| Literature DB >> 31412872 |
Xin Xie1, Jianhao Yin1, Zhangjian Zhou1, Chengxue Dang1, Hao Zhang2, Yong Zhang3.
Abstract
BACKGROUND: The risk of lymph node positivity in early-stage colon cancer is a parameter that impacts therapeutic recommendations. However, little is known about the effect of age on lymph node positivity in colon cancer with mucosal invasion. In this study, we aimed to quantify the effect of younger age on lymph node positivity in colon cancer with mucosal invasion.Entities:
Mesh:
Year: 2019 PMID: 31412872 PMCID: PMC6693219 DOI: 10.1186/s12885-019-5995-4
Source DB: PubMed Journal: BMC Cancer ISSN: 1471-2407 Impact factor: 4.430
The demographic and clinicopathological characteristics of patients
| Lymph nodes metastasis | |||||
|---|---|---|---|---|---|
| Negative | Positive | ||||
| Counts | Percentage | Counts | Percentage | ||
| Gender | |||||
| Male | 17,215 | 84.8% | 3077 | 15.2% | |
| Female | 17,917 | 84.5% | 3281 | 15.5% | 0.37 |
| Age (year) | |||||
| ≤ 40 | 414 | 69.9% | 178 | 30.1% | |
| 41–50 | 1913 | 77.9% | 543 | 22.1% | |
| 51–60 | 5470 | 81.1% | 1273 | 18.9% | |
| 61–70 | 8695 | 84.3% | 1616 | 15.7% | |
| 71–80 | 10,753 | 86.5% | 1675 | 13.5% | |
| ≥ 81 | 7887 | 88.0% | 1073 | 12.0% | < 0.01a |
| Race | |||||
| White | 28,841 | 85.4% | 4950 | 14.6% | |
| Black | 3747 | 81.8% | 831 | 18.2% | |
| Others | 2544 | 81.5% | 577 | 18.5% | < 0.01 |
| Size (mm) | |||||
| ≤ 10 | 5426 | 89.0% | 668 | 11.0% | |
| 11–20 | 8180 | 85.5% | 1390 | 14.5% | |
| 21–30 | 8320 | 84.1% | 1570 | 15.9% | |
| 31–40 | 5913 | 83.2% | 1190 | 16.8% | |
| 41–50 | 3611 | 83.0% | 742 | 17.0% | |
| ≥ 51 | 3682 | 82.2% | 798 | 17.8% | < 0.01 |
| Mucinous | |||||
| Non-mucin | 32,766 | 84.8% | 5853 | 15.2% | |
| Mucin | 2366 | 82.4% | 505 | 17.6% | 0.01 |
| Grade | |||||
| Well | 5782 | 90.5% | 604 | 9.5% | |
| Moderate | 26,388 | 85.0% | 4667 | 15.0% | |
| Poor | 2677 | 73.1% | 985 | 26.9% | |
| Undifferentiated | 285 | 73.6% | 102 | 26.4% | < 0.01 |
| Depth of invasion | |||||
| T1 | 13,694 | 88.7% | 1743 | 11.3% | |
| T2 | 21,438 | 82.3% | 4615 | 17.7% | < 0.01 |
aThere were significant differences of the adjacent age groups
Fig. 1The distribution of lymph nodes positivity. a. Node positivity and age of diagnosis by depth of invasion. b. Node positivity and age of diagnosis by depth of invasion and number of lymph nodes examined. LNE = lymph nodes examined
The risk factors of predicted lymph node metastasis
| Lymph nodes metastasis | |||
|---|---|---|---|
| Univariate | Multivariate | ||
| P value | P value | Hazard Ratio | |
| Gender | 0.678 | ||
| Age (year) | < 0.001 | < 0.001 | 0.976–0.982 |
| Race | < 0.001 | < 0.001 | 1.053–1.174 |
| Size (mm) | < 0.001 | 0.053 | 1–1.003 |
| Mucinous | 0.016 | 0.085 | 0.985–1.267 |
| Grade | < 0.001 | < 0.001 | 1.612–1.827 |
| Depth of invasion | < 0.001 | < 0.001 | 1.045–1.061 |
Fig. 2The predictive model of lymph nodes metastasis. a. Nomogram predicted lymph nodes metastasis risk using four available clinical characteristics. b. The calibration curve of the nomogram predicted system of the training set. c. The calibration curve of the validation set. The x-axis is the predicted lymph nodes metastatic risk calculated by the nomogram, and the y-axis is the actual metastatic status. The solid line represents the ideal reference line where predicted risk corresponds with the actual appearance, and the dotted lines represent a 10% margin of error. The actual status corresponded closely with the predicted lymph nodes metastasis and was always within the 10% margin of error
Fig. 3The decision curve analysis for the nomogram model. a. Decision curve analysis for the nomogram predictive model. The y-axis represents the net benefit. The red line represents the nomogram model. The grey line represents the hypothesis that all patients had lymph node metastases. The black line represents the hypothesis that no patients had lymph node metastases. The x-axis represents the threshold probability. The threshold probability is where the expected benefit of treatment is equal to the expected benefit of avoiding treatment. For example, if the possibility of lymph node metastasis involvement of a patient is over the threshold probability, then a treatment strategy for lymph node metastasis should be adopted. The decision curves in the validation set showed that if the threshold probability is between 0 and 0.4, then using the nomogram to predict lymph node metastases adds more benefit than treating either all or no patients. b. The cost-benefit ratio of the nomogram predictive model. The red curve represents the number of people classified as positive (high risk) at each threshold probability; the blue curve is the number of true positives for each threshold probability