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Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Introduction: To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients’ recurrence risk. Methods: Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at

Serum Levels of Neurotensin, Pannexin-1, and Sestrin-2 and the Correlations with Sleep Quality or/and Cognitive Function in the Patients with Chronic Insomnia Disorder

Objectives: To examine serum concentrations of neurotensin, pannexin-1 and sestrin-2, and their correlations with subjective and objective sleep quality and cognitive function in the patients with chronic insomnia disorder (CID).Methods: Sixty-five CID patients were enrolled continuously and fifty-six good sleepers in the same period were served as healthy controls

Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images

Objective: The pathological classification and imaging manifestation of parotid gland tumors are complex, while accurate preoperative identification plays a crucial role in clinical management and prognosis assessment. This study aims to construct and compare the performance of clinical models, traditional radiomics models, deep learning (DL) models, and deep learning radiomics

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