BRAF突变甲状腺乳头状癌淋巴结转移预测模型的构建及临床验证OA

Background and purpose:Papillary thyroid carcinoma(PTC)is the most common malignancy of the endocrine system,with its global incidence continuing to rise.The BRAF V600E mutation,as the most common genetic mutation in PTC,is closely associated with aggressive clinicopathological features,including lymph node metastasis(LNM),extrathyroidal extension and poor prognosis.LNM is an important risk factor for recurrence and poor prognosis in PTC patients.Accurate preoperative prediction of LNM risk is of critical guiding significance for formulating individualized surgical plans(such as the extent of prophylactic central or lateral neck lymph node dissection).However,existing prediction models are mostly based on traditional clinicopathological indicators,and few studies have incorporated patients'mental health indicators into the evaluation system.This study aimed to construct a preoperative LNM prediction model based on the clinicopathological characteristics and mental health indicators of BRAF-mutant PTC patients,and to evaluate its predictive efficacy through internal validation,thereby providing an objective basis for clinical preoperative decision-making.Methods:BRAF-mutant PTC patients who underwent surgical treatment at the Department of Thyroid and Neck Oncology,Tianjin Medical University Cancer Institute&Hospital from October to December 2023,were retrospectively collected and randomly divided into the training set and the validation set at a ratio of 85%:15%.Inclusion criteria:① Postoperative pathological examination confirmed PTC;② Preoperative or postoperative pathological detection of BRAF V600E mutation positivity;③ Undergoing total or near-total thyroidectomy combined with central neck lymph node dissection,with some patients additionally undergoing lateral neck lymph node dissection;④ Complete laboratory examination and clinicopathological data.Exclusion criteria:① History of thyroid surgery or neck radiotherapy;② Concomitant other malignancies;③ Distant metastasis;④ Pathological types of follicular thyroid carcinoma,medullary thyroid carcinoma,or anaplastic thyroid carcinoma;⑤ Incomplete clinicopathological data.Basic patient information,laboratory examinations,postoperative pathological data,and Patient Health Questionnaire-9(PHQ-9)scores were collected.Postoperative pathological examination served as the gold standard for LNM diagnosis.LASSO regression was used for preliminary variable screening,followed by multivariate logistic regression to screen for independent risk factors and construct the prediction model,and a nomogram was drawn.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to evaluate discrimination,the calibration curve was used to evaluate calibration,and decision curve analysis(DCA)was used to evaluate clinical utility.Results:A total of 144 patients were included,with an LNM incidence of 52.8%(76/144).Baseline characteristics were balanced between the training set and validation set.LASSO regression and multivariate analysis identified three independent risk factors:PHQ-9 score>3.5[odds ratio(OR)=7.802,95%CI:2.862-25.477],maximum tumor diameter>0.94 cm(OR=4.317,95%CI:2.031-9.532)and tumor number>1.50(OR=2.419,95%CI:1.083-5.573).The AUC of the training set was 0.757 4(95%CI:0.670 3-0.844 5),and the AUC of the validation set was 0.750 0(95%CI:0.535 8-0.964 2).The calibration curve demonstrated good agreement between predicted probabilities and actual observed probabilities within the range of 0.4-1.0.The Hosmer-Lemeshow test(P>0.05)indicated satisfactory model calibration.DCA showed positive net benefits within the threshold probability range of 0.2-0.6,with maximum net benefits of 0.41-0.43,which were superior to the extreme strategies of"treat all"and"treat none".Conclusion:The prediction model constructed in this study based on the PHQ-9 score,maximum tumor diameter and tumor number can effectively predict the preoperative LNM risk in BRAF-mutant PTC patients,with good discrimination,calibration,and clinical utility.This study innovatively incorporated mental health indicators into the preoperative prediction system,providing an objective and quantitative tool for individualized surgical decision-making.