Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis.

This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC, and other metrics derived from confusion matrices. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.

Immunotherapy and radiotherapy - a future partnership Focus on radiation induced lymphopenia (RIL) implications.

The hematological toxicity associated with radiotherapy is represented by neutropenia, anemia, thrombocytopenia, being associated with the increased risk of infection with opportunists, with fatigue and intolerance to effort, but also with the increased risk of bleeding. In the context of the preclinical and clinical results that mention the synergistic effect of the immunotherapy-radiotherapy association, radiation-induced lymphopenia (RIL) becomes an immunosuppression factor, a factor that would tip the fragile antitumor immunopotentiation-immunosuppression balance in favor of the immunosuppressive effect. Both the number of lymphocytes and the neutrophil/lymphocyte ratio (NLR) are prognostic and predictive biomarkers, providing information on the immune status of the host and on a possible response of the tumor to immunotherapy.

Immunotherapy and Radiotherapy as an Antitumoral Long-Range Weapon-A Partnership with Unsolved Challenges: Dose, Fractionation, Volumes, Therapeutic Sequence.

Immunotherapy, the modern oncological treatment with immune checkpoint inhibitors (ICIs), has been part of the clinical practice for malignant melanoma for more than a decade. Anti-cytotoxic T-lymphocyte antigen 4 (CTLA4), anti-programmed cell death Protein 1 (PD-1), or anti programmed death-ligand 1 (PD-L1) agents are currently part of the therapeutic arsenal of metastatic or relapsed disease in numerous cancers; more recently, they have also been evaluated and validated as consolidation therapy in the advanced local stage. The combination with radiotherapy, a treatment historically considered loco-regional, changes the paradigm, offering-via synergistic effects-the potential to increase immune-mediated tumor destruction.

Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology.

Radiomics is a relatively new concept that consists of extracting data from images and applies advanced characterization algorithms to generate imaging features. These features are biomarkers with prognostic and predictive value, which provide a characterization of tumor phenotypes in a non-invasive manner. The clinical application of radiomics is hampered by challenges such as lack of image acquisition and analysis standardization.