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Objective: To establish a neural network model for predicting lymph node metastasis in patients with stage II-III gastric cancer. Methods: Case inclusion criteria: (1) gastric adenocarcinoma diagnosed by pathology as stage II-III (the 8th edition of AJCC staging); (2) no distant metastasis of liver, lung and abdominal cavity in preoperative chest film, abdominal ultrasound and upper abdominal CT; (3) undergoing R0 resection. Case exclusion criteria: (1) receiving preoperative neoadjuvant chemotherapy or radiotherapy; (2) incomplete clinical data; (3) gastric stump cancer.Clinicopathological data of 1231 patients with stage II-III gastric cancer who underwent radical surgery at the Fujian Medical University Union Hospital from January 2010 to August 2014 were retrospectively analyzed. A total of 1035 patients with lymph node metastasis were confirmed after operation, and 196 patients had no lymph node metastasis. According to the postoperative pathologic staging. 416 patients (33.8%) were stage Ⅱ and 815 patients (66.2%) were stage III. Patients were randomly divided into training group (861/1231, 69.9%) and validation group (370/1231, 30.1%) to establish an artificial neural network model (N+-ANN) for the prediction of lymph node metastasis. Firstly, the Logistic univariate analysis method was used to retrospectively analyze the case samples of the training group, screen the variables affecting lymph node metastasis, determine the variable items of the input point of the artificial neural network, and then the multi-layer perceptron (MLP) to train N+-ANN. The input layer of N+-ANN was composed of the variables screened by Logistic univariate analysis. Artificial intelligence analyzed the status of lymph node metastasis according to the input data and compared it with the real value. The accuracy of the model was evaluated by drawing the receiver operating characteristic (ROC) curve and obtaining the area under the curve (AUC). The ability of N+-ANN was evaluated by sensitivity, specificity, positive predictive values, negative predictive values, and AUC values. Results: There were no significant differences in baseline data between the training group and validation group (all P>0.05). Univariate analysis of the training group showed that preoperative platelet to lymphocyte ratio (PLR), preoperative systemic immune inflammation index (SII), tumor size, clinical N (cN) stage were closely related to postoperative lymph node metastasis. The N+-ANN was constructed based on the above variables as the input layer variables. In the training group, the accuracy of N+-ANN for predicting postoperative lymph node metastasis was 88.4% (761/861), the sensitivity was 98.9% (717/725), the specificity was 32.4% (44/136), the positive predictive value was 88.6% (717/809), the negative predictive value was 84.6% (44/52), and the AUC value was 0.748 (95%CI: 0.717-0.776). In the validation group, N+-ANN had a prediction accuracy of 88.4% (327/370) with a sensitivity of 99.7% (309/310), specificity of 30.0% (18/60), positive predictive value of 88.0% (309/351), negative predictive value of 94.7% (18/19), and an AUC of 0.717 (95%CI:0.668-0.763). According to the individualized lymph node metastasis probability output by N+-ANN, the cut-off values of 0-50%, >50%-75%, >75%-90% and >90%-100% were applied and patients were divided into N0 group, N1 group, N2 group and N3 group. The overall prediction accuracy of N+-ANN for pN staging in the training group and the validation group was 53.7% and 54.1% respectively, while the overall prediction accuracy of cN staging for pN staging in the training group and the validation group was 30.1% and 33.2% respectively, indicating that N+-ANN had a better prediction than cN stage. Conclusions: The N+-ANN constructed in this study can accurately predict postoperative lymph node metastasis in patients with stage Ⅱ-Ⅲ gastric cancer. The N+-ANN based on individualized lymph node metastasis probability has better accurate prediction for pN staging as compared to cN staging.
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Humans , Artificial Intelligence , Lymph Nodes/pathology , Lymphatic Metastasis , Neoplasm Staging , Neural Networks, Computer , Prognosis , Retrospective Studies , Stomach Neoplasms/surgeryABSTRACT
Objective: To explore the risk factors of postoperative complications after radical gastrectomy + D2 lymphadenectomy and establish a predictive nomogram model. Methods: From July 2016 to June 2019, 1 705 patients who received radical gastrectomy + D2 lymphadenectomy in the Department of Gastrointestinal Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine were collected. According to Clavien-Dindo grading system, the postoperative complications were graded, and the risk factors of postoperative complications ≥grade Ⅱ were analyzed by χ2 test. Multivariate Logistic regression was used to analyze the independent risk factors of postoperative complications ≥grade Ⅱ. According to the selected independent risk factors, the nomogram model was established. For verification, above patients were used as the training set, and 612 patients undergoing the same operation in this department from July to December 2019 were used as the validation set. Results: A total of 416 (24.4%) gastric cancer patients had postoperative complications. Multivariate Logistic regression analysis showed that male (OR=1.507, P=0.002), age ≥60 years old (OR=1.962, P=0.001), maximum diameter of tumor ≥5 cm (OR=1.456, P=0.002) and total gastrectomy (OR=1.313, P=0.026) were independent risk factors for postoperative complications ≥ grade Ⅱ. Based on these independent risk factors, the nomogram was established and presented good discrimination and predictive consistency in training set and validation set. Conclusion: The nomogram based on these four independent risk factors has a good predictive performance in predicting postoperative complications after radical gastrectomy for gastric cancer, and has a certain clinical application and reference value.
