Survival Analysis of Glioblastoma: A Scientometric Perspective.

Background: Glioblastoma is the most aggressive primary brain tumor and the outlook for patients is usually pessimistic. Numerous ongoing studies have focused on enhancing the survival rate of glioblastoma patients. This study aims to analyze the research trends surrounding glioblastoma survival and facilitate studying recent topics to provide insight into the perspective, research fields, and international collaborations.

Comparison prediction models of bladder toxicity based on radiomic features of CT and MRI in patients with prostate cancer undergoing radiotherapy.

Purpose: This study aimed to assess the radiomic features of computed tomography (CT) and magnetic resonance imaging (MRI) of the bladder wall before radiotherapy using machine learning (ML) methods to predict bladder radiotoxicity in patients with prostate cancer.

Methods: This study enrolled 70 patients with pathologically confirmed prostate cancer who were candidates for radiation therapy (RT). CT and MRI of the bladder wall before radiotherapy were used to extract radiomic features.

Dosiomics-based detection of dose distribution variations in helical tomotherapy for prostate cancer patients: influence of treatment plan parameters.

The stability of dosiomics features (DFs) and dose-volume histogram (DVH) parameters for detecting disparities in helical tomotherapy planned dose distributions was assessed. Treatment plans of 18 prostate patients were recalculated using the followings: field width (WF) (2.5 vs.

Safety evaluation of the trastuzumab biosimilar in Iranian women with HER2-positive breast cancer undergoing adjuvant chemotherapy: a post-marketing surveillance.

Background: Trastuzumab is a humanized monoclonal antibody against the human epidermal growth factor receptor 2 (HER2). This post-marketing surveillance evaluates the safety of a trastuzumab biosimilar (AryoTrust), produced by AryoGen Co. Iran in Iranian women with HER2-positive non-metastatic breast cancer (BC).

Prospective Prediction of Treatment Response in High-Grade Glioma Patients using Pre-Treatment Tumor ADC Value and miR-222 and miR-205 Expression Levels in Plasma.

Background: Treatment response in High-grade Glioma (HGG) patients changes based on their genetic and biological characteristics. MiRNAs, as important regulators of drug and radiation resistance, and the Apparent Diffusion Coefficients (ADC) value of tumor can be used as a prognostic predictor for glioma.

Objective: This study aimed to identify some of the pre-treatment individual patient features for predicting the treatment response in HGG patients.