Browsing by Author "Akbari, Hamed"

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    Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements
    (Elsevier, 2024) Villanueva-Meyer, Javier E.; Bakas, Spyridon; Tiwari, Pallavi; Lupo, Janine M.; Calabrese, Evan; Davatzikos, Christos; Bi, Wenya Linda; Ismail, Marwa; Akbari, Hamed; Lohmann, Philipp; Booth, Thomas C.; Wiestler, Benedikt; Aerts, Hugo J. W. L.; Rasool, Ghulam; Tonn, Joerg C.; Nowosielski, Martha; Jain, Rajan; Colen, Rivka R.; Pati, Sarthak; Baid, Ujjwal; Vollmuth, Philipp; Macdonald, David; Vogelbaum, Michael A.; Chang, Susan M.; Huang, Raymond Y.; Galldiks, Norbert; Response Assessment in Neuro Oncology (RANO) group; Pathology and Laboratory Medicine, School of Medicine
    The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.
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    Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice
    (Elsevier, 2024) Bakas, Spyridon; Vollmuth, Philipp; Galldiks, Norbert; Booth, Thomas C.; Aerts, Hugo J. W. L.; Bi, Wenya Linda; Wiestler, Benedikt; Tiwari, Pallavi; Pati, Sarthak; Baid, Ujjwal; Calabrese, Evan; Lohmann, Philipp; Nowosielski, Martha; Jain, Rajan; Colen, Rivka; Ismail, Marwa; Rasool, Ghulam; Lupo, Janine M.; Akbari, Hamed; Tonn, Joerg C.; Macdonald, David; Vogelbaum, Michael; Chang, Susan M.; Davatzikos, Christos; Villanueva-Meyer, Javier E.; Huang, Raymond Y.; Response Assessment in Neuro Oncology (RANO) group; Pathology and Laboratory Medicine, School of Medicine
    Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.
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    The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
    (Springer Nature, 2025-03-01) Fathi Kazerooni, Anahita; Akbari, Hamed; Hu, Xiaoju; Bommineni, Vikas; Grigoriadis, Dimitris; Toorens, Erik; Sako, Chiharu; Mamourian, Elizabeth; Ballinger, Dominique; Sussman, Robyn; Singh, Ashish; Verginadis, Ioannis I.; Dahmane, Nadia; Koumenis, Constantinos; Binder, Zev A.; Bagley, Stephen J.; Mohan, Suyash; Hatzigeorgiou, Artemis; O'Rourke, Donald M.; Ganguly, Tapan; De, Subhajyoti; Bakas, Spyridon; Nasrallah, MacLean P.; Davatzikos, Christos; Pathology and Laboratory Medicine, School of Medicine
    Background: Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events. Methods: We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity. Results: Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas. Conclusions: This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.