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Nomogram for predicting skip metastasis in cN0 papillary thyroid cancer patients at increased risk of lymph node
Fang Li1, Fang-Jian Zhou2, Tong-Wei Zhu3
1Department of Ultrasound, The People's Hospital of Yuhuan, China.
Background:
Skip lymph node metastasis (SLNM) refers to lateral lymph node metastasis (LLNM) without involving central lymph node (CLN). Some microscopic nodal positivity may be difficult to detect before surgery due to atypical imaging characteristics. These patients are misdiagnosed as having clinically node-negative (cN0) papillary thyroid cancer (PTC) even after central lymph node dissection, leading to a high risk of developing LNM after surgery. Current prediction models have limited clinical utility, as they are only applicable to predict SLNM from clinically node-positive (cN+) PTC, not cN0 PTC, and this has little impact on treatment strategies.
Objectives:
This study aimed to establish a nomogram for preoperatively assessing the likelihood of SLNM in cN0 PTC patients with increased risk of LNM, thus optimizing their therapeutic options.
Material And Methods:
The records of 780 PTC patients undergoing thyroidectomy along with bilateral central lymph node dissection were retrospectively reviewed. The cN0 patients with postoperative LLNM (occult SLNM) and cN+ patients without central lymph node metastasis (CLNM) (typical SLNM) were included in the SLNM group (n = 82). The CLNM-negative cN0 patients without postoperative LLNM were assigned to the non-SLNM group (n = 698). The independent correlates of SLNM constituted the nomogram for determining the likelihood of SLNM in high-risk cN0 PTC patients.
Results:
The independent correlates of SLNM were age (hazard ratio (HR) = 1.016), tumor location (HR = 1.801), tumor size (HR = 1.528), and capsular invasion (HR = 2.941). They served as components in the development of the nomogram. This model was verified to present acceptable discrimination. It showed good calibration and a decent net benefit when the predicted probability was <60%.
Conclusions:
We developed a nomogram incorporating preoperative clinical data to predict the probability of SLNM development in high-risk cN0 PTC patients, which contributed to their optimized treatment options.
