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http://dx.doi.org/10.1016/j.arbres.2024.10.001 | 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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