Evidence map›Paper›PMID 42738334›Full record

ArticleCancers2026

A Logistic Regression Model Integrating Flow Cytometry-Derived Immune Cell Profiles and Hematological Parameters for Preoperative Prediction of Peritoneal Metastasis in Gastric Cancer.

Ruihu Zhao, Yuming Ju, Zhichao Yu, Yingwei Xue, Hongjiang Song

Abstract read
In one paragraph

Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ruihu ZhaoDepartment of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin 150081, China.
Yuming JuDepartment of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin 150081, China.
Zhichao YuDepartment of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin 150081, China.
Yingwei XueDepartment of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin 150081, China.
Hongjiang SongDepartment of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin 150081, China.

Funding

Wu Jieping Medical Foundation 320.6750.2022-07-13
6 · The paper itself

Abstract

backgroundPeritoneal metastasis (PM) is a lethal and often occult event in advanced gastric cancer (GC), and its preoperative detection remains difficult. The role of peripheral NK and NKT-like cells in predicting PM has not been well defined.

methodsWe retrospectively analyzed 433 patients who underwent surgery for GC from 2016 to 2020. Patients were stratified by PM status and randomly assigned to training and held-out internal validation cohorts. Flow cytometry-derived lymphocyte subset percentages were treated as compositional data, and NK and NKT-like cell variables were entered into the model after log-ratio transformation. Candidate variables were selected using LASSO logistic regression in the training cohort and further assessed by multivariable logistic regression. Model performance was evaluated using AUROC, AUPRC, and confusion matrices in the validation cohort. Nomogram and SHAP analyses were used for model interpretation.

resultsSeven predictors were retained in the final model: log-ratio-transformed NKT-like cells, direct bilirubin, prealbumin, lymphocyte count, lactate dehydrogenase, CA125, and log-ratio-transformed NK cells. The model achieved an AUROC of 0.879 (95% CI: 0.805-0.936) and an AUPRC of 0.698 (95% CI: 0.524-0.848) in the validation cohort. At the conventional threshold of 0.50, the model achieved an accuracy of 0.915, sensitivity of 0.821, specificity of 0.941, PPV of 0.793, and NPV of 0.950. At the Youden-optimal threshold of 0.101, sensitivity, specificity, accuracy, PPV, and NPV were 1.000, 0.892, 0.915, 0.718, and 1.000, respectively.

conclusionsA preoperative model integrating log-ratio-transformed NK/NKT-like cell variables and routine hematological parameters showed good ability to identify PM in GC and may help select patients for diagnostic laparoscopy or closer preoperative evaluation.

Indexed as

gastric cancerlogistic regressionNK cellsNKT−like cellsperitoneal metastasis

Identifiers

PMID42738334
PMCPMC13565575

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.