Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme
Peeken JC, Goldberg T, Pyka T, Bernhofer M, Wiestler B, Kessel KA, Tafti PD, Nuesslin F, Braun AE, Zimmer C, Rost B, Combs SE (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 8
Pages Range: 128-136
Journal Issue: 1
DOI: 10.1002/cam4.1908
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.
Involved external institutions
Germany (DE)How to cite
APA:
Peeken, J.C., Goldberg, T., Pyka, T., Bernhofer, M., Wiestler, B., Kessel, K.A.,... Combs, S.E. (2019). Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme. Cancer Medicine, 8(1), 128-136. https://doi.org/10.1002/cam4.1908
MLA:
Peeken, Jan C., et al. "Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme." Cancer Medicine 8.1 (2019): 128-136.
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