A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer

Zhao, Feng; Wang, Ping; Yu, Chengqun; Song, Xuefei; Wang, Hui; Fang, Jun; Zhu, Chenfang; Li, Yousheng

doi:10.3389/fonc.2023.1034047

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…Finally, 14 retrospective studies and two prospective study from 2012 to 2023 were included in our review. [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Most studies were performed in Asian countries including 11 from China, two from South Korea and one from Saudi Arabia. Basic information was demonstrated in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Gene mutations as predictors of central lymph mode metastasis in cN0 PTC: A meta‐analysis

Ji,

Shi

2023

Clinical Genetics

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Gene mutations could predict the tumor progression and prognosis, which are us to predict CLNM in patients with cN0 PTC, however, these results are not consistent. This meta‐analysis tried to identify gene mutations which could predict CLNM in patients with cN0 PTC. A systematic search was performed for identifying relevant literature published prior to July 2023 in three search engines: PubMed, EMBASE and Web of Science. Studies that investigated the gene mutations for CLNM in patients with cN0 PTC were included in our meta‐analysis. Sixteen studies, including 6095 cN0 PTC with BRAF mutations were include in our meta‐analysis. The prevalence of CLNM in cN0 PTC ranged from 13.7% to 50.6%. The pooled analysis demonstrated that BRAFV600E mutation is significantly associated with CLNM (OR = 2.01, 95% CI: 1.55–2.60, p < 0.001) in PTC and PTMC (OR = 1.70, 95% CI: 0.51–1.81, p < 0.001). Whereas, cN0 PTC with TERT (OR = 1.94, 95% CI: 0.51–7.36, p = 0.33) and KRAS (OR = 0.57, 95% CI: 0.51–1.81, p = 0.34) mutations might not contribute to predict CLNM. Our analysis identified that BRAF mutation was a predictive factor for cN0 PTC, as well as for cN0 PTMC, which could be useful for clinician to accurately choose prophylactic CLND and better manage cN0 PTC.

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Section: Resultsmentioning

confidence: 99%

Gene mutations as predictors of central lymph mode metastasis in cN0 PTC: A meta‐analysis

Ji,

Shi

2023

Clinical Genetics

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“…Molecular alterations associated with BRAF mutations influence PTMC initiation, progression, and metastasis. Some studies reported a high incidence of CLNM in patients with PTMC and BRAF mutations [ 21 , 25 ]. In the present study, BRAF status was also significantly associated with the risk of CLNM.…”

Section: Discussionmentioning

confidence: 99%

Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma

Liu,

Yu,

Wei

et al. 2024

Thyroid Res

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Background Whether prophylactic central lymph node dissection is necessary for patients with clinically node-negative (cN0) papillary thyroid microcarcinoma (PTMC) remains controversial. Herein, we aimed to establish an ultrasound (US) radiomics (Rad) score for assessing the probability of central lymph node metastasis (CLNM) in such patients. Methods 480 patients (327 in the training cohort, 153 in the validation cohort) who underwent thyroid surgery for cN0 PTMC at two institutions between January 2018 and December 2020 were included. Radiomics features were extracted from the US images. Least absolute shrinkage and selection operator logistic regression were utilized to generate a Rad score. A nomogram consisting of the Rad score and clinical factors was then constructed for the training cohort. Both cohorts assessed model performance using discrimination, calibration, and clinical usefulness. Results Based on the six most valuable radiomics features, the Rad score was calculated for each patient. A multivariate analysis revealed that a higher Rad score (P < 0.001), younger age (P = 0.006), and presence of capsule invasion (P = 0.030) were independently associated with CLNM. A nomogram integrating these three factors demonstrated good calibration and promising clinical utility in the training and validation cohorts. The nomogram yielded areas under the curve of 0.795 (95% confidence interval [CI], 0.745–0.846) and 0.774 (95% CI, 0.696–0.852) in the training and validation cohorts, respectively. Conclusions The radiomics nomogram may be a clinically useful tool for the individual prediction of CLNM in patients with cN0 PTMC.

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“…Consistent with several previous studies [30][31][32], our study found that youth, indistinct tumor boundaries, side of position and microcalci cation are also signi cant ultrasound features of total PLNM. Our ROC analysis in this study set the age diagnostic threshold at 45 years, where PTC patients ≤ 45 years old are more likely to present with CLNM [33,34]. Microcalci cations are mainly caused by calcium salt deposits resulting from vascular and brous proliferation, representing an accelerated growth of tumor cells.…”

Section: Discussionmentioning

confidence: 99%

Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer

Chun,

Wang,

et al. 2024

Preprint

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Prophylactic dissection of the paratracheal lymph nodes in clinical lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial, as it is difficult to accurately assess the status of the paratracheal lymph nodes preoperatively. This study aimed to construct and validate an interpretable predictive model for paratracheal lymph node metastasis (PLNM) in cN0 PTC using machine learning (ML) methods. We retrospectively selected 3,212 PTC patients treated at the First Affiliated Hospital of Chongqing Medical University from 2016 to 2020. They were randomly divided into the training and test datasets with a 7:3 ratio. The 533 PTC patients treated at the Guangyuan Central Hospital from 2019 to 2022 were used as an external test set. Nine ML models, including XGBoost, were developed. The predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. SHapley Additive exPlanations (SHAP) were used to interpret the top 10 predictive features, and a web-based calculator was created. The XGBoost model achieved AUC values of 0.935, 0.857, and 0.775 in the training, validation, and test sets, respectively, significantly outperforming the traditional nomogram model with AUCs of 0.85, 0.844, and 0.769, respectively. SHAP-based visualizations identified the top ten predictive features: prelaryngeal and pretracheal LNMR, tumor size, pretracheal LNMR, prelaryngeal and pretracheal LNM, age, tumor border, pretracheal LNM, pretracheal NLNM, side of position, calcification. These features were used to develop a web-based calculator. ML is a reliable tool for predicting PLNM in cN0 PTC patients. The SHAP method provides valuable insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for paratracheal lymph node dissection.

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A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer

Cited by 9 publications

References 38 publications

Gene mutations as predictors of central lymph mode metastasis in cN0 PTC: A meta‐analysis

Gene mutations as predictors of central lymph mode metastasis in cN0 PTC: A meta‐analysis

Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma

Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer

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