ArticleBMC cancer2026
Integrating spatial lymph node patterns and multimodal clinicopathological features to predict post-neoadjuvant recurrence in gastric adenocarcinoma: a machine learning nomogram.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveConventional TNM staging systems exhibit limited prognostic accuracy in gastric cancer patients following neoadjuvant therapy (NAT), creating an urgent need for novel risk-stratification tools. This study aimed to develop a machine learning-based nomogram integrating lymph node tumor regression grade (LN.TRG) and other clinicopathological features to improve recurrence prediction in this setting.
methodsA cohort of 112 gastric cancer patients receiving NAT was retrospectively analyzed. Clinical and pathological features, including lymph node tumor regression grade (LN.TRG) were evaluated. Univariable analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to identify recurrence-free survival (RFS)-associated predictors. A predictive nomogram was developed using multivariable Cox regression and validated via ROC curves, decision curve analysis (DCA), and calibration curves. Additional analyses explored the prognostic impact of metastatic lymph node location, residual tumor ratio in lymph nodes, and lymph node dissection count.
resultsSix key predictors—including LN.TRG and lymph node-positive rate—were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression. The model exhibited robust discrimination in both training (AUC: 0.836–0.886) and validation (AUC: 0.907–0.957) sets. DCA and calibration curves confirmed clinical utility and stability. Risk stratification via ggrisk plots and Kaplan-Meier analysis revealed significant separation between high- and low-risk groups (P < 0.001), outperforming traditional staging systems (ypStage/ycStage). A fitted curve demonstrated a negative correlation between recurrence risk and lymph node dissection count (P < 0.05). Location-specific analysis identified higher residual tumor ratios and hazard ratios (HR) in station No. 5 and No. 6 lymph nodes.
conclusionThe LN.TRG-integrated model enables precise risk stratification for post-NAT gastric cancer. Proactive dissection of lymph nodes, particularly station No. 5 and No. 6, may improve patient outcomes. This study offers insights to enhance survival and quality of life.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.