue 6, December 2026, Article No: em764

https://doi.org/10.29333/ejgm/19775

Publication date: 04 Nov 2026

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