Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics.

Background: Personalized medicine has transformed disease management by focusing on individual characteristics, driven by advancements in genome mapping and biomarker discoveries.

Objectives: This study aims to develop a predictive model for the early detection of treatment-related cardiac side effects in breast cancer patients by integrating clinical data, high-sensitivity Troponin-T (hs-TropT), radiomics, and dosiomics. The ultimate goal is to identify subclinical cardiotoxicity before clinical symptoms manifest, enabling personalized surveillance strategies.

The Effects of Probiotic-Prebiotic Use on Acute Inflammatory Parameters in Patients Receiving Pelvic Radiotherapy: A Prospective Multicenter Study.

This is the first prospective clinical study to evaluate the impact of prebiotic and probiotic use on acute radiation-induced pelvic side effects and associated changes in serum cytokine levels in patients with pelvic malignancies. This multicenter prospective study included 80 patients with pelvic cancers treated at two centers between 2021 and 2023. Patients were divided into three groups: probiotic + prebiotic, prebiotic only, and nonsupplemented.

Synergistic Potential of GLP-1 Receptor Agonists and Radiotherapy in Breast Cancer Treatment: A New Therapeutic Avenue (TROD-GROG 006).

Introduction: GLP-1 receptor agonists (GLP-1 RAs) are anti-diabetic agents known for their anti-inflammatory and antioxidant properties. This study investigates the synergistic effects of GLP-1 RAs and radiotherapy (RT) on breast cancer in a preclinical mouse model.

Materials And Methods: Female BALB/c mice were inoculated with 4T1 breast cancer cells and divided into five groups: control, placebo, GLP-1 RA alone, RT alone, and combination treatment.