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Objective To investigate the predictive value of preoperative hematological inflammatory markers in gastric cancer (GC) with lymphovascular invasion(LVI). Methods A retrospective study was conducted on 341 GC patients who undergoing surgical resection and confirmed by pathological examination in Jiangsu Cancer Hospital from October 2020 to July 2022. The patients were divided into a positive group (n=163) and a negative group (n=178) based on the presence or absence of vascular cancer thrombi in postoperative pathology. Before operation, the following clinical data were collected, including neutrophil to lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio(LMR), fibrinogen-lymphocyte ratio (FLR) and common tumor markers. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of NLR, PLR and FLR for LVI to find the optimal cut-off values. Univariate logistic regression was used to analyze the influencing factors of LVI in GC patients, and a logistic regression model for predicting LVI in GC patients was constructed. Results Compared with those in the patients with LVI negative, the levels of NLR, PLR, FLR and carbohydrate antigen (CA)19-9, the tumor infiltration depth and the rate of lymph node metastasis rate significantly increased in LVI positive patients(P＜0.05). ROC curve analysis showed that the best cutoff values of NLR, PLR and FLR were 1.68, 115.29 and 1.96 respectively. The logistic regression model of LVI in GC patients showed that Logit(P)=-2.117+0.890×X1+0.286×X2+0.917×X3(X1=PLR, X2=cT, X3=lymph node metastasis) had a good model fit(χ=58.405, P＜0.01), with the prediction accuracy of 67.26% and an AUC of 0.733 (95%CI: 0.680-0.786, P＜0.01), and this model analysis suggested that PLR(＞115.29, OR=2.436, 95%CI: 1.386-4.282, P=0.002), cT stage (OR=1.331, 95%CI: 1.031-1.749, P=0.030) and lymph node metastasis(OR=2.503, 95%CI: 1.507-4.156, P＜0.001) were the independent risk factors for LVI in patients with GC. Conclusion Preoperative monitoring of PLR level has a certain predictive value in evaluating the occurrence of LVI in GC patients, and can be used as a supplementary indicator to judge the formation of LVI.

Objective: This study aimed to introduce a novel artificial intelligence (AI)-based robotic system for autonomous planning of spinal posterior decompression and verify its accuracy through a cadaveric model., Methods: Seventeen vertebrae from 3 cadavers were included in the study. Three thoracic vertebrae (T9-11) and 3 lumbar vertebrae (L3-5) were selected from each cadaver. After obtaining CT data, the robotic system independently planned the laminectomy path based on AI algorithms before the surgical procedure and automatically performed the decompression during the procedure. A postoperative CT scan was performed, and the deviation of each cutting plane from the preoperative plan was quantitatively analyzed to evaluate the accuracy and safety of the cuts. The duration of laminectomy was also recorded., Results: A total of 285 cuts were made on thoracic and lumbar vertebrae. The average duration for unilateral longitudinal cutting was 16.38 ± 4.76 minutes, while for transverse cutting it was 4.44 ± 1.52 minutes. In terms of accuracy assessment, 3 levels were divided based on the distance between the actual cutting plane and the preplanned plane: 77 (84%) were grade A, 15 (16%) were grade B, and none were grade C. Regarding safety assessment, 74 (80%) were designated safe (grade A), with 18 (20%) classified as uncertain (grade B)., Conclusions: The results confirm the accuracy and preliminary safety of the robotic system for autonomous planning and cutting of spinal decompression.