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Deep learning ident...
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Bychkov, DUniv Helsinki, Nord EMBL Partnership Mol Med, Inst Mol Med Finland FIMM, Helsinki, Finland.;iCAN Digital Precis Canc Med Flagship, Helsinki, Finland.
(författare)
Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy
- Artikel/kapitelEngelska2021
Förlag, utgivningsår, omfång ...
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2021-02-17
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Springer Science and Business Media LLC,2021
Nummerbeteckningar
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LIBRIS-ID:oai:prod.swepub.kib.ki.se:146101593
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http://kipublications.ki.se/Default.aspx?queryparsed=id:146101593URI
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https://doi.org/10.1038/s41598-021-83102-6DOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440475URI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:art swepub-publicationtype
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The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin–eosin (H&E)-stained formalin-fixed primary breast tumor tissue sections, weakly supervised by ERBB2 gene amplification status. The gene amplification was determined by chromogenic in situ hybridization (CISH). The training data comprised digitized tissue microarray (TMA) samples from 1,047 patients. The correlation between the deep learning–predicted ERBB2 status, which we call H&E-ERBB2 score, and distant disease-free survival (DDFS) was investigated on a fully independent test set, which included whole-slide tumor images from 712 patients with trastuzumab treatment status available. The area under the receiver operating characteristic curve (AUC) in predicting gene amplification in the test sets was 0.70 (95% CI, 0.63–0.77) on 354 TMA samples and 0.67 (95% CI, 0.62–0.71) on 712 whole-slide images. Among patients with ERBB2-positive cancer treated with trastuzumab, those with a higher than the median morphology–based H&E-ERBB2 score derived from machine learning had more favorable DDFS than those with a lower score (hazard ratio [HR] 0.37; 95% CI, 0.15–0.93; P = 0.034). A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.
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Biuppslag (personer, institutioner, konferenser, titlar ...)
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Linder, NinaUppsala universitet,Internationell barnhälsa och nutrition,Univ Helsinki, Nord EMBL Partnership Mol Med, Inst Mol Med Finland FIMM, Helsinki, Finland.;iCAN Digital Precis Canc Med Flagship, Helsinki, Finland.(Swepub:uu)ninli617
(författare)
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Tiulpin, AUniv Oulu, Res Unit Med Imaging, Phys & Technol, Oulu, Finland.;Oulu Univ Hosp, Dept Diagnost Radiol, Oulu, Finland.;Ailean Technol Oy, Oulu, Finland.
(författare)
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Kucukel, HUniv Helsinki, Nord EMBL Partnership Mol Med, Inst Mol Med Finland FIMM, Helsinki, Finland.;iCAN Digital Precis Canc Med Flagship, Helsinki, Finland.
(författare)
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Lundin, MUniv Helsinki, Nord EMBL Partnership Mol Med, Inst Mol Med Finland FIMM, Helsinki, Finland.
(författare)
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Nordling, SUniv Helsinki, Dept Pathol, Medicum, Helsinki, Finland.
(författare)
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Sihto, HUniv Helsinki, Dept Pathol, Medicum, Helsinki, Finland.
(författare)
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Isola, JUniv Tampere, Dept Canc Biol, BioMediTech, Tampere, Finland.
(författare)
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Lehtimaki, THelsinki Univ Hosp, Helsinki, Finland.
(författare)
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Kellokumpu-Lehtinen, PLTampere Univ Hosp, Dept Oncol, Tampere, Finland.
(författare)
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von Smitten, KEira Hosp, Helsinki, Finland.
(författare)
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Joensuu, HiCAN Digital Precis Canc Med Flagship, Helsinki, Finland.;Helsinki Univ Hosp, Dept Oncol, Helsinki, Finland.;Univ Helsinki, Helsinki, Finland.
(författare)
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Lundin, JKarolinska Institutet,Univ Helsinki, Nord EMBL Partnership Mol Med, Inst Mol Med Finland FIMM, Helsinki, Finland.;iCAN Digital Precis Canc Med Flagship, Helsinki, Finland.;Karolinska Inst, Dept Global Publ Hlth, Stockholm, Sweden.
(författare)
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Univ Helsinki, Nord EMBL Partnership Mol Med, Inst Mol Med Finland FIMM, Helsinki, Finland.;iCAN Digital Precis Canc Med Flagship, Helsinki, Finland.Internationell barnhälsa och nutrition
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Scientific reports: Springer Science and Business Media LLC11:1, s. 4037-2045-2322
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- Av författaren/redakt...
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Bychkov, D
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Linder, Nina
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Tiulpin, A
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Kucukel, H
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Lundin, M
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Nordling, S
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visa fler...
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Sihto, H
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Isola, J
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Lehtimaki, T
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Kellokumpu-Lehti ...
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von Smitten, K
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Joensuu, H
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Lundin, J
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Karolinska Institutet
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