Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology

被引：35

作者：

Lancellotti, Cesare ^{[1
,2
]}

Cancian, Pierandrea ^{[3
]}

Savevski, Victor ^{[3
]}

Kotha, Soumya Rupa Reddy ^{[1
,2
]}

Fraggetta, Filippo ^{[4
]}

Graziano, Paolo ^{[5
]}

Di Tommaso, Luca ^{[1
,2
]}

机构：

[1] Humanitas Univ, Dept Biomed Sci, Via Rita Levi Montalcini 4, I-20090 Milan, Italy

[2] Humanitas Res Hosp, Pathol Unit, Via Manzoni 56, I-20089 Milan, Italy

[3] Humanitas Res Hosp, IRCCS, Artificial Intelligence Ctr, Via Manzoni 56, I-20089 Milan, Italy

[4] Cannizzaro Hosp, Dept Pathol, I-95021 Catania, Italy

[5] Fdn IRCCS Casa Sollievo Sofferenza, Pathol Unit, I-71013 San Giovanni Rotondo, FG, Italy

来源：

CELLS | 2021年 / 10卷 / 04期

关键词：

biomarker; artificial intelligence; pathology; personalized medicine; TUMOR-INFILTRATING LYMPHOCYTES; CONVOLUTIONAL NEURAL-NETWORKS; COMPUTER-EXTRACTED FEATURES; IMAGE-ANALYSIS; LUNG ADENOCARCINOMA; DIGITAL PATHOLOGY; PREDICT RESPONSE; PROSTATE-CANCER; DEEP; CLASSIFICATION;

D O I：

10.3390/cells10040787

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. In this review we will present how AI has been used to support Tissue Biomarkers evaluation in the specific field of Pathology, give an insight to the intriguing field of AI-based biomarkers and discuss possible advantages, risk and perspectives for Pathology.

引用

页数：12

共 73 条

[1] Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy [J].

Althammer, Sonja ;

Tan, Tze Heng ;

Spitzmuller, Andreas ;

Rognoni, Lorenz ;

Wiestler, Tobias ;

Herz, Thomas ;

Widmaier, Moritz ;

Rebelatto, Marlon C. ;

Kaplon, Helene ;

Damotte, Diane ;

Alifano, Marco ;

Hammond, Scott A. ;

Dieu-Nosjean, Marie-Caroline ;

Ranade, Koustubh ;

Schmidt, Guenter ;

Higgs, Brandon W. ;

Steele, Keith E. .

JOURNAL FOR IMMUNOTHERAPY OF CANCER, 2019, 7

[2] Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results [J].

Alvarez-Jimenez, Charlems ;

Sandino, Alvaro A. ;

Prasanna, Prateek ;

Gupta, Amit ;

Viswanath, Satish E. ;

Romero, Eduardo .

CANCERS, 2020, 12 (12) :1-17

[3] Computer-extracted features relating to spatial arrangement of tumor infiltrating lymphocytes to predict response to nivolumab in non-small cell lung cancer (NSCLC). [J].

Barrera, Cristian ;

Velu, Priya ;

Bera, Kaustav ;

Wang, Xiangxue ;

Prasanna, Prateek ;

Khunger, Monica ;

Khunger, Arjun ;

Velcheti, Vamsidhar ;

Romero, Eduardo ;

Madabhushi, Anant .

JOURNAL OF CLINICAL ONCOLOGY, 2018, 36 (15)

[4] Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival [J].

Beck, Andrew H. ;

Sangoi, Ankur R. ;

Leung, Samuel ;

Marinelli, Robert J. ;

Nielsen, Torsten O. ;

van de Vijver, Marc J. ;

West, Robert B. ;

van de Rijn, Matt ;

Koller, Daphne .

SCIENCE TRANSLATIONAL MEDICINE, 2011, 3 (108)

[5] Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer [J].

Bejnordi, Babak Ehteshami ;

Veta, Mitko ;

van Diest, Paul Johannes ;

van Ginneken, Bram ;

Karssemeijer, Nico ;

Litjens, Geert ;

van der Laak, Jeroen A. W. M. .

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2017, 318 (22) :2199-2210

[6] Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology [J].

Bera, Kaustav ;

Schalper, Kurt A. ;

Rimm, David L. ;

Velcheti, Vamsidhar ;

Madabhushi, Anant .

NATURE REVIEWS CLINICAL ONCOLOGY, 2019, 16 (11) :703-715

[7] 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

[8] 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

[9] Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers [J].

Calderaro, Julien ;

Kather, Jakob Nikolas .

GUT, 2021, 70 (06) :1183-1193

[10] 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-+

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