| Autor: |
Hsieh KL; Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Chen Q; Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA., Salzillo TC; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Zhang J; Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA., Jiang X; Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics at UTHealth Houston, Houston, TX 77030, USA., Bhattacharya PK; Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Shams S; Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics at UTHealth Houston, Houston, TX 77030, USA. |
| Abstrakt: |
Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs and a week earlier than HPMRS alone. Our model accurately predicted in vivo biomarkers from HPMRS, and the results inferred biological relevance. Additionally, the model showed potential for examining treatment effects. Our model successfully detected tumor progression two weeks earlier than conventional MRIs and accurately predicted in vivo biomarkers using ex vivo information such as conventional MRIs, HPMRS, and tumor size data. The accuracy of these predictions is consistent with biological relevance. |
