Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

Xu, Zongbin; Jing, Jing; Ma, Guiliang

doi:10.21147/j.issn.1000-9604.2020.06.11

Cited by 15 publications

(18 citation statements)

References 36 publications

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“…Prior research has been carried out to develop simple multivariable regression predictive models for GSRC. Xu et al developed a prognostic nomogram based on log odds of positive lymph nodes to predict the OS and CSS rate of GSRC [ 12 ]. However, the model was not good at predicting the 1-year OS rate (AUC: 0.768), and its 5-year OS rate prediction ability showed only a slight preponderance over AJCC.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, patients with GSRC often have a poorer prognosis than other types of GC [ 9 ]. According to the previous studies, GSRC is an independent factor for survival prediction, and the 5-year survival rate was 30%-46.1% [ 10 – 12 ]. Besides, medical decision-making in GSRC is particularly complex and requires weighing treatment benefit against tumor progression.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Chen

Lin

et al. 2022

Computational and Mathematical Methods in Medicine

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Background and Aims. Accurate prediction is essential for the survival of patients with nonmetastatic gastric signet ring cell carcinoma (GSRC) and medical decision-making. Current models rely on prespecified variables, limiting their performance and not being suitable for individual patients. Our study is aimed at developing a more precise model for predicting 1-, 3-, and 5-year overall survival (OS) in patients with nonmetastatic GSRC based on a machine learning approach. Methods. We selected 2127 GSRC patients diagnosed from 2004 to 2014 from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly partitioned them into a training and validation cohort. We compared the performance of several machine learning-based models and finally chose the eXtreme gradient boosting (XGBoost) model as the optimal method to predict the OS in patients with nonmetastatic GSRC. The model was assessed using the receiver operating characteristic curve (ROC). Results. In the training cohort, for predicting OS rates at 1-, 3-, and 5-year, the AUCs of the XGBoost model were 0.842, 0.831, and 0.838, respectively, while in the testing cohort, the AUCs of 1-, 3-, and 5-year OS rates were 0.749, 0.823, and 0.829, respectively. Besides, the XGBoost model also performed better when compared with the American Joint Committee on Cancer (AJCC) stage. The performance for this model was stably maintained when stratified by age and ethnicity. Conclusion. The XGBoost-based model accurately predicts the 1-, 3-, and 5-year OS in patients with nonmetastatic GSRC. Machine learning is a promising way to predict the survival outcomes of tumor patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Chen

Lin

et al. 2022

Computational and Mathematical Methods in Medicine

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“…A nomogram can intuitively display the risk by synthesizing and explaining the relative importance of various predictive factors. It has been used to predict the outcomes of many diseases[ 10 , 11 ].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a prognostic nomogram for colorectal cancer after surgery

Lai

et al. 2021

WJCC

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BACKGROUND A nomogram is a diagram that aggregates various predictive factors through multivariate regression analysis, which can be used to predict patient outcomes intuitively. Lymph node (LN) metastasis and tumor deposit (TD) conditions are two critical factors that affect the prognosis of patients with colorectal cancer (CRC) after surgery. At present, few effective tools have been established to predict the overall survival (OS) of CRC patients after surgery. AIM To screen out suitable risk factors and to develop a nomogram that predicts the postoperative OS of CRC patients. METHODS Data from a total of 3139 patients diagnosed with CRC who underwent surgical removal of tumors and LN resection from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results program. The data were divided into a training set ( n = 2092) and a validation set ( n = 1047) at random. The Harrell concordance index (C-index), Akaike information criterion (AIC), and area under the curve (AUC) were used to assess the predictive performance of the N stage from the American Joint Committee Cancer tumor-node-metastasis classification, LN ratio (LNR), and log odds of positive lymph nodes (LODDS). Univariate and multivariate analyses were utilized to screen out the risk factors significantly correlating with OS. The construction of the nomogram was based on Cox regression analysis. The C-index, receiver operating characteristic (ROC) curve, and calibration curve were employed to evaluate the discrimination and prediction abilities of the model. The likelihood ratio test was used to compare the sensitivity and specificity of the final model to the model with the N stage alone to evaluate LN metastasis. RESULTS The predictive efficacy of the LODDS was better than that of the LNR based on the C-index, AIC values, and AUC values of the ROC curve. Seven independent predictive factors, namely, race, age at diagnosis, T stage, M stage, LODDS, TD condition, and serum carcinoembryonic antigen level, were included in the nomogram. The C-index of the nomogram for OS prediction was 0.8002 (95%CI: 0.7839-0.8165) in the training set and 0.7864 (95%CI: 0.7604-0.8124) in the validation set. The AUC values of the ROC curve predicting the 1-, 3-, and 5-year OS were 0.846, 0.841, and 0.825, respectively, in the training set and 0.823, 0.817, and 0.835, respectively, in the validation test. Great consistency between the predicted and actual observed OS for the 1-, 3-, and 5-year OS in the training set and validation set was shown in the calibration curves. The final nomogram showed a better sensitivity and specificity than the nomogram with N stage alone for evaluating LN metastasis in both the training set (-4668.0 vs -4688.3, P < 0.001) and the validation set (-1919.5 vs -1919.8, P ...

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“…A nomogram based on body mass index category, tumor location, T and N stages was reported to have a more refined prognostic accuracy when compared to the eighth AJCC [ 43 ]. Additionally, another nomogram based on log odds of positive lymph nodes (LODDS) was more predictive of overall and cancer-specific survival for signet ring cell carcinoma than TNM staging alone [ 56 ]. Bando and colleagues devised a novel pre-treatment model that accurately predicted the overall survival in gastric cancer patients than AJCC TNM and recommended its use in patient counseling [ 57 ].…”

Section: Validation Of the 8th Ajcc Tnm Staging Systemmentioning

confidence: 99%

Revisiting the 8th AJCC system for gastric cancer: A review on validations, nomograms, lymph nodes impact, and proposed modifications

Mranda

Xiang

Zhou

et al. 2022

Annals of Medicine &Amp; Surgery

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Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

Cited by 15 publications

References 36 publications

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach

Development and validation of a prognostic nomogram for colorectal cancer after surgery

Revisiting the 8th AJCC system for gastric cancer: A review on validations, nomograms, lymph nodes impact, and proposed modifications

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