Fig. 6: Posterior distributions of the 47 regression coefficients in the Bayes-splenic hilum lymph node metastasis (SHLNM) model for SHLNM prediction in upper gastrointestinal cancer (UGC).

This figure presents the posterior distributions of the 47 regression coefficients obtained from the Bayes-SHLNM model, which was trained on data from 593 cases. The regression coefficients indicate the impact of various clinical, tumor-related, histological, and lymph node metastasis (LNM) factors on the probability of SHLN metastasis in UGC. a Clinical features: Include variables, such as age, sex, and neoadjuvant chemotherapy. b Tumor location: Describes the anatomical site of the tumor, including the upper third, anterior wall, and involvement of the greater curvature. c Tumor features: • Macroscopic types (0–5): Different macroscopic types of the tumor. • Tumor size: Represents the maximum tumor diameter, standardized as Z-scores. • Pathological T stage: Refers to the depth of tumor invasion into the gastric wall, discretized into six values corresponding to T1a (1), T1b (2), T2 (3), T3 (4), T4a (5), and T4b (6). d Histological features: The histological subtypes are classified as follows: • Papillary adenocarcinoma (pap). • Tubular adenocarcinoma (tub): ○ (1) Well differentiated (tub1). ○ (2) Moderately differentiated (tub2). • Poorly differentiated adenocarcinoma (por): ○ (1) Solid type (por1). ○ (2) Non-solid type (por2). • Signet-ring cell carcinoma (sig). • Mucinous adenocarcinoma (muc). e LNM (#1–#12a): Represents LNM across various nodal stations. Both coefficients for #4sb and #4sa showed values exceeding 0 within the 95% highest density interval, indicating their significant positive influence on predicting SHLNM. Additionally, the location of the tumor in the greater curvature, tumor size (Z-score), non-solid type of poorly differentiated adenocarcinoma (por2), LNM #11d, and LNM #12a exhibited positive parameter values, suggesting a higher likelihood of SHLN metastasis in the presence of these factors.