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

被引：2

作者：

Villanueva-Meyer, Javier E. ^{[1
,2
]}

Bakas, Spyridon ^{[3
,4
,5
,6
,7
,8
]}

Tiwari, Pallavi ^{[9
]}

Lupo, Janine M. ^{[1
]}

Calabrese, Evan ^{[10
]}

Davatzikos, Christos ^{[11
,12
,13
]}

Bi, Wenya Linda ^{[14
]}

Ismail, Marwa ^{[9
]}

Akbari, Hamed ^{[13
,34
]}

Lohmann, Philipp ^{[18
]}

Booth, Thomas C. ^{[19
,20
,21
]}

Wiestler, Benedikt ^{[22
]}

Aerts, Hugo J. W. L. ^{[16
,35
,36
]}

Rasool, Ghulam ^{[31
]}

Tonn, Joerg C. ^{[23
,24
]}

Nowosielski, Martha ^{[25
]}

Jain, Rajan ^{[26
,27
]}

Colen, Rivka R. ^{[35
,36
]}

Pati, Sarthak ^{[3
]}

Baid, Ujjwal ^{[3
]}

Vollmuth, Philipp ^{[28
]}

Macdonald, David ^{[29
]}

Vogelbaum, Michael A. ^{[30
,31
]}

Chang, Susan M. ^{[2
]}

Huang, Raymond Y. ^{[15
]}

Galldiks, Norbert ^{[17
,32
,33
]}

机构：

[1] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA

[2] Univ Calif San Francisco, Dept Neurol Surg, San Francisco, CA USA

[3] Indiana Univ Sch Med, Div Computat Pathol, Dept Pathol & Lab Med, Indianapolis, IN USA

[4] Indiana Univ Sch Med, Sch Med, Dept Radiol & Imaging Sci, Indianapolis, IN USA

[5] Indiana Univ Sch Med, Dept Biostat & Hlth Data Sci, Indianapolis, IN USA

[6] Dept Neurol SurgeryIndiana Univ Sch Med, Indianapolis, IN USA

[7] Indiana Univ, Melvin & Bren Simon Comprehens Canc Ctr, Indianapolis, IN USA

[8] Indiana Univ, Luddy Sch Informat Comp & Engn, Dept Comp Sci, Indianapolis, IN USA

[9] Univ Wisconsin, Dept Radiol & Biomed Engn, Madison, WI USA

[10] Duke Univ, Ctr Artificial Intelligence Radiol, Dept Radiol, Durham, NC USA

[11] Univ Penn, Ctr Artificial Intelligence & Data Sci Integrated, Philadelphia, PA USA

[12] Univ Penn, Ctr Biomed Image Comp & Analyt Analyt CBICA, Philadelphia, PA USA

[13] Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA USA

[14] Harvard Med Sch, Brigham & Womens Hosp, Dana Farber Canc Inst, Dept Neurosurg, Boston, MA USA

[15] Harvard Med Sch, Brigham & Womens Hosp, Dana Farber Canc Inst, Dept Radiol, Boston, MA USA

[16] Harvard Med Sch, Artificial Intelligence Med AIM Program, Mass Gen Brigham, Boston, MA USA

[17] Res Ctr Juelich FZJ, Inst Neurosci & Med INM 4, Julich, Germany

[18] Univ Hosp RWTH Aachen, Dept Nucl Med, Aachen, Germany

[19] Kings Coll Hosp NHS Fdn Trust, Dept Neuroradiol, London, England

[20] Kings Coll London, Sch Biomed Engn & Imaging Sci, London, England

[21] London Reg Canc Program, London, England

[22] Tech Univ Munich, Univ Hosp, Dept Neuroradiol, Munich, Germany

[23] Ludwig Maximilians Univ Munchen, Dept Neurosurg, Partner Site Munich, Munich, Germany

[24] German Canc Consortium DKTK, Partner Site Munich, Munich, Germany

[25] Med Univ Innsbruck, Dept Neurol, Innsbruck, Austria

[26] New York Univ Langone Hlth, Dept Radiol, New York, NY USA

[27] New York Univ Langone Hlth, Dept Neurosurg, New York, NY USA

[28] Heidelberg Univ Hosp, Dept Neuroradiol, Heidelberg, Germany

[29] H Lee Moffitt Canc Ctr & Res Inst, Dept Neurooncol, Tampa, FL USA

[30] H Lee Moffitt Canc Ctr & Res Inst, Dept Neurosurg, Tampa, FL USA

[31] H Lee Moffitt Canc Ctr & Res Inst, Dept Machine Learning, Tampa, FL USA

[32] Fac Med, Dept Neurol, Cologne, Germany

[33] Univ Hosp Cologne, Cologne, Germany

[34] Santa Clara Univ, Dept Bioengn, Santa Clara, CA USA

[35] Maastricht Univ, Radiol & Nucl Med, CARIM, Maastricht, Netherlands

[36] Maastricht Univ, GROW, Maastricht, Netherlands

来源：

LANCET ONCOLOGY | 2024年 / 25卷 / 11期

关键词：

HIGH-GRADE GLIOMAS; MRI; GLIOBLASTOMA; FEATURES; RADIOMICS; MUTATIONS; CLASSIFICATION; SIGNATURE; SURVIVAL; NETWORK;

D O I：

10.1016/S1470-2045(24)00316-4

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

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.

引用

页码：e581 / e588

页数：8

共 50 条

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