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

Javier E Villanueva-Meyer; Spyridon Bakas; Pallavi Tiwari; Janine M Lupo; Evan Calabrese; Christos Davatzikos; Wenya Linda Bi; Marwa Ismail; Hamed Akbari; Philipp Lohmann; Thomas C Booth; Benedikt Wiestler; Hugo J W L Aerts; Ghulam Rasool; Joerg C Tonn; Martha Nowosielski; Rajan Jain; Rivka R Colen; Sarthak Pati; Ujjwal Baid; Philipp Vollmuth; David Macdonald; Michael A Vogelbaum; Susan M Chang; Raymond Y Huang; Norbert Galldiks; Response Assessment in Neuro Oncology (RANO) group

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

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

Lancet Oncol. 2024 Nov;25(11):e581-e588. doi: 10.1016/S1470-2045(24)00316-4.

Authors

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
DOI: 10.1016/S1470-2045(24)00316-4

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.

Publication types

Review

MeSH terms

Artificial Intelligence*
Brain Neoplasms / genetics
Brain Neoplasms / pathology
Brain Neoplasms / therapy
Humans
Medical Oncology / methods
Prognosis
Treatment Outcome