Factors associated with the local control of brain metastases:A systematic search and machine learning application

Background: Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment failure a crucial aspect of treatment planning. Understanding the factors that influence LC of brain metastases is imperative for optimizing treatment strategies and subsequently extending overall survival. Machine learning algorithms may help to identify factors that predict outcomes. Methods: This paper systematically reviews these factors associated with LC to select candidate predictor features for a practical application of predictive modeling. A systematic literature search was conducted to identify studies in which the LC of brain metastases is assessed for adult patients. EMBASE, PubMed, Web-of-Science, and the Cochrane Database were searched up to December 24, 2020. All studies investigating the LC of brain metastases as one of the endpoints were included, regardless of primary tumor type or treatment type. We first grouped studies based on primary tumor types resulting in lung, breast, and melanoma groups. Studies that did not focus on a specific primary cancer type were grouped based on treatment types resulting in surgery, SRT, and whole-brain radiotherapy groups. For each group, significant factors associated with LC were identified and discussed.. As a second project, we assessed the practical importance of selected features in predicting LC after Stereotactic Radiotherapy (SRT) with a Random Forest machine learning model. Accuracy and Area Under the Curve (AUC) of the Random Forest model, trained with the list of factors that were found to be associated with LC for the SRT treatment group, were reported.Results: The systematic literature search identified 6270 unique records. After screening titles and abstracts, 410 full texts were considered, and ultimately 159 studies were included for review. Most of the studies focused on the LC of the brain metastases for a specific primary tumor type or after a specific treatment type. Higher SRT radiation dose was found to be associated with better LC in lung cancer, breast cancer, and melanoma groups. Also, a higher dose was associated with better LC in the SRT group, while higher tumor volume was associated with worse LC in this group. The Random Forest model predicted the LC of brain metastases with an accuracy of 80% and an AUC of 0.84. Conclusion: This paper thoroughly examines factors associated with LC in brain metastases and highlights the translational value of our findings for selecting variables to predict LC in a sample of patients who underwent SRT. The prediction model holds great promise for clinicians, offering a valuable tool to predict personalized treatment outcomes and foresee the impact of changes in treatment characteristics such as radiation dose.<br/

Memory in low-grade glioma patients treated with radiotherapy or temozolomide: a correlative analysis of EORTC study 22033-26033.

EORTC study 22033-26033 showed no difference in progression-free survival between high-risk low-grade glioma receiving either radiotherapy (RT) or temozolomide (TMZ) chemotherapy alone as primary treatment. Considering the potential long-term deleterious impact of RT on memory functioning, this study aims to determine whether TMZ is associated with less impaired memory functioning. Using the Visual Verbal Learning Test (VVLT), memory functioning was evaluated at baseline and subsequently every 6 months. Minimal compliance for statistical analyses was set at 60%. Conventional indices of memory performance (VVLT Immediate Recall, Total Recall, Learning Capacity, and Delayed Recall) were used as outcome measures. Using a mixed linear model, memory functioning was compared between treatment arms and over time. Neuropsychological assessment was performed in 98 patients (53 RT, 46 TMZ). At 12 months, compliance had dropped to 66%, restricting analyses to baseline, 6 months, and 12 months. At baseline, patients in either treatment arm did not differ in memory functioning, sex, age, or educational level. Over time, patients in both arms showed improvement in Immediate Recall (P = 0.017) and total number of words recalled (Total Recall; P &lt; 0.001, albeit with delayed improvement in RT patients (group by time; P = 0.011). Memory functioning was not associated with RT gross, clinical, or planned target volumes. In patients with high-risk low-grade glioma there is no indication that in the first year after treatment, RT has a deleterious effect on memory function compared with TMZ chemotherapy

The EPTN consensus-based atlas for CT- and MR-based contouring in neuro-oncology

Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Optimization of Brain and Head &amp; Neck Radiotherapy

The aim of this thesis is to further optimise radiation therapy of Brain and Head &amp; Neck by reducing the dose to the healthy surrounding tissue, so called organs at risk (OARs), leading to a reduction in side effects. The first objective of this doctoral thesis was to assess the value of proton therapy in reducing the dose to the OARs, in particular for re-irradiation in head and neck squamous cell carcinoma and primary irradiation of low-grade glioma. Chapters 2 and 3 report on two in silico trials conducted within the international Radiation Oncology Collaborative Comparison (ROCOCO)..

The posterior cerebellum, a new organ at risk?

Eekers et al. have recently proposed a neuro-oncology atlas [doi:10.17195/candat.2017.08.1, Ed.], which was co-authored by most centers associated in the European Proton Therapy Network (EPTN). With the introduction of new treatment techniques, such as integrated magnetic resonance imaging and linear accelerators (MR-linac) or particle therapy, the prediction of clinical efficacy of these more costly treatment modalities becomes more relevant. One of the side-effects of brain irradiation, being cognitive decline, is one of the toxicities most difficult to measure and predict. In order to..

Subclassification of the Koos grade 2 vestibular schwannoma into 2a and 2b for individualized patient care: A validity and reliability study

Objective: Vestibular schwannoma (VS) growth of >= 2 mm during serial MRI observation, irrespective of size, is the benchmark for treatment initiation in almost all centers. Although the probability of less optimal outcomes significantly increases in VS closer to the brainstem, early intervention does not improve long-term quality of life. Moving beyond the recommendation of definitive treatment for all VS after detected growth, we subclassified Koos 2 tumors based on extrameatal extension and relation to the brainstem. The aim of the current study was to evaluate the Koos 2 subclassification's validity and the inter-and intra-rater reliability of the entire Koos classification.Methods: Six experts, including neurosurgeons, otorhinolaryngologists and radiologists from two tertiary referral centers, classified 43 VS MRI scans. Validity of the Koos 2 subclassification was evaluated by the percentage agreement against the multidisciplinary skull base tumor board management advice. Inter-and intra-rater reliability were calculated using the intraclass correlation coefficient (ICC).Results: Validity was almost perfect in Koos 2a VSs with a 100% agreement and 87.5% agreement for Koos 2b. Inter-rater reliability for all Koos grades was significantly excellent (ICC 0.91; 95%CI 0.866 to 0.944, p= 0.90; p= <0.01) and one rater had a good intra-rater reliability (ICC 0.88; 95% CI 0.742 to 0.949).Conclusions: Although multiple factors influence decision-making, the classification of Koos 2a and 2b with excellent inter-and intra-rater reliability, can aid in recommending treatment initiation, moving beyond detected tumor growth, aiming to optimize patient centered care

Evaluation of the Hippocampal Normal Tissue Complication Model in a Prospective Cohort of Low Grade Glioma Patients-An Analysis Within the EORTC 22033 Clinical Trial

Neurolog