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Objective: Medullary thyroid carcinoma (MTC) patients with distant metastases frequently present a relatively poor survival prognosis. Our main purpose was developing a nomogram model to predict distant metastases in MTC patients.
Methods: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database. Data of 807 MTC patients diagnosed from 2004 to 2015 who undergone total thyroidectomy and neck lymph nodes dissection was included in our study. Independent risk factors were screened by univariate and multivariate logistic regression analysis successively, which were used to develop a nomogram model predicting for distant metastasis risk. Further, the log-rank test was used to compare the differences of Kaplan-Meier curves of cancer-specific survival (CSS) in different M stage and each independent risk factor groups.
Results: Four clinical parameters including age > 55 years, higher T stage (T3/T4), higher N stage (N1b) and lymph node ratio (LNR) > 0.4 were significant for distant metastases at the time of diagnosis in MTC patients, and were selected to develop a nomogram model. This model had satisfied discrimination with the AUC and C-index of 0.894, and C-index was confirmed to be 0.878 through bootstrapping validation. A decision curve analysis (DCA) was subsequently made to evaluate the feasibility of this nomogram for predicting distant metastasis. In addition, CSS differed by different M stage, T stage, N stage, age and LNR groups.
Conclusions: Age, T stage, N stage and LNR were extracted to develop a nomogram model for predicting the risk of distant metastases in MTC patients. The model is of great significance for clinicians to timely identify patients with high risk of distant metastases and make further clinical decisions.
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http://dx.doi.org/10.3389/fendo.2023.1119656 | DOI Listing |
J Neurooncol
September 2025
Department of Neurology, Xiangya Hospital, Central South University, No.87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan Province, China.
Background And Objective: Differentiating central nervous system infections (CNSIs) from brain tumors (BTs) is difficult due to overlapping features and the limited individual indicators, and cerebrospinal fluid (CSF) cytology remains underutilized. To improve differential diagnosis, we developed a model based on 9 early, cost-effective cerebrospinal fluid parameters, including CSF cytology.
Methods: Patients diagnosed with CNSIs or BTs at Xiangya Hospital of Central South University between October 1st, 2017 and March 31st, 2024 were enrolled and divided into the training set and the test set.
Ann Surg Oncol
September 2025
Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.
Background: The optimal number of examined lymph nodes (ELN) for accurate staging and prognosis for esophageal cancer patients receiving neoadjuvant therapy remains controversial. This study aimed to evaluate the impact of ELN count on pathologic staging and survival outcomes and to develop a predictive model for lymph node positivity in this patient population.
Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and a multicenter cohort.
Ann Surg Oncol
September 2025
Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China.
Background: Postoperative late recurrence (POLAR) after 2 years from the date of surgical resection of hepatocellular carcinoma (HCC) represents a unique surveillance and management challenge. Despite identified risk factors, individualized prediction tools to guide personalized surveillance strategies for recurrence remain scarce. The current study sought to develop a predictive model for late recurrence among patients undergoing HCC resection.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Thoracic Surgery, Changchun Tumor Hospital.
Objective: The risk factors of postoperative survival in T4N0M0 NSCLC patients are not fully understood. This study aimed to develop and validate a nomogram model for predicting postoperative survival in patients with T4N0M0 non-small cell lung cancer (NSCLC).
Methods: Clinicopathological data of patients were collected from Surveillance, Epidemiology, and End Results (SEER) database.
Clin Nurs Res
September 2025
Xuzhou Medical University, Jiangsu Province, China.
This study aimed to develop and validate a machine learning-based predictive model for assessing the risk of fear of childbirth in pregnant women during late pregnancy. A cross-sectional observational study was conducted from November 2022 to July 2023, involving 406 pregnant women. Six machine learning algorithms, including Lasso-assisted logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGB), support vector machine (SVM), Bayesian network (BN), and k-nearest neighbors (KNN), were used to construct the models with 10-fold cross-validation.
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