Background: Recent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LNM) in early gastric cancer (EGC), yet tools for prediction grounded in the Lauren classification remain scarce. Methods: Data of 6468 patients diagnosed with EGC from fifteen Chinese high-volume cancer centers between January 2005 and December 2015 were retrospectively analyzed. Utilizing multivariate logistic regression analysis and the multilayer perceptron (MLP) prediction algorithm, a nomogram and an artificial neural network (ANN) model were developed and validated, respectively, for predicting the likelihood of LNM in non-intestinal EGC cases. The models’ performances were evaluated and a comparative analysis of their parameters was undertaken. Subsequently, in-depth risk stratification analyses were performed around the two models. Results: Non-intestinal type EGC demonstrated an elevated LNM rate and inferior prognosis compared to the intestinal type. Both nomogram and ANN model were developed and performed well in discrimination, calibration and clinical utility. Notably, the ANN model surpassed the nomogram in specificity (95.8% vs. 71.3%, P < 0.001), positive predictive value (PPV) (62.0% vs. 36.2%, P < 0.001) and overall accuracy (82.7% vs. 70.5%, P < 0.001). Patients with different risk strata derived from the nomogram, ANN model, and their combined application exhibited significantly different outcomes. The extent of lymph node dissection significantly influenced prognoses in high-risk patients identified by the combined model, whereas the anatomical location of metastatic lymph nodes did not. Conclusion: The ANN model established in this study can screen the patients at high risk of LNM in non-intestinal type EGC more accurately. Considering the high extragastric LNM rate observed in the high-risk stratum, radical gastrectomy combined with D2 lymph node dissection is strongly recommended.

Increasing evidence suggests that non-operative management (NOM) with antibiotics could serve as a safe alternative to surgery for the treatment of uncomplicated acute appendicitis (AA). However, accurately differentiating between uncomplicated and complicated AA remains challenging. Our aim was to develop and validate machine-learning-based diagnostic models to differentiate uncomplicated from complicated AA. This was a multicenter cohort trial conducted from January 2021 and December 2022 across five tertiary hospitals. Three distinct diagnostic models were created, namely, the clinical-parameter-based model, the CT-radiomics-based model, and the clinical-radiomics-fused model. These models were developed using a comprehensive set of eight machine-learning algorithms, which included logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), gradient boosting (GB), K-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), and multi-layer perceptron (MLP). The performance and accuracy of these diverse models were compared. All models exhibited excellent diagnostic performance in the training cohort, achieving a maximal AUC of 1.00. For the clinical-parameter model, the GB classifier yielded the optimal AUC of 0.77 (95% confidence interval [CI]: 0.64–0.90) in the testing cohort, while the LR classifier yielded the optimal AUC of 0.76 (95% CI: 0.66–0.86) in the validation cohort. For the CT-radiomics-based model, GB classifier achieved the best AUC of 0.74 (95% CI: 0.60–0.88) in the testing cohort, and SVM yielded an optimal AUC of 0.63 (95% CI: 0.51–0.75) in the validation cohort. For the clinical-radiomics-fused model, RF classifier yielded an optimal AUC of 0.84 (95% CI: 0.74–0.95) in the testing cohort and 0.76 (95% CI: 0.67–0.86) in the validation cohort. An open-access, user-friendly online tool was developed for clinical application. This multicenter study suggests that the clinical-radiomics-fused model, constructed using RF algorithm, effectively differentiated between complicated and uncomplicated AA.

BACKGROUND The prognosis of patients with poorly differentiated gastric neuroendocrine neoplasms (PDGNENs) is dismal and related research is limited. AIM To investigate the prognostic factors, and validate a novel prognostic nomogram for PDGNEN patients. METHODS We conducted a retrospective study using clinical and pathological data from PDGNEN patients treated at the First Medical Center of the Chinese PLA General Hospital from January 2000 to June 2023. Overall survival (OS) differences were assessed with the Log-rank test and Kaplan-Meier survival curves. Cox regression analysis identified independent risk factors for prognosis. Model performance was evaluated using Harrell’s concordance index, receiver operating characteristic analysis, area under the curve, calibration curves, and decision curve analysis (UDC), including the area under the UDC. RESULTS The study included 336 patients (227 with neuroendocrine carcinoma and 109 with mixed adenoneuroendocrine carcinoma). The average age was 62.7 years. The cohort comprised 80 (24.7%) patients in stage I, 146 (42.9%) in stage II, 62 (18.1%) in stage III, and 48 (14.3%) in stage IV. Significant differences in OS were observed across tumor-node-metastasis stages (P < 0.001). Multivariate analysis showed age, Ki-67 index, invasion depth, lymph node metastasis, distant metastasis, and platelet-to-lymphocyte ratio as independent risk factors. We developed a nomogram with a concordance index of 0.779 (95% confidence interval: 0.743-0.858). Receiver operating characteristic analysis showed area under the curves for 1-year, 3-year, and 5-year OS predictions of 0.865, 0.850, and 0.890, respectively. The calibration curve demonstrated good agreement with actual outcomes. The area under the UDC for the nomogram vs the 8th American Joint Committee on Cancer tumor-node-metastasis staging system were 0.047 vs 0.027, 0.291 vs 0.179, and 0.376 vs 0.216 for 1-year, 3-year, and 5-year OS, respectively. CONCLUSION PDGNENs are predominantly found in older men, often in advanced stages at diagnosis, resulting in poor prognosis. The established nomogram demonstrates strong predictive capability and clinical utility.

