A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis

Chen, Jingyu; Wu, Lidong; Zhang, Zizhen; Zheng, Shu; Lin, Yaowang; Ding, Ning; Sun, Jiawei; Shi, Liuhong; Xue, Min

doi:10.1002/cam4.3680

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…Tumor size and TMN stage are significantly related to the survival of young gastric cancer patients (8). Many studies have proposed that age, race, tumor size, and depth may be risk factors for distant metastasis of gastric cancer (9).…”

Section: Introductionmentioning

confidence: 99%

Prediction of distant metastasis and survival prediction of gastric cancer patients with metastasis to the liver, lung, bone, and brain: research based on the SEER database

Lin¹,

Wang²,

Zhou³

et al. 2022

Ann Transl Med

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Background: Gastric cancer (GC) is a globally important disease. It is the 5th most common malignancy and the 4th most common cause of death from cancer in the world. Patients with GC are often at an advanced stage when they are first diagnosed, and their overall prognosis is poor due to locally advanced and distant metastasis. This study sought to establish a predictive model of GC distant metastasis and survival that can be used to guide individualized treatment.Methods: Patients diagnosed with GC from the Surveillance, Epidemiology, and End Results database were enrolled in the study. Univariate and multivariate logistic regression analyses were used to identify risk and prognostic factors for GC patients with distant metastasis. The factors were then used to construct nomograms to predict the probability of distant metastasis and the survival time of GC patients. Receiver operating characteristic (ROC) curve and decision curve analyses were used to verify the prediction ability of the nomograms. Results:We established a comprehensive nomogram to predict the survival time of GC patients and 4 nomograms to predict distant metastasis. Nomograms could help oncologists to formulate treatment strategies and provide hospice care under an overall management model.Conclusions: Establishing a prediction model for distant metastasis and the survival of GC patients is of great clinical significance. The prediction of distant metastasis could help clinicians to make individualized assessments of patients and formulate individualized examination measures. Survival prediction models could help oncologists to formulate good treatment strategies and provide hospice care.

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Section: Introductionmentioning

confidence: 99%

Prediction of distant metastasis and survival prediction of gastric cancer patients with metastasis to the liver, lung, bone, and brain: research based on the SEER database

Lin¹,

Wang²,

Zhou³

et al. 2022

Ann Transl Med

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show abstract

“…Due to the high 5-year survival rate of T1 patients, many scholars have ignored the possibility of distant metastasis in T1 patients, especially in recent years when endoscopic treatment has gradually replaced traditional radical surgery 9 , 17 . A recent study showed that the probability of distant metastasis in patients with stage T1 is 8.17% 18 . Therefore, it is necessary to explore the risk factors and prognosis of distant metastasis of T1 gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer

Tian

Liu

et al. 2023

Sci Rep

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Distant metastasis (DM) is relatively uncommon in T1 stage gastric cancer (GC). The aim of this study was to develop and validate a predictive model for DM in stage T1 GC using machine learning (ML) algorithms. Patients with stage T1 GC from 2010 to 2017 were screened from the public Surveillance, Epidemiology and End Results (SEER) database. Meanwhile, we collected patients with stage T1 GC admitted to the Department of Gastrointestinal Surgery of the Second Affiliated Hospital of Nanchang University from 2015 to 2017. We applied seven ML algorithms: logistic regression, random forest (RF), LASSO, support vector machine, k-Nearest Neighbor, Naive Bayesian Model, Artificial Neural Network. Finally, a RF model for DM of T1 GC was developed. The AUC, sensitivity, specificity, F1-score and accuracy were used to evaluate and compare the predictive performance of the RF model with other models. Finally, we performed a prognostic analysis of patients who developed distant metastases. Independent risk factors for prognosis were analysed by univariate and multifactorial regression. K-M curves were used to express differences in survival prognosis for each variable and subvariable. A total of 2698 cases were included in the SEER dataset, 314 with DM, and 107 hospital patients were included, 14 with DM. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. A combined analysis of seven ML algorithms in the training and test sets found that the RF prediction model had the best prediction performance (AUC: 0.941, Accuracy: 0.917, Recall: 0.841, Specificity: 0.927, F1-score: 0.877). The external validation set ROCAUC was 0.750. Meanwhile, survival prognostic analysis showed that surgery (HR = 3.620, 95% CI 2.164–6.065) and adjuvant chemotherapy (HR = 2.637, 95% CI 2.067–3.365) were independent risk factors for survival prognosis in patients with DM from stage T1 GC. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. ML algorithms had shown that RF prediction models had the best predictive efficacy to accurately screen at-risk populations for further clinical screening for metastases. At the same time, aggressive surgery and adjuvant chemotherapy can improve the survival rate of patients with DM.

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“…The value of this index was consistent with our study. In general, a few studies have applied AUC to predict the OS 26 , 28 , 33 . The AUC values of ROC were more than 60% for 1-, 3-, and 5-year of survival, which are compatible with our study.…”

Section: Discussionmentioning

confidence: 99%

Development of web-based dynamic nomogram to predict survival in patients with gastric cancer: a population-based study

Talebi

Borumandnia

Doosti

et al. 2022

Sci Rep

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Gastric cancer (GC) is the fifth most frequent malignancy worldwide and the third leading cause of cancer-associated mortality. The study’s goal was to construct a predictive model and nomograms to predict the survival of GC patients. This historical cohort study assessed 733 patients who underwent treatments for GC. The univariate and multivariable Cox proportional hazard (CPH) survival analyses were applied to identify the factors related to overall survival (OS). A dynamic nomogram was developed as a graphical representation of the CPH regression model. The internal validation of the nomogram was evaluated by Harrell’s concordance index (C-index) and time-dependent AUC. The results of the multivariable Cox model revealed that the age of patients, body mass index (BMI), grade of tumor, and depth of tumor elevate the mortality hazard of gastric cancer patients (P < 0.05). The built nomogram had a discriminatory performance, with a C-index of 0.64 (CI 0.61, 0.67). We constructed and validated an original predictive nomogram for OS in patients with GC. Furthermore, nomograms may help predict the individual risk of OS in patients treated for GC.

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A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 10 publications

References 30 publications

Prediction of distant metastasis and survival prediction of gastric cancer patients with metastasis to the liver, lung, bone, and brain: research based on the SEER database

Prediction of distant metastasis and survival prediction of gastric cancer patients with metastasis to the liver, lung, bone, and brain: research based on the SEER database

Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer

Development of web-based dynamic nomogram to predict survival in patients with gastric cancer: a population-based study

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