A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer

Yang, Tao; Martínez‐Useros, Javier; Liu, JingWen; Alarcón, Isaías; Li, Chao; Li, WeiYao; Xiao, Yuanxun; Xiang, Ji; Zhao, Yandong; Wang, Lei; Morales‐Conde, Salvador; Yang, Zhaohui

doi:10.3389/fonc.2022.1023110

Cited by 4 publications

(6 citation statements)

References 70 publications

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“…The importance of ML models are given in a few GC studies. These approaches were evaluated to predict the lymph node metastasis in GC patients [11][12][13][14][15][16][17] . Arai et al (2022) suggested ML method to predict the risk of GC in patients by information on gastric atrophy and intestinal metaplasia at the initial EGD 11 .…”

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confidence: 99%

“…Also, an advanced model was expanded to comprise radiomics clinical and features attributes to boost the performance of the model. Some studies also have advised ML methods to predict metastasis in GC patients [13][14][15][16][17] . Yang et al (2022) obtained some factors that depend on lymph node metastasis in GC patients.…”

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confidence: 99%

“…Some studies also have advised ML methods to predict metastasis in GC patients [13][14][15][16][17] . Yang et al (2022) obtained some factors that depend on lymph node metastasis in GC patients. Then, they constructed 5 algorithms of ML in a retrospective dataset 15 .…”

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confidence: 99%

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Predicting metastasis in gastric cancer patients: machine learning-based approaches

Talebi

Celis‐Morales

Borumandnia

et al. 2023

Sci Rep

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Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.

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Predicting metastasis in gastric cancer patients: machine learning-based approaches

Talebi

Celis‐Morales

Borumandnia

et al. 2023

Sci Rep

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“…Several researchers have developed ML models that predict the risk of LNM in patients with EGC [20][21][22][23][24]; however, these studies include many lesions satisfying the endoscopic curability criteria that have no risk of LNM. In contrast, we used only EGC data categorized as endoscopic curability C-2 (i.e., lesions at a high risk of LNM).…”

Section: Discussionmentioning

confidence: 99%

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

Kato,

Hayashi,

Uema

et al. 2024

Gastric Cancer

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Background We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.

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“…Machine learning (ML), a fundamental branch of AI, excels in deciphering complex nonlinear associations among multidimensional features [ 14 ]. It has been extensively applied in the realm of healthcare, spanning areas such as medical diagnostics and the prediction of disease risks [ 15 , 16 ]. Numerous studies employ ML models to predict mortality risk in patients with conditions such as heart failure, surgical interventions, and sepsis [ 17 – 19 ].…”

Section: Introductionmentioning

confidence: 99%

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Xu,

Li,

Zhang

et al. 2024

Eur J Med Res

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Background Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. Methods We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. Results The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. Conclusions Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

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A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer

Cited by 4 publications

References 70 publications

Predicting metastasis in gastric cancer patients: machine learning-based approaches

Predicting metastasis in gastric cancer patients: machine learning-based approaches

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

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