Details

Autor(en) / Beteiligte

• Fathi Kazerooni, Anahita
• Saxena, Sanjay
• Toorens, Erik
• Tu, Danni
• Bashyam, Vishnu
• Akbari, Hamed
• Mamourian, Elizabeth
• Sako, Chiharu
• Koumenis, Costas
• Verginadis, Ioannis
• Verma, Ragini
• Shinohara, Russell T.
• Desai, Arati S.
• Lustig, Robert A.
• Brem, Steven
• Mohan, Suyash
• Bagley, Stephen J.
• Ganguly, Tapan
• O’Rourke, Donald M.
• Bakas, Spyridon
• Nasrallah, MacLean P.
• Davatzikos, Christos

Titel

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Ist Teil von

• Scientific reports, 2022-05, Vol.12 (1), p.8784-8784, Article 8784

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70–0.85)/0.75 (95% CI 0.64–0.79) and 0.75 (95% CI 0.65–0.84)/0.63 (95% CI 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6–0.7) for clinical data improving to 0.75 (95% CI 0.72–0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.