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. Modern radiation techniques can increase the risk of lymphopenia by irradiating large volumes of tissue with low doses of radiation. In this context, a redefinition of the dose-volume constraints and the definition of bone marrow, lymphoid organs and lymph nodes not involved in tumors as organs at risk (OARs) is strictly necessary in the case of using irradiation through intensity-modulated radiation therapy (IMRT) techniques or volumetric modulated arc therapy (VMAT) for solid tumors that benefit from immune checkpoint inhibitor (ICI) therapy (Ref. 22). Text in PDF www.elis.sk Keywords: lymphopenia, immunotherapy, radiotherapy, toxicity, spleen, nodes irradiation.

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 0.97, effectively complementing the broader model analysis. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like "control at one year", "age of the patient", and "beam-on time for volume V1 treated" were consistently influential across various models, albeit their impacts were interpreted differently depending on the model's underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research.

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. Textural features extracted from computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT) images of patients diagnosed with head and neck cancers can be used in the pre-therapeutic evaluation of the response to multimodal chemo-radiotherapy. For patients with positive HPV-oropharyngeal cancers, the correlation of the radiomic textural features from the tumor with p16 values from the pathological sample can identify tumor specific signatures in CT imaging, an entity with favorable prognosis and a better response to chemo-radiotherapy. Pretreatment contrast CT-scans were extracted and radiomics analysis of gross tumor volume were performed using MaZda package apart from MaZda software containing B11 program for texture analysis and visualization. Data set was randomly divided into a training dataset and a test dataset and machine learning algorithms were applied to identify a textural radiomic signature. Radiomic texture analysis and machine learning algorithms demonstrate a predictive potential related to the capability of stratification for subclasses of platinum-chemotherapy resistance and radioresistant head and neck cancers requiring an intensification of multimodal treatment.