Establishment and Verification of a Nomogram for Predicting the Risk of Lymph Node Metastasis in Early Gastric Cancer

Wang, Zhengbing; Liu, Jiangtao; Zhu, Qunzhi; Wei, Lifu; Luo, Yi; Xu, Yaozong; Li, Xuan

doi:10.17235/reed.2020.7102/2020

Cited by 7 publications

(10 citation statements)

References 23 publications

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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%

“…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%

“…There were different numbers for training and test models because there were five studies which only reported the training results (35,(50)(51)(52)(53). The overall c-index for ML in training group was 0.837 [(95%CI (0.814, 0.859)] (Table 3; eFigure 1).…”

Section: C-indexmentioning

confidence: 99%

“…Of the 41 included studies, 21 were early-gastric GC (EGC) (T1) studies (32,34,35,37,39,42,46,(48)(49)(50)(51)(52)(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: Subgroup Analysis For Early-gastric Cancer and Advanced Gast...mentioning

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: C-indexmentioning

confidence: 99%

Section: Subgroup Analysis For Early-gastric Cancer and Advanced Gast...mentioning

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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“…Although there are many published relevant studies on the prediction of LNM in EGC, the exploration for the optimal model has never ended (12,(25)(26)(27)(28)(29)(30). Of particular note, Chen et al put forward a model by incorporating the collagen features of the tumor tissues extracted from multiphoton imaging, which showed a more robust ability to estimate the risk of LNM than traditional models (10).…”

Section: Figurementioning

confidence: 99%

Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissection

Zhang

Yang²,

Wei³

et al. 2022

Front. Oncol.

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BackgroundEndoscopic submucosal dissection (ESD) has been accepted as the standard treatment for the appropriate indication of early gastric cancer (EGC). Determining the risk of lymph node metastasis (LNM) is critical for the following treatment selection after ESD. This study aimed to develop a predictive model to quantify the probability of LNM in EGC to help minimize the invasive procedures.MethodsA total of 952 patients with EGC who underwent radical gastrectomy were retrospectively reviewed. LASSO regression was used to help screen the potential risk factors. Multivariate logistic regression was used to establish a predictive nomogram, which was subjected to discrimination and calibration evaluation, bootstrapping internal validation, and decision curve analysis.ResultsResults of multivariate analyses revealed that gender, fecal occult blood test, CEA, CA19-9, histologic differentiation grade, lymphovascular invasion, depth of infiltration, and Ki67 labeling index were independent prognostic factors for LNM. The nomogram had good discriminatory performance, with a concordance index of 0.816 (95% CI 0.781–0.853). The validation dataset yielded a corrected concordance index of 0.805 (95% CI 0.770–0.842). High agreements between ideal curves and calibration curves were observed.ConclusionsThe nomogram is clinically useful for predicting LNM after ESD in EGC, which is beneficial to identifying patients who are at low risk for LNM and would benefit from avoiding an unnecessary gastrectomy.

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Development and validation of nomogram for predicting lymph node metastasis in early gastric cancer

Cao

et al. 2023

Preprint

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Purpose: To establish and verify a prediction model for lymph node metastasis (LNM) in early gastric cancer (EGC) and provide a reference for the selection of appropriate treatment for EGC patients. Methods: The clinicopathological data of 1584 patients with EGC admitted to Zhejiang Cancer Hospital from January 2010 to April 2019 were retrospectively analysed. Univariate and multivariate logistic regression analyses were used to explore the correlation between various clinicopathological factors and LNM in patients with EGC. Univariate K‒M and multivariate Cox regression analyses were used to explore the influence of multiple clinical factors on the prognosis of patients with EGC. The discrimination and calibration of the established prediction model, which is presented in the form of a nomogram, were also evaluated. Results: The incidence of LNM was 19.6%. Multivariate logistic regression analysis showed that tumour size, location, differentiation degree and pathological type were independent risk factors for LNM in EGC. Tumour pathological type and LNM were independent factors affecting the prognosis of patients with EGC. The area under the curve in the training and verification group was 0.750 (95% CI: 0.701 ~ 0.789) and 0.763 (95% CI: 0.687 ~ 0.838), respectively. The calibration curve showed good agreement between the predicted and actual probability, and decision curve analysis indicated strong clinical practicability. Conclusion: Tumour diameter ≥ 2 cm, poor differentiation degree, middle-lower tumour location and signet ring cell carcinoma were identified as independent risk factors for LNM in EGC. Among them, tumour pathological type and LNM were independent risk factors for prognosis in EGC. This clinical model for predicting LNM in EGC was used to construct a nomogram, which has high diagnostic value and can serve as a reference in clinical treatment selection.

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Establishment and Verification of a Nomogram for Predicting the Risk of Lymph Node Metastasis in Early Gastric Cancer

Cited by 7 publications

References 23 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

Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissection

Development and validation of nomogram for predicting lymph node metastasis in early gastric cancer

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