From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

被引：285

作者：

Swanson, Kyle ^{[1
]}

Wu, Eric ^{[2
]}

Zhang, Angela ^{[3
]}

Alizadeh, Ash A. ^{[4
]}

Zou, James ^{[1
,2
,5
]}

机构：

[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Genet, Stanford, CA USA

[4] Stanford Univ, Dept Med, Stanford, CA USA

[5] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA

来源：

CELL | 2023年 / 186卷 / 08期

基金：

美国国家卫生研究院;

关键词：

ARTIFICIAL-INTELLIGENCE; GENE-EXPRESSION; LUNG-CANCER; SKIN-CANCER; DEEP; SYSTEM; CLASSIFICATION; VALIDATION; BIOPSIES; DENSITY;

D O I：

10.1016/j.cell.2023.01.035

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient out-comes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.

引用

页码：1772 / 1791

页数：20

共 139 条

[11] Harnessing multimodal data integration to advance precision oncology [J].

Boehm, Kevin M. ;

Khosravi, Pegah ;

Vanguri, Rami ;

Gao, Jianjiong ;

Shah, Sohrab P. .

NATURE REVIEWS CANCER, 2022, 22 (02) :114-126

[12] Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study [J].

Bulten, Wouter ;

Pinckaers, Hans ;

van Boven, Hester ;

Vink, Robert ;

de Bel, Thomas ;

van Ginneken, Bram ;

van der Laak, Jeroen ;

Hulsbergen-van de Kaa, Christina ;

Litjens, Geert .

LANCET ONCOLOGY, 2020, 21 (02) :233-241

[13] Deep learning based tissue analysis predicts outcome in colorectal cancer [J].

Bychkov, Dmitrii ;

Linder, Nina ;

Turkki, Riku ;

Nordling, Stig ;

Kovanen, Panu E. ;

Verrill, Clare ;

Walliander, Margarita ;

Lundin, Mikael ;

Haglund, Caj ;

Lundin, Johan .

SCIENTIFIC REPORTS, 2018, 8

[14] Clinical-grade computational pathology using weakly supervised deep learning on whole slide images [J].

Campanella, Gabriele ;

Hanna, Matthew G. ;

Geneslaw, Luke ;

Miraflor, Allen ;

Silva, Vitor Werneck Krauss ;

Busam, Klaus J. ;

Brogi, Edi ;

Reuter, Victor E. ;

Klimstra, David S. ;

Fuchs, Thomas J. .

NATURE MEDICINE, 2019, 25 (08) :1301-+

[15] Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet [J].

Cao, Ruiming ;

Bajgiran, Amirhossein Mohammadian ;

Mirak, Sohrab Afshari ;

Shakeri, Sepideh ;

Zhong, Xinran ;

Enzmann, Dieter ;

Raman, Steven ;

Sung, Kyunghyun .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2019, 38 (11) :2496-2506

[16] DNA methylation-based classification of central nervous system tumours [J].

Capper, David ;

Jones, David T. W. ;

Sill, Martin ;

Hovestadt, Volker ;

Schrimpf, Daniel ;

Sturm, Dominik ;

Koelsche, Christian ;

Sahm, Felix ;

Chavez, Lukas ;

Reuss, David E. ;

Kratz, Annekathrin ;

Wefers, Annika K. ;

Huang, Kristin ;

Pajtler, Kristian W. ;

Schweizer, Leonille ;

Stichel, Damian ;

Olar, Adriana ;

Engel, Nils W. ;

Lindenberg, Kerstin ;

Harter, Patrick N. ;

Braczynski, Anne K. ;

Plate, Karl H. ;

Dohmen, Hildegard ;

Garvalov, Boyan K. ;

Coras, Roland ;

Hoelsken, Annett ;

Hewer, Ekkehard ;

Bewerunge-Hudler, Melanie ;

Schick, Matthias ;

Fischer, Roger ;

Beschorner, Rudi ;

Schittenhelm, Jens ;

Staszewski, Ori ;

Wani, Khalida ;

Varlet, Pascale ;

Pages, Melanie ;

Temming, Petra ;

Lohmann, Dietmar ;

Selt, Florian ;

Witt, Hendrik ;

Milde, Till ;

Witt, Olaf ;

Aronica, Eleonora ;

Giangaspero, Felice ;

Rushing, Elisabeth ;

Scheurlen, Wolfram ;

Geisenberger, Christoph ;

Rodriguez, Fausto J. ;

Becker, Albert ;

Preusser, Matthias .

NATURE, 2018, 555 (7697) :469-+

[17] Can we open the black box of AI? [J].

Castelvecchi D. .

Nature, 2016, 538 (7623) :20-23

[18]

Center for Devices Radiological Health, 2022, Artificial intelligence and machine learning in software as a medical device

[19] Integrating genomic features for non-invasive early lung cancer detection [J].

Chabon, Jacob J. ;

Hamilton, Emily G. ;

Kurtz, David M. ;

Esfahani, Mohammad S. ;

Moding, Everett J. ;

Stehr, Henning ;

Schroers-Martin, Joseph ;

Nabet, Barzin Y. ;

Chen, Binbin ;

Chaudhuri, Aadel A. ;

Liu, Chih Long ;

Hui, Angela B. ;

Jin, Michael C. ;

Azad, Tej D. ;

Almanza, Diego ;

Jeon, Young-Jun ;

Nesselbush, Monica C. ;

Keh, Lyron Co Ting ;

Bonilla, Rene F. ;

Yoo, Christopher H. ;

Ko, Ryan B. ;

Chen, Emily L. ;

Merriott, David J. ;

Massion, Pierre P. ;

Mansfield, Aaron S. ;

Jen, Jin ;

Ren, Hong Z. ;

Lin, Steven H. ;

Costantino, Christina L. ;

Burr, Risa ;

Tibshirani, Robert ;

Gambhir, Sanjiv S. ;

Berry, Gerald J. ;

Jensen, Kristin C. ;

West, Robert B. ;

Neal, Joel W. ;

Wakelee, Heather A. ;

Loo, Billy W., Jr. ;

Kunder, Christian A. ;

Leung, Ann N. ;

Lui, Natalie S. ;

Berry, Mark F. ;

Shrager, Joseph B. ;

Nair, Viswam S. ;

Haber, Daniel A. ;

Sequist, Lecia V. ;

Alizadeh, Ash A. ;

Diehn, Maximilian .

NATURE, 2020, 580 (7802) :245-+

[20]

Chen BB, 2018, METHODS MOL BIOL, V1711, P243, DOI 10.1007/978-1-4939-7493-1_12

← 1 2 3 4 5 6 7 8 9 10 →