Acute kidney injury (AKI) is a serious adverse event often overlooked following major abdominal surgery. While radical gastrectomy stands as the primary curative method for treating gastric cancer patients, little information exists regarding AKI post-surgery. Hence, this study aimed to ascertain the incidence rate, risk factors, and consequences of AKI among patients undergoing radical gastrectomy. This was a population-based, retrospective cohort study. The incidence of AKI was calculated. Multivariate logistic regression was used to identify independent predictors of AKI. Survival curves were plotted by using the Kaplan–Meier method and differences in survival rates between groups were analyzed by using the log-rank test. Of the 2,875 patients enrolled in this study, 61 (2.1%) developed postoperative AKI, with AKI Network 1, 2, and 3 in 50 (82.0%), 6 (9.8%), and 5 (8.2%), respectively. Of these, 49 patients had fully recovered by discharge. Risk factors for AKI after radical gastrectomy were preoperative hypertension (odds ratio [OR], 1.877; 95% CI, 1.064–3.311; P = 0.030), intraoperative blood loss (OR, 1.001; 95% CI, 1.000–1.002; P = 0.023), operation time (OR, 1.303; 95% CI, 1.030–1.649; P = 0.027), and postoperative intensive care unit (ICU) admission (OR, 4.303; 95% CI, 2.301–8.045; P < 0.001). The probability of postoperative complications, mortality during hospitalization, and length of stay in patients with AKI after surgery were significantly higher than those in patients without AKI. There was no statistical difference in overall survival (OS) rates between patients with AKI and without AKI (1-year, 3-year, 5-year overall survival rates of patients with AKI and without AKI were 93.3% vs 92.0%, 70.9% vs 73.6%, and 57.1% vs 67.1%, respectively, P = 0.137). AKI following radical gastrectomy is relatively rare and typically self-limited. AKI is linked with preoperative hypertension, intraoperative blood loss, operation time, and postoperative ICU admission. While AKI raises the likelihood of postoperative complications, it does not affect OS.

Objective: This study aimed to propose and validate a new and practical lymph node (LN) staging strategy to mitigate staging migration due to examined LN (ELN) retrieval and improve the accuracy of prognostic evaluation for early gastric cancer (EGC) patients. Background: EGC patients often face staging inaccuracies due to inadequate ELNs, as there is no clear standard for ELN retrieval requirements. Methods: From an initial cohort of 7001 EGC patients across fifteen large institutions in China, 6566 eligible patients were included as the training and validation cohort after applying predefined exclusion criteria. We analyzed these data to determine the optimal cutoff value for the number of ELNs, construct a prediction model, and propose a new LN staging method distinct from that of the latest AJCC guidelines. Subsequently, 2094 patients (from 2326 with survival data available in the multicenter dataset) and 1944 patients (from 5262 initially retrieved from the SEER database after exclusions) were included as independent test cohorts to evaluate model performance, including the correlation between ELNs and metastatic LNs (MLNs) and survival differences. Results: This study found that ≤ 20 ELNs were inadequate for accurate LN evaluation in EGC patients. In the test cohort, MLNs was positively associated with ELNs. A model was constructed, and accurate MLNs could be displayed after correction for patients with inadequate ELNs. Survival analysis revealed significant differences between patients with different pN_M (pN _modified) stages in both the multicenter test cohort and the SEER test cohort. Conclusion: Model-corrected pN_M staging may improve staging migration and survival prediction than the AJCC staging, showing great clinical applicability for EGC patients with ELN ≤ 20. A flowchart outlines a study on early gastric cancer patients, examining lymph nodes and metastasis risks.A study of 6566 early gastric cancer (EGC) patients across 15 Chinese centers determined the optimal examined lymph node (ELN) cutoff for lymph node metastasis (LNM) risk. Data was split into training and validation cohorts. A logistic regression model predicted LNM, correcting for insufficient ELNs and predicting metastatic lymph nodes (MLNs). 2094 multicenter and 1944 SEER EGC patients with survival data were tested. AJCC staging was redefined based on corrected ELNs/MLNs, grouped into PN stages and survival analysis was performed comparing pre- and post-correction differences.