Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer

Tian, Huakai; Ning, Zhikun; Zong, Zhen; Liu, Jiang; Hu, Cegui; Ying, Hou-Qun; Li, Hui

doi:10.3389/fmed.2021.759013

Cited by 19 publications

(17 citation statements)

References 33 publications

(37 reference statements)

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“…Larger tumor typically indicates a higher risk of LNM ( 86 – 88 ), which might be attributed to easiness of invasion for larger tumor to the surrounding tissues. Depth of tumor invasion was found to be a strong predictor in 32 models ( 32 , 34 – 37 , 39 , 41 , 42 , 46 , 49 , 50 , 52 , 54 – 56 , 62 , 69 – 71 ), which is in consistence with substantial evidence supporting its use as a predictor of LNM ( 36 , 63 , 89 – 91 ). There were 20 models considered histology differentiation as a vital factor for predicting LNM ( 34 – 37 , 41 , 42 , 48 – 55 , 61 – 63 ).…”

Section: Discussionmentioning

confidence: 55%

“…There were 35 studies (85.4%) that were conducted in China ( 32 , 33 , 35 – 43 , 45 – 49 , 51 – 54 , 56 – 63 , 65 – 69 , 71 , 72 ), 5 (12.2%) in Korea ( 34 , 44 , 50 , 55 , 64 ) and 1 (2.4%) in Germany ( 70 ), with the publication date ranged from 2004 to 2022. The number of studies using ML for LNM prediction has gradually increased since 2018 ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…Eight studies lacked external validation in model development ( 35 , 50 , 51 , 53 , 59 , 63 , 65 , 70 ). Concern regarding ‘overall applicability’ was rated as low in 15 studies (36.59%) ( 32 , 34 , 37 , 38 , 40 , 42 , 44 , 54 – 56 , 60 , 62 , 66 , 67 , 69 ), high in 16 studies (39.02%) ( 33 , 35 , 41 , 43 , 47 , 50 , 53 , 58 , 59 , 61 , 63 , 65 , 68 , 70 , 71 ) and unclear in the remaining 10 (24.39%) ( 36 , 39 , 45 , 46 , 48 , 49 , 52 , 57 , 64 , 72 ). Risk of bias and applicability assessment were shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Of the 41 included studies, 21 were early-gastric GC (EGC) (T1) studies ( 32 , 34 , 35 , 37 , 39 , 42 , 46 , 48 – 53 , 55 , 56 , 60 , 64 , 65 , 69 , 71 , 72 ) and 3 were advanced GC (T2-4) studies ( 43 , 45 , 67 ). In EGC, there was a pooled c-index of 0.832 [95%CI (0.804, 0.860)] ( Table 5 ; eFigure 13 ) and 0.795 [95%CI (0.755, 0.838)] ( eFigure 14 ) for the training and test groups, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…There were 20 models considered histology differentiation as a vital factor for predicting LNM ( 34 – 37 , 41 , 42 , 48 – 55 , 61 – 63 ). Deeply infiltrated and poorly differentiated tumors might have sufficient nutritional support to facilitate its invasion to tissues, capillaries and lymphatic vessels, and thus to have the potential to grow and metastasize faster ( 69 ).…”

Section: Discussionmentioning

confidence: 99%

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Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Xie

Xiong

et al. 2022

Front. Oncol.

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ObjectiveTo evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models.MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool.ResultsA total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction.ConclusionML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752

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Section: Discussionmentioning

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Xie

Xiong

et al. 2022

Front. Oncol.

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A discrimination model by machine learning to avoid gastrectomy for early gastric cancer

Hayashi

Takasawa

Yoshikawa

et al. 2023

Annals of Gastroent Surgery

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AimGastrectomy is recommended for patients with early gastric cancer (EGC) because the possibility of lymph node metastasis (LNM) cannot be completely denied. The aim of this study was to develop a discrimination model to select patients who do not require surgery using machine learning.MethodsData from 382 patients who received gastrectomy for gastric cancer and who were diagnosed with pT1b were extracted for developing a discrimination model. For the validation of this discrimination model, data from 140 consecutive patients who underwent endoscopic resection followed by gastrectomy, with a diagnosis of pT1b EGC, were extracted. We applied XGBoost to develop a discrimination model for clinical and pathological variables. The performance of the discrimination model was evaluated based on the number of cases classified as true negatives for LNM, with no false negatives for LNM allowed.ResultsLymph node metastasis was observed in 95 patients (25%) in the development cohort and 11 patients (8%) in the validation cohort. The discrimination model was developed to identify 27 (7%) patients with no indications for additional surgery due to the prediction of an LNM‐negative status with no false negatives. In the validation cohort, 13 (9%) patients were identified as having no indications for additional surgery and no patients with LNM were classified into this group.ConclusionThe discrimination model using XGBoost algorithms could select patients with no risk of LNM from patients with pT1b EGC. This discrimination model was considered promising for clinical decision‐making in relation to patients with EGC.

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Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer

Wang

Zhang

et al. 2023

J Cancer Res Clin Oncol

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Purpose This study aims to establish the best prediction model of lymph node metastasis (LNM) in patients with intermediate- and high-risk prostate cancer (PCa) through machine learning (ML), and provide the guideline of accurate clinical diagnosis and precise treatment for clinicals. Methods A total of 24,470 patients with intermediate- and high-risk PCa were included in this study. Multivariate logistic regression model was used to screen the independent risk factors of LNM. At the same time, six algorithms, namely random forest (RF), naive Bayesian classifier (NBC), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR) and decision tree (DT) are used to establish risk prediction models. Based on the best prediction performance of ML algorithm, a prediction model is established, and the performance of the model is evaluated from three aspects: area under curve (AUC), sensitivity and specificity. Results In multivariate logistic regression analysis, T stage, PSA, Gleason score and bone metastasis were independent predictors of LNM in patients with intermediate- and high-risk PCa. By comprehensively comparing the prediction model performance of training set and test set, GBM model has the best prediction performance (F1 score = 0.838, AUROC = 0.804). Finally, we developed a preliminary calculator model that can quickly and accurately calculate the regional LNM in patients with intermediate- and high-risk PCa. Conclusion T stage, PSA, Gleason and bone metastasis were independent risk factors for predicting LNM in patients with intermediate- and high-risk PCa. The prediction model established in this study performs well; however, the GBM model is the best one.

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Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer

Cited by 19 publications

References 33 publications

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

A discrimination model by machine learning to avoid gastrectomy for early gastric cancer

Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer

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