Digital biomarkers: Importance of patient stratification for re-irradiation of glioma patients - Review of latest developments regarding scoring assessment.

PURPOSE: To review scoring assessments in re-irradiation of high-grade glioma (HGG) patients and how to use scoring for patient stratification. The next aim was to investigate the different approaches employed by the scoring systems and the way they can be applied to build homogeneous patient groups for a reliable prognosis. METHODS: We searched the Medline/Pubmed and Web of science databases for relevant articles regarding scores for re-irradiation of recurrent HGG. All references were divided into the following groups: novel score establishment (n = 5), score validation (n = 6), not relevant to this evaluation (n = 26). RESULTS: We identified five scoring systems. Two are modifications of an already existing score. Calculations differ immensely from easy point addition to a more complex formula with including three up to 10 individual parameters. Six validation articles were found for three of the scores; one was validated four times. Two scores were never validated. CONCLUSION: For recurrent HGG, the clinical situation remains demanding. Due to the heterogeneity of data at re-irradiation, patient stratification is important. Several scoring systems have been developed to predict prognosis. As a digital biomarker, scores are of high value regarding quick patient assessment and therapy decision making. For the next generation of digital biomarkers, easy calculation, and inclusion of easily available parameters are crucial.

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme.

BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, pathological, semantic MRI-based, and FET-PET/CT-derived information. Finally, the value of adding treatment features was evaluated. METHODS: One hundred and eighty-nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET-PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI-based," and "FET-PET/CT-based" models, as well as combinations. Treatment features were combined with all other features. RESULTS: Of all single feature class models, the MRI-based model had the highest prediction performance on the validation set for OS (C-index: 0.61 [95% confidence interval: 0.51-0.72]) and PFS (C-index: 0.61 [0.50-0.72]). The combination of all features did increase performance above all single feature class models up to C-indices of 0.70 (0.59-0.84) and 0.68 (0.57-0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C-indices of 0.73 (0.62-0.84) and 0.71 (0.60-0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. CONCLUSIONS: MRI-based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.