Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme. (18th December 2018)

• Record Type: Journal Article
• Authors: Peeken, Jan C.; Goldberg, Tatyana; Pyka, Thomas; Bernhofer, Michael; Wiestler, Benedikt; Kessel, Kerstin A.; Tafti, Pouya D.; Nüsslin, Fridtjof; Braun, Andreas E.; Zimmer, Claus; Rost, Burkhard; Combs, Stephanie E.
• Is Part Of: Cancer medicine. Volume 8:Number 1(2019:Jan.)
• Page Start: 128
• Page End: 136
• Publication Date: 2018-12-18
• Subjects: biomarker -- FET‐PET -- glioblastoma -- machine learning -- MRI -- prognostic model -- VASARI; 616.994005
• DOI: 10.1002/cam4.1908
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract : 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. Abstract : In comparison with clinical, pathological, and FET‐PET based features, semantic MRI‐based (VASARI) showed the best performance predicting OS and PFS in GBM patients. Combining all features triggered an improved predictive performance above single feature class models. Adding treatment information to the combined model achieved the best predictive performance in an internal validation cohort with a concordance index of up to 0.74 and 0.72 for OS and PFS, respectively. .