Deep learning transforms colorectal cancer biomarker prediction from histopathology images

Pekka Ruusuvuori; Mira Valkonen; Leena Latonen

doi:10.1016/j.ccell.2023.08.006

Deep learning transforms colorectal cancer biomarker prediction from histopathology images

Pekka Ruusuvuori, Mira Valkonen, Leena Latonen

Research output: Contribution to journal › Comment/debate › Scientific

4 Citations (Scopus)

2 Downloads (Pure)

Abstract

Artificial intelligence (AI) is rapidly gaining interest in medicine, including pathological assessments for personalized medicine. In this issue of Cancer Cell, Wagner et al. demonstrate superior accuracy of transformer-based deep learning in predicting biomarker status in CRC. The work has implications for increased efficiency and accuracy in clinical diagnostics guiding treatment decisions in precision oncology.

Original language	English
Pages (from-to)	1543-1545
Number of pages	3
Journal	Cancer Cell
Volume	41
Issue number	9
DOIs	https://doi.org/10.1016/j.ccell.2023.08.006
Publication status	Published - 11 Sept 2023
Publication type	B1 Journal article

ASJC Scopus subject areas

Oncology
Cancer Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.ccell.2023.08.006

Deep learning transformsAccepted author manuscript, 308 KBLicence: CC BY-NC-ND

https://urn.fi/URN:NBN:fi:tuni-202411019775Licence: CC BY-NC-ND

Cite this

@article{5aeea8a6cb8e46f9a23a576c8ed5e866,

title = "Deep learning transforms colorectal cancer biomarker prediction from histopathology images",

abstract = "Artificial intelligence (AI) is rapidly gaining interest in medicine, including pathological assessments for personalized medicine. In this issue of Cancer Cell, Wagner et al. demonstrate superior accuracy of transformer-based deep learning in predicting biomarker status in CRC. The work has implications for increased efficiency and accuracy in clinical diagnostics guiding treatment decisions in precision oncology.",

author = "Pekka Ruusuvuori and Mira Valkonen and Leena Latonen",

note = "Funding Information: This work was financially supported by Research Council of Finland (# 341967 # 334782 P.R. and # 334774 L.L.), ERAPerMed ABCAP (P.R., L.L.), and Cancer Foundation Finland (P.R., L.L.). Publisher Copyright: {\textcopyright} 2023 Elsevier Inc.",

year = "2023",

month = sep,

day = "11",

doi = "10.1016/j.ccell.2023.08.006",

language = "English",

volume = "41",

pages = "1543--1545",

journal = "Cancer Cell",

issn = "1535-6108",

publisher = "Cell Press",

number = "9",

}

TY - JOUR

T1 - Deep learning transforms colorectal cancer biomarker prediction from histopathology images

AU - Ruusuvuori, Pekka

AU - Valkonen, Mira

AU - Latonen, Leena

N1 - Funding Information: This work was financially supported by Research Council of Finland (# 341967 # 334782 P.R. and # 334774 L.L.), ERAPerMed ABCAP (P.R., L.L.), and Cancer Foundation Finland (P.R., L.L.). Publisher Copyright: © 2023 Elsevier Inc.

PY - 2023/9/11

Y1 - 2023/9/11

N2 - Artificial intelligence (AI) is rapidly gaining interest in medicine, including pathological assessments for personalized medicine. In this issue of Cancer Cell, Wagner et al. demonstrate superior accuracy of transformer-based deep learning in predicting biomarker status in CRC. The work has implications for increased efficiency and accuracy in clinical diagnostics guiding treatment decisions in precision oncology.

AB - Artificial intelligence (AI) is rapidly gaining interest in medicine, including pathological assessments for personalized medicine. In this issue of Cancer Cell, Wagner et al. demonstrate superior accuracy of transformer-based deep learning in predicting biomarker status in CRC. The work has implications for increased efficiency and accuracy in clinical diagnostics guiding treatment decisions in precision oncology.

U2 - 10.1016/j.ccell.2023.08.006

DO - 10.1016/j.ccell.2023.08.006

M3 - Comment/debate

C2 - 37652005

AN - SCOPUS:85169512694

SN - 1535-6108

VL - 41

SP - 1543

EP - 1545

JO - Cancer Cell

JF - Cancer Cell

IS - 9

ER -

Deep learning transforms colorectal cancer biomarker prediction from histopathology images

Abstract

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this