| Literature DB >> 35256856 |
Yizhou Huang1, Yu Min2, Gangyi Yang1, Hanghang Wang3, Guobing Yin2, Lili Zhang1.
Abstract
Purpose: Medullary thyroid carcinoma (MTC) is a rare but highly invasive malignancy, especially in terms of cervical lymph node metastasis. However, the role of prophylactic lateral lymph node dissection (LLND) is still controversial. We hereby aim to explore the risk factors of lateral lymph node metastasis (LLNM) in patients with MTC to guide clinical practice. Patients andEntities:
Keywords: SEER; lateral lymph node metastasis; medullary thyroid carcinoma; predicting model; risk factors
Year: 2022 PMID: 35256856 PMCID: PMC8898042 DOI: 10.2147/IJGM.S353497
Source DB: PubMed Journal: Int J Gen Med ISSN: 1178-7074
Clinicopathological Characteristics of Patients with Medullary Thyroid Carcinoma
| Characteristics | Initial Cohort (n=924) | Training Cohort (n=450) | Internal Cohort (n=474) | External Cohort (n=22) | ||||
|---|---|---|---|---|---|---|---|---|
| No. | % | No. | % | No. | % | No. | % | |
| 18–54 | 422 | 45.7 | 211 | 46.9 | 211 | 44.5 | 13 | 59.1 |
| ≥55 | 502 | 54.3 | 239 | 53.1 | 263 | 55.5 | 9 | 40.9 |
| Female | 578 | 62.6 | 280 | 62.2 | 298 | 62.9 | 11 | 50 |
| Male | 346 | 37.4 | 170 | 37.8 | 176 | 37.1 | 11 | 50 |
| White | 789 | 85.4 | 398 | 88.4 | 391 | 82.5 | 0 | 0 |
| Black | 66 | 7.1 | 28 | 6.2 | 38 | 8.0 | 0 | 0 |
| Other | 69 | 7.5 | 24 | 5.3 | 45 | 9.5 | 22 | 100 |
| Unifocal | 645 | 69.8 | 331 | 73.6 | 314 | 66.2 | 15 | 68.2 |
| Multifocal | 279 | 30.2 | 119 | 26.4 | 160 | 33.8 | 7 | 31.8 |
| Absent | 796 | 86.1 | 389 | 86.4 | 407 | 85.9 | 15 | 68.2 |
| Present | 128 | 13.9 | 61 | 13.6 | 67 | 14.1 | 7 | 31.8 |
| ≤10 | 291 | 31.5 | 145 | 32.2 | 146 | 30.8 | 7 | 31.8 |
| >10 and ≤20 | 300 | 32.5 | 147 | 32.7 | 153 | 32.3 | 5 | 22.7 |
| >20 and ≤40 | 333 | 36.0 | 158 | 35.1 | 175 | 36.9 | 10 | 45.5 |
Note: Other defined as the Asian/Pacific Islander and American Indian/Alaska Native.
Univariate and Multivariate Analyses of Risk Factors Related to LLNM
| Characteristics | Univariate Analysis | Multivariate Analysis | ||
|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | |||
| <55 | Reference | Reference | ||
| ≥55 | 0.809 (0.517–1.267) | 0.355 | 0.814 (0.474–1.398) | 0.456 |
| Female | Reference | Reference | ||
| Male | 2.940 (1.857–4.656) | 2.414 (1.425–4.090) | ||
| White | Reference | Reference | ||
| Black | 0.733 (0.271–1.984) | 0.541 | 1.041 (0.344–3.151) | 0.943 |
| Other | 0.307 (0.071–1.329) | 0.114 | 0.243 (0.048–1.238) | 0.089 |
| Unifocal | Reference | Reference | ||
| Multifocal | 2.679 (1.669–4.299) | 2.234 (1.246–4.004) | ||
| Absent | Reference | Reference | ||
| Present | 11.940 (6.527–21.842) | 8.969 (4.660–17.265) | ||
| ≤10 | Reference | Reference | ||
| >10 and ≤20 | 2.924 (1.498–5.710) | 2.878 (1.367–6.059) | ||
| >20 and ≤40 | 4.206 (2.205–8.026) | 3.684 (1.789–7.588) | ||
Note: Bold values indicate statistical significance (p < 0.05).
Abbreviations: LLNM, lateral lymph node metastasis; HR, hazard ratio; CI, confidence interval.
Figure 1Nomogram for predicting lateral lymph node metastasis in patients with MTC. Patients were scored through each item, and total points are calculated to obtain the probability of LLNM.
Figure 2(A) Calibration curve of the nomogram for the verification of training set. The x-axis represents the predicted LLNM. The y-axis represents the actual LLNM. The dotted line stands for a perfect prediction using an ideal model. The solid line was drawn to represent the performance of the nomogram, of which the closer fit to the dotted line represents the better prediction of the nomogram. (B) Decision curve analysis for LLNM in MTC patients. The black line (None) represents the assumption of net benefit that no patient has LLNM. The gray line (All) shows the assumption of net benefit that all patients have LLNM. The red line (Score) represents the assumption of net benefit of nomogram for LLNM considering clinical risk factors (gender, multifocal, extrathyroidal invasion and tumor size).
Figure 3The receiver operating characteristics (ROC) curve and area under the ROC curve (AUC). (A) AUC for the training group; (B) AUC for the internal validation group; (C) AUC for the external validation group.