Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

Javier E Villanueva-Meyer¹, Spyridon Bakas², Pallavi Tiwari³, Janine M Lupo⁴, Evan Calabrese⁵, Christos Davatzikos⁶, Wenya Linda Bi⁷, Marwa Ismail³, Hamed Akbari⁸, Philipp Lohmann⁹, Thomas C Booth¹⁰, Benedikt Wiestler¹¹, Hugo J W L Aerts¹², Ghulam Rasool¹³, Joerg C Tonn¹⁴, Martha Nowosielski¹⁵, Rajan Jain¹⁶, Rivka R Colen¹⁷, Sarthak Pati¹⁸, Ujjwal Baid¹⁸, Philipp Vollmuth¹⁹, David Macdonald²⁰, Michael A Vogelbaum²¹, Susan M Chang²², Raymond Y Huang²³, Norbert Galldiks²⁴; Response Assessment in Neuro Oncology (RANO) group

Affiliations

¹ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA. Electronic address: javier.villanueva-meyer@ucsf.edu.
² Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA; Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN, USA.
³ Department of Radiology and Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
⁴ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
⁵ Duke University Center for Artificial Intelligence in Radiology, Department of Radiology, Duke University, Durham, NC, USA.
⁶ Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁷ Department of Neurosurgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
⁸ Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, Santa Clara University, Santa Clara, CA, USA.
⁹ Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany.
¹⁰ Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; London Regional Cancer Program, London, UK.
¹¹ Department of Neuroradiology, University Hospital, Technical University of Munich, Munich, Germany.
¹² Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, Netherlands.
¹³ Department of Machine Learning, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
¹⁴ Department of Neurosurgery, Ludwig Maximilians University, Munich, Germany and German Cancer Consortium (DKTK), Partner Site Munich, Germany.
¹⁵ Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.
¹⁶ Department of Radiology and Department of Neurosurgery, New York University Langone Health, New York, NY, USA.
¹⁷ Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, Netherlands.
¹⁸ Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
¹⁹ Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
²⁰ Department of Neuro-Oncology, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
²¹ Department of Neurosurgery, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Machine Learning, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
²² Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
²³ Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
²⁴ Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.

PMID: 39481414
PMCID: PMC12045294
DOI: 10.1016/S1470-2045(24)00316-4

Review

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

Javier E Villanueva-Meyer et al. Lancet Oncol. 2024 Nov.

. 2024 Nov;25(11):e581-e588.

doi: 10.1016/S1470-2045(24)00316-4.

Authors

Affiliations

¹ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA. Electronic address: javier.villanueva-meyer@ucsf.edu.
² Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA; Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN, USA.
³ Department of Radiology and Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
⁴ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
⁵ Duke University Center for Artificial Intelligence in Radiology, Department of Radiology, Duke University, Durham, NC, USA.
⁶ Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁷ Department of Neurosurgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
⁸ Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, Santa Clara University, Santa Clara, CA, USA.
⁹ Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany.
¹⁰ Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; London Regional Cancer Program, London, UK.
¹¹ Department of Neuroradiology, University Hospital, Technical University of Munich, Munich, Germany.
¹² Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, Netherlands.
¹³ Department of Machine Learning, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
¹⁴ Department of Neurosurgery, Ludwig Maximilians University, Munich, Germany and German Cancer Consortium (DKTK), Partner Site Munich, Germany.
¹⁵ Department of Neurology, Medical University Innsbruck, Innsbruck, Austria.
¹⁶ Department of Radiology and Department of Neurosurgery, New York University Langone Health, New York, NY, USA.
¹⁷ Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, Netherlands.
¹⁸ Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
¹⁹ Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
²⁰ Department of Neuro-Oncology, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
²¹ Department of Neurosurgery, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Machine Learning, H Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
²² Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
²³ Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
²⁴ Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.

PMID: 39481414
PMCID: PMC12045294
DOI: 10.1016/S1470-2045(24)00316-4

Erratum in

Correction to Lancet Oncol 2024; 25: e581-88.
[No authors listed] [No authors listed] Lancet Oncol. 2024 Dec;25(12):e626. doi: 10.1016/S1470-2045(24)00693-4. Lancet Oncol. 2024. PMID: 39637899 No abstract available.

Abstract

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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Conflict of interest statement

Declaration of interests JML received grants from the National Institutes of Health (NIH; P01CA118816), Department of Defense, and GE Healthcare. WLB reported receiving an honorarium from Stryker. PL received a grant from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; 428090865/SPP 2177). TCB received consulting fees from Microvention; is on speakers bureaus for Siemens Healthineers, Bayer, and Medtronic; and received grants from the Medical Research Council (MR/W021684/1) and the Wellcome Engineering and Physical Sciences Research Council Centre for Medical Engineering (WT 203,148/Z/16/Z). HJWLA received grants from the NIH (U24CA194354, U01CA190234, U01CA209414, R35CA22052, and U54CA27451), the European Research Council (866504), and the US Department of Veterans Affairs; and consulting fees from Onc.AI, Sphera, and Love Health. JCT received grants from Novocure and Munich Surgical Imaging; royalties from Springer Publishing; consulting fees from Novartis; an honorarium from the Italian Society for Neurology; and support to attend meetings of the Belgian Association for Neurooncology and Glioma Meeting Athens 2023. RJ received a grant from AIRS Medical. MAV received grants from Infuseon Therapeutics, Oncosynergy, and DeNovo Pharma; consulting fees from Servier Pharma and BioDexa Pharma; and holds patents and received royalties for drug delivery devices. RYH reported consulting fees from Nuvation Bio and Vysioneet. NG received a grant from the DFG (428090865/SPP 2177); honorarium from Blue Earth Diagnostics; and participated on an advisory board for Telix Pharmaceuticals. CD received grants from the NIH (U24CA189523, UL1TR001878, and R01NS042645). SB received grants from the NIH (U01CA242871 and UL1TR001878). RRC received grants from the NIH. MI received grants from the Musella Foundation Grant and the R&D Pilot Award from the University of Wisconsin-Madison. PT received grants from the NIH (U01CA248226, R01CA264017, and U01CA248226), the Veterans Affairs (VA) Merit Award (1I01BX005842-01A2), the DOD/PRCRP Career Development Award (W81XWH-18-1-0404), the Dana Foundation David Mahoney Neuroimaging Program, the V Foundation Translational Research Award, the Johnson & Johnson WiSTEM2D Award, the Ohio Third Frontier Technology Validation Fund, the Musella Foundation Grant, and the R&D Pilot Award from the University of Wisconsin-Madison. All other authors declare no competing interests.

Figures

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References

1. Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated biophysical modeling and image analysis. Application to neuro-oncology. Annu Rev Biomed Eng 2020; 22: 309–41. - PMC - PubMed
1. Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C. Imaging signatures of glioblastoma molecular characteristics: a radiogenomics review. J Magn Reson Imaging 2020; 52: 54–69. - PMC - PubMed
1. Fathi Kazerooni A, Bagley SJ, Akbari H, et al. Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine. Cancers 2021; 13: 5921. - PMC - PubMed
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Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

Affiliations

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements

Authors

Affiliations

Erratum in

Abstract

Conflict of interest statement

Figures

References

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MeSH terms

Grants and funding

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