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OBJECTIVE: To analyze the factors influencing postpyloric placement of spiral nasoenteral feeding tube(NET) in neurocritical care patients and establish a visualized prediction model. METHODS: Patients in Neurological Intensive Care Unit(NICU)who undertook postpyloric placement of NET after receiving prokinetics from Apr 2012 to Mar 2018 were included for retrospective analysis. The patients were divided into the success and failure group base on whether the tube tip entered into duodenum(or beyond)or not confirmed by bedside X-ray 24 hours later. The baseline data, APACHE Ⅱ score(acute physiology and chronic health evaluation Ⅱ), AGI grade(acute gastrointestinal injury), therapeutic measures and agents administered were recorded. Univariate and multivariate Logistic regression analysis was used to identify the potential factors affecting the postpyloric placement of NET. Based on those factors, a predicting model was established and visualized into an easy-to-use nomogram. RESULTS: A total of 241 patients including146 male and 95 female were enrolled for the study, with an median age of 58 years, median APACHEⅡscore of 20, median AGI of Ⅰ.The placement succeeded in 119(49.4%) of 241 patients. Logistic regression analysis demonstrated that APACHE Ⅱ score, sedatives and analgesics, vasopressors and AGI grade were among the influencing factors. A prediction model with a ROC-AUC of 0.8002 were established and visualized into a nomogram. CONCLUSION: APACHE Ⅱ score, sedatives and analgesics, vasopressors and AGI grade are the factors influencing success of postpyloric NET placement in neurocritical care patients, which incorporate a predicting model that can be visualized into a nomogram. The nomogram provided intensivists an easy-to-use decision support tool in NET placements.
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Objective To study the mathematical predicting model of parotid DVH for the NPC IMRT planning, and its accuracy with the analysis of medical data. Methods 50 NPC radiotherapy treatment plans with same beam setup were chosen as sample data set, then their parotid DVHs and distance of voxels in the parotid to the target volumes were calculated with self-developed program to form the distance to target histogram ( DTHs);principal component analysis was applied to DVHs and DTHs to acquire their principal components ( PCs) ,and then nonlinear multiple variable regression was used to model correlation between the DTHs' PCs, parotids volume, PTVs and the DVHs. Another 10 plans were chosen as test data set to evaluate the efficacy and accuracy of the final model by comparing the DVHs calculated from our model with those calculated from the TPS. Results Up to 97% information of DTHs and DVHs can be represented with 2 to 3 components, the average fitting error of sample data set was (0±3. 5)%;in the 10 test cases, the shapes of DVH curves calculated from predicting model was highly the same with those from the TPS, the average modeling error was (-0.7± 4. 4)%,the accuracy of prediction was up 95%. Conclusions Our developed model can be used as a quality evaluating tool to predict and assure the dose distribution in parotid of NPC radiotherapy treatment planning effectively and accurately.
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Objective To establish a model to predict the clinical response of neoadjuvant chemotherapy for nasopharyngeal car‐cinoma ,and provide basis for the individual treatment .Methods The clinical data of 63 cases of advanced nasopharyngeal carcinoma patients who have received neoadjuvant chemotherapy in the past 2 years were analyzed retrospectively .Univariate and multivariate analyses were performed using the Logistic analyses to identify efficacy factors .Results The response rate in nasopharyngeal tumor and lymph node metastasis were 39 .7% and 50 .8% ,respectively .Single factor analysis showed that patients with no distant metas‐tasis ,cranial nerve inviolated ,EBV negative and high expression of Ki67 were more sensitive to therapy .Logistic analysis showed that the influencing factors for the effect of the new chemotherapy include :distant metastasis ,cranial nerve inviolated and EBV . Thus ,the prediction model would be:Logit= -0 .470 -2 .863 × distant metastasis + 1 .328 × cranial nerve invasion+ 3 .639 × EBV ,its sensitivity ,specificity ,positive predictive value and negative predictive value were 79 .4% ,82 .8% ,84 .4% and 77 .4% . Conclusion The distant metastasis ,cranial nerve invasion and EBV infection were important predictive factors for neoadjuvant chemotherapy of nasopharyngeal carcinoma .This model could be used to predict the response of patients with nasopharyngeal carci‐noma .
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With the development of the food industry in China,it has been found that food safety is becoming the biggest issue in the food manufacture and logistics. Accurate and timely to establish a risk assessment method in produce market is the challenge for food safety system. Predictive microbiology is a core early warning technology in the food safety risk assessment. According to the microorganism predicting model,the pathogen and spoilage microorganism's growth in food can be fast judgment in advance. And it plays an important part in controlling the growth of pathogen and the spoilage microorganism in food. This paper summarized the predictive microbiology model's establishment and the present research situation,and discussed the present situation and application of predictive microbiology in food safety risk assessment. The future trend of predictive microbiology in food safety risk assessment was prospected as well.