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. 2023 Jul;12(13):14120-14129.
doi: 10.1002/cam4.6076. Epub 2023 May 15.

A preoperative nomogram model for the prediction of lymph node metastasis in buccal mucosa cancer

Qian Chen  1   2 , Rui Wei  2 , Shan Li  1   2
Affiliations

A preoperative nomogram model for the prediction of lymph node metastasis in buccal mucosa cancer

Qian Chen et al. Cancer Med. 2023 Jul.

Abstract

Objectives: We sought to construct a nomogram model predicting lymph node metastasis (LNM) in patients with squamous cell carcinoma of the buccal mucosa based on preoperative clinical characteristics.

Methods: Patients who underwent radical resection of a primary tumor in the buccal mucosa with neck dissection were enrolled. Clinical characteristics independently associated with LNM in multivariate analyses were adopted to build the model. Patients at low risk of LNM were defined by a predicted probability of LNM of less than 5%.

Results: Patients who underwent surgery in an earlier period (January 2015-November 2019) were defined as the model development cohort (n = 325), and those who underwent surgery later (November 2019-March 2021) were defined as the validation cohort (n = 140). Age, tumor differentiation, tumor thickness, and clinical N stage assessed by computed tomography/magnetic resonance imaging (cN) were independent predictors of LNM. The nomogram model based on these four predictors showed good discrimination accuracy in both the model development and validation cohorts, with areas under the receiver-operating characteristic curve (AUC) of 0.814 and 0.828, respectively. LNM prediction by the nomogram model was superior to cN in AUC comparisons (0.815 vs. 0.753) and decision curve analysis of the whole cohort. Seventy-one patients were defined as having a low risk of LNM, among whom the actual metastasis rate was only 1.4%.

Conclusions: A robust nomogram model for preoperative LNM prediction is built.

Keywords: buccal mucosa carcinoma; lymph node metastasis; nomogram.

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Figures

FIGURE 1
FIGURE 1
Flowchart of patient enrollment.
FIGURE 2
FIGURE 2
The nomogram predicting the risk of LNM in patients with squamous cell carcinoma of the buccal mucosa. The total points of each patient were calculated by drawing an upward vertical line to “Points,” and the corresponding risk of LNM was calculated by drawing a downward vertical line from “Total Points.” cN, clinical N stage; LNM, lymph node metastasis.
FIGURE 3
FIGURE 3
Internal and external validation of the nomogram model. (A) Calibration plot of the nomogram model for the model development cohort. (B) Calibration plot of the nomogram model for the validation cohort. (C) The ROC curve of the model development cohort. The AUC was 0.814 (95% CI, 0.767–0.861; Hosmer–Lemeshow test, p = 0.538). (D) The ROC curve of the validation cohort. The AUC was of 0.828 (95% CI, 0.759–0.898; Hosmer–Lemeshow test, p = 0.838).
FIGURE 4
FIGURE 4
Decision curve analysis for the cN approach and the nomogram model. The x‐axis and y‐axis represent the threshold probability and net benefit, respectively. “All” and “None” refer to the assumptions that all patients and no patients have LNM, respectively.
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