Incidence, risk factors, and a predictive model for lymph node metastasis of submucosal (T1) colon cancer: A population‐based study

Abstract Background: Surgery combined with chemo-radiotherapy is a recognized model for the treatment of gastric and colon cancers. Lymph node metastasis determines the patient's surgical or comprehensive treatment plan. This analytical study aims to compare preoperative prediction scores to better predict lymph node metastasis in gastric and colon cancer patients.Methods: This study comprised 768 patients, which included 312 patients with gastric cancer and 462 with colon cancer. Preoperative clinical tumor characteristics, serum markers, and immune indices were evaluated using single-factor analysis. Logistic analysis was designed to recognize independent predictors of lymph node metastasis in these patients. The independent risk factors were integrated into preoperative prediction scores, which were accurately assessed using receiver operating characteristic (ROC) curves.Results: Results showed that serum markers (CA125, hemoglobin, albumin), immune indices (S100, CD31, d2–40), and tumor characteristics (pathological type, size) were independent risk factors for lymph node metastasis in patients with gastric and colon cancer. The preoperative prediction scores reliably predicted lymph node metastasis in gastric and colon cancer patients with a higher area under the ROC curve (0.901). The area was 0.923 and 0.870 in gastric cancer and colon cancer, respectively. Based on the ROC curve, the ideal cutoff value of preoperative prediction scores to predict lymph node metastasis was established to be 287. Conclusion: The preoperative prediction scores is a useful indicator that can be applied to predict lymph node metastasis in gastric and colon cancer patients.

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Risk factors for lymph node metastasis in early colon cancer

International Journal of Colorectal Disease ◽

10.1007/s00384-020-03618-7 ◽

2020 ◽

Vol 35 (8) ◽

pp. 1607-1613

Author(s):

You Jin Lee ◽

Jung Wook Huh ◽

Jung Kyong Shin ◽

Yoon Ah Park ◽

Yong Beom Cho ◽

...

Keyword(s):

Risk Factors ◽

Colon Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Node Metastasis ◽

Early Colon Cancer

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Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

Journal of Immunology Research ◽

10.1155/2019/6263249 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9

Author(s):

Chengyan Zhang ◽

Guanchao Pang ◽

Chengxi Ma ◽

Jingni Wu ◽

Pingli Wang ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Logistic Regression ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Predictive Model ◽

Smoking History ◽

Lymph Node Status ◽

Node Metastasis ◽

Node Status

Background. Lymph node status of clinical T1 (diameter≤3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. Methods. We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models. Results. Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p=0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models. Conclusion. In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

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