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Eur J Surg Oncol ; 2020 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-32044202


BACKGROUND: and purpose: For gastric cancer patients with peritoneal metastasis (GCPM), there is no universally accepted prognostic staging system. This study aimed to validate the predictive ability of the 15th peritoneal metastasis staging system (P1abc) of the Japanese Classification of Gastric Carcinoma (JCGC). METHODS: The data of 309 GCPM patients from July 2007 to July 2017 were retrospectively analyzed. This study compared the prognosis prediction performances of P1abc, the previous JCGC PM staging (P123) and Gilly staging systems. RESULTS: The survival curve revealed a significant difference in overall survival (OS) predicted by P1abc, P123 and Gilly staging (all P < 0.05), and the survival of the two adjacent substages were well distinguished by P1abc but not by P123 and Gilly staging. Both P123 and Gilly staging were substituted with P1abc staging in a 2-step multivariate analysis. The results showed that P1abc staging was superior to both P123 and Gilly staging in its discriminatory ability (C-index), predictive accuracy (AIC) and predictive homogeneity (likelihood ratio chi-square). A stratified analysis by different therapies indicated that for the P1a and P1b patients, OS following palliative resection combined with palliative chemotherapy (PRCPC) was better than that after palliative resection (PR) or palliative chemotherapy (PC) alone (P < 0.05). For the P1c patients, OS after receiving PC was significantly superior to that after receiving PRCPC or PR (P = 0.021). CONCLUSION: P1abc staging is superior to P123 and Gilly staging in predicting the survival of GCPM patients. Surgeons can provide these patients with appropriate treatment options according to the corresponding substages within P1abc.

World J Gastroenterol ; 25(43): 6451-6464, 2019 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-31798281


BACKGROUND: Because of the powerful abilities of self-learning and handling complex biological information, artificial neural network (ANN) models have been widely applied to disease diagnosis, imaging analysis, and prognosis prediction. However, there has been no trained preoperative ANN (preope-ANN) model to preoperatively predict the prognosis of patients with gastric cancer (GC). AIM: To establish a neural network model that can predict long-term survival of GC patients before surgery to evaluate the tumor condition before the operation. METHODS: The clinicopathological data of 1608 GC patients treated from January 2011 to April 2015 at the Department of Gastric Surgery, Fujian Medical University Union Hospital were analyzed retrospectively. The patients were randomly divided into a training set (70%) for establishing a preope-ANN model and a testing set (30%). The prognostic evaluation ability of the preope-ANN model was compared with that of the American Joint Commission on Cancer (8th edition) clinical TNM (cTNM) and pathological TNM (pTNM) staging through the receiver operating characteristic curve, Akaike information criterion index, Harrell's C index, and likelihood ratio chi-square. RESULTS: We used the variables that were statistically significant factors for the 3-year overall survival as input-layer variables to develop a preope-ANN in the training set. The survival curves within each score of the preope-ANN had good discrimination (P < 0.05). Comparing the preope-ANN model, cTNM, and pTNM in both the training and testing sets, the preope-ANN model was superior to cTNM in predictive discrimination (C index), predictive homogeneity (likelihood ratio chi-square), and prediction accuracy (area under the curve). The prediction efficiency of the preope-ANN model is similar to that of pTNM. CONCLUSION: The preope-ANN model can accurately predict the long-term survival of GC patients, and its predictive efficiency is not inferior to that of pTNM stage.

World J Clin Cases ; 7(21): 3419-3435, 2019 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-31750326


BACKGROUND: The incidence of proximal gastric cancer (GC) is increasing, and methods for the prediction of the long-term survival of proximal GC patients have not been well established. AIM: To develop nomograms for the prediction of long-term survival among proximal GC patients. METHODS: Between January 2007 and June 2013, we prospectively collected and retrospectively analyzed the medical records of 746 patients with proximal GC, who were divided into a training set (n = 560, 75%) and a validation set (n = 186, 25%). A Cox regression analysis was used to identify the preoperative and postoperative risk factors for overall survival (OS). RESULTS: Among the 746 patients examined, the 3- and 5-year OS rates were 66.1% and 58.4%, respectively. In the training set, preoperative T stage (cT), N stage (cN), CA19-9, tumor size, ASA core, and 3- to 6-mo weight loss were incorporated into the preoperative nomogram to predict the OS. In addition to these variables, lymphatic vascular infiltration (LVI), postoperative tumor size, T stage, N stage, blood transfusions, and complications were incorporated into the postoperative nomogram. All calibration curves used to determine the OS probability fit well. In the training set, the preoperative nomogram achieved a C-index of 0.751 [95% confidence interval (CI): 0.732-0.770] in predicting OS and accurately stratified the patients into four prognostic subgroups (5-year OS rates: 86.8%, 73.0%, 43.72%, and 20.9%, P < 0.001). The postoperative nomogram had a C-index of 0.758 in predicting OS and accurately stratified the patients into four prognostic subgroups (5-year OS rates: 82.6%, 74.3%, 45.9%, and 18.9%, P < 0.001). CONCLUSION: The nomograms accurately predicted the pre- and postoperative long-term survival of proximal GC patients.

World J Gastroenterol ; 25(37): 5641-5654, 2019 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-31602164


BACKGROUND: Robotic surgery has been considered to be significantly better than laparoscopic surgery for complicated procedures. AIM: To explore the short-term effect of robotic and laparoscopic spleen-preserving splenic hilar lymphadenectomy (SPSHL) for advanced gastric cancer (GC) by Huang's three-step maneuver. METHODS: A total of 643 patients who underwent SPSHL were recruited from April 2012 to July 2017, including 35 patients who underwent robotic SPSHL (RSPSHL) and 608 who underwent laparoscopic SPSHL (LSPSHL). One-to-four propensity score matching was used to analyze the differences in clinical data between patients who underwent robotic SPSHL and those who underwent laparoscopic SPSHL. RESULTS: In all, 175 patients were matched, including 35 patients who underwent RSPSHL and 140 who underwent LSPSHL. After matching, there were no significant differences detected in the baseline characteristics between the two groups. Significant differences in total operative time, estimated blood loss (EBL), splenic hilar blood loss (SHBL), splenic hilar dissection time (SHDT), and splenic trunk dissection time were evident between these groups (P < 0.05). Furthermore, no significant differences were observed between the two groups in the overall noncompliance rate of lymph node (LN) dissection (62.9% vs 60%, P = 0.757), number of retrieved No. 10 LNs (3.1 ± 1.4 vs 3.3 ± 2.5, P = 0.650), total number of examined LNs (37.8 ± 13.1 vs 40.6 ± 13.6, P = 0.274), and postoperative complications (14.3% vs 17.9%, P = 0.616). A stratified analysis that divided the patients receiving RSPSHL into an early group (EG) and a late group (LG) revealed that the LG experienced obvious improvements in SHDT and length of stay compared with the EG (P < 0.05). Logistic regression showed that robotic surgery was a significantly protective factor against both SHBL and SHDT (P < 0.05). CONCLUSION: RSPSHL is safe and feasible, especially after overcoming the early learning curve, as this procedure results in a radical curative effect equivalent to that of LSPSHL.