Positive node‐ratio in curative‐intent treatment for gastric cancer is a strong independent prognostic factor for 5‐year overall survival

Wohnrath, Durval R.; Araújo, Raphael L. C.

doi:10.1002/jso.25755

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…As depicted in our data, approximately half of the stage II patients are with pN0 stage and a NR of 0%, thus, the prognosis predictive value of NR is limited. But for the pN+ patients with insufficient LNRs, introducing an index of positive lymph node ratio (NR) could potentially overcome the bias of inaccurate N staging, which is in concordance with the previous reports (17,18). Nevertheless, the efforts by the surgeons and pathologists to retrieve more lymph nodes were important for avoiding false pN staging (19).…”

Section: Discussionsupporting

confidence: 66%

Retrieval of 30 Lymph Nodes Is Mandatory for Selected Stage II Gastric Cancer Patients

et al. 2021

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BackgroundAccording to the 8th edition AJCC staging manual, a least of 16 lymph nodes retrieval (LNRs) is the minimal requirement for lymph nodes (LNs) dissection of gastric cancer surgery. Previous studies have shown that increasing the number of LNRs (≥30) prolongs survival for selected patients. However, the necessity of retrieving 30 or more LN for stage II gastric cancer patients is still under debate.AimThis study aims to explore the impact of retrieving 30 or more lymph nodes on the survival of stage II cancer patients.MethodsA total of 1,177 patients diagnosed with stage II gastric cancer were enrolled in this study. The clinicopathological parameters and the impact of different LNRs (<30 or ≥30) and positive lymph node ratio (NR) on overall survival (OS) were retrospectively analyzed.ResultsThe mean number of LNRs was 34 ± 15. A total of 44% (518/1,177) of patients had an LNRs <30, while 56% (659/1,177) of patients had an LNRs ≥30. The 5-year survival rate was 81% for all patients, 76% for the LNRs <30 group, and 86% for LNRs ≥30 group, respectively (P = 0.003). The survival benefit of retrieving 30 lymph nodes was significant in certain subgroups: age >60 years/male/underwent total gastrectomy/stage IIB. For N+ patients, higher NR was significantly correlated with poor survival.ConclusionThe survival benefit of retrieving 30 LNs varies in different subgroups. An LNRs of 30 is mandatory for selected stage II gastric cancer patients.

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Section: Discussionsupporting

confidence: 66%

Retrieval of 30 Lymph Nodes Is Mandatory for Selected Stage II Gastric Cancer Patients

et al. 2021

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“…6,7 The 5-year relative survival rate is up to 70% for lesions in the early stages and 4% for lesions in the advanced stages. 6,8,9 Survival often refers to the likelihood, by which a patient will live 60 months after being diagnosed with cancer. This index is commonly used in medical science to evaluate the effects of surgical and treatment plans.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Afrash

Yazdani

Kazemi-Arpanahi³

2022

Clin Med Insights Oncol

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Background: Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. Methods: A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. Results: The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance. Conclusions: The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.

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Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study

Afrash

Mirbagheri

Mashoufi

et al. 2023

BMC Med Inform Decis Mak

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Background Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an individual’s prognosis. Numerous clinical and pathological features are generally used in predicting gastric cancer survival, and their influence on the survival of this cancer has not been fully elucidated. Moreover, the five-year survivability prognosis performances of feature selection methods with machine learning (ML) classifiers for gastric cancer have not been fully benchmarked. Therefore, we adopted several well-known feature selection methods and ML classifiers together to determine the best-paired feature selection-classifier for this purpose. Methods This was a retrospective study on a dataset of 974 patients diagnosed with gastric cancer in the Ayatollah Talleghani Hospital, Abadan, Iran. First, four feature selection algorithms, including Relief, Boruta, least absolute shrinkage and selection operator (LASSO), and minimum redundancy maximum relevance (mRMR) were used to select a set of relevant features that are very informative for five-year survival prediction in gastric cancer patients. Then, each feature set was fed to three classifiers: XG Boost (XGB), hist gradient boosting (HGB), and support vector machine (SVM) to develop predictive models. Finally, paired feature selection-classifier methods were evaluated to select the best-paired method using the area under the curve (AUC), accuracy, sensitivity, specificity, and f1-score metrics. Results The LASSO feature selection algorithm combined with the XG Boost classifier achieved an accuracy of 89.10%, a specificity of 87.15%, a sensitivity of 89.42%, an AUC of 89.37%, and an f1-score of 90.8%. Tumor stage, history of other cancers, lymphatic invasion, tumor site, type of treatment, body weight, histological type, and addiction were identified as the most significant factors affecting gastric cancer survival. Conclusions This study proved the worth of the paired feature selection-classifier to identify the best path that could augment the five-year survival prediction in gastric cancer patients. Our results were better than those of previous studies, both in terms of the time required to form the models and the performance measurement criteria of the algorithms. These findings may be very promising and can, therefore, inform clinical decision-making and shed light on future studies.

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Positive node‐ratio in curative‐intent treatment for gastric cancer is a strong independent prognostic factor for 5‐year overall survival

Cited by 7 publications

References 29 publications

Retrieval of 30 Lymph Nodes Is Mandatory for Selected Stage II Gastric Cancer Patients

Retrieval of 30 Lymph Nodes Is Mandatory for Selected Stage II Gastric Cancer Patients

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study

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