AUTHOR=Jin Lichao , Zhang Xiwei , Ni Song , Yan Dangui , Wang Minjie , Li Zhengjiang , Liu Shaoyan , An Changming 

TITLE=A nomogram to predict lateral lymph node metastases in lateral neck in patients with medullary thyroid cancer

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.902546

DOI=10.3389/fendo.2022.902546

ISSN=1664-2392

ABSTRACT=Background: Medullary thyroid cancer (MTC) can only be cured by surgery, but the management of lateral lymph nodes is controversial, especially for patients with cN0+cN1a. To address this challenge, we developed a multivariate logistic regression model to predict lateral lymph node metastases (LNM).
Methods: We retrospectively collected clinical data from 124 consecutive MTC patients who underwent initial surgery at our institution. The data of 82 patients (from 2010 to 2018) and 42 patients (from January 2019 to November 2019) were used as the training set for building the model and as the test set for validating the model, respectively.
Results: In the training group, the multivariate analyses indicated that male and MTC patients with higher preoperative basal calcitonin levels were more likely to have lateral LNM (P = 0.007 and 0.005, respectively). Multifocal lesions and suspected lateral LNM in preoperative ultrasound (US) were independent risk factors (P = 0.032 and 0.002, respectively). The identified risk factors were incorporated into a multivariate logistic regression model to generate the nomogram, which showed good discrimination (C-index = 0.963, 95% confidence interval [CI]: 0.9286–0.9972). Our model was validated with an excellent result in the test set and even superior to the training set (C-index = 0.964, 95% CI: 0.9121–1.000).
Conclusion: Higher preoperative basal calcitonin level, male sex, multifocal lesions and lateral lymph node involvement suspicion on US are risk factors for lateral LNM. Lateral LNM in patients with MTC could be objectively and accurately predicted using our model and nomogram.