Sökning: WFRF:(Wählby Carolina 1974 )
> (2020-2024) >
Morphological Featu...
Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer
-
- Chelebian, Eduard (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab,Avdelningen för visuell information och interaktion
-
- Avenel, Christophe (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Science for Life Laboratory, SciLifeLab,Bildanalys och människa-datorinteraktion
-
- Kartasalo, Kimmo (författare)
- Karolinska Institutet
-
visa fler...
-
- Marklund, Maja (författare)
- KTH,Genteknologi,Science for Life Laboratory, SciLifeLab
-
- Tanoglidi, Anna (författare)
- Uppsala Univ Hosp, Dept Clin Pathol, S-75237 Uppsala, Sweden.
-
- Mirtti, Tuomas (författare)
- Univ Helsinki, Helsinki Univ Hosp, Dept Pathol, Helsinki 00100, Finland.;Univ Helsinki, Helsinki Univ Hosp, Res Program Syst Oncol, Helsinki 00100, Finland.
-
- Colling, Richard (författare)
- Univ Oxford, Nuffield Dept Surg Sci, Oxford OX3 7DQ, England.;Oxford Univ Hosp NHS Fdn Trust, Dept Cellular Pathol, Oxford OX3 9DU, England.
-
- Erickson, Andrew (författare)
- Univ Oxford, Nuffield Dept Surg Sci, Oxford OX3 7DQ, England.
-
- Lamb, Alastair D. (författare)
- Univ Oxford, Nuffield Dept Surg Sci, Oxford OX3 7DQ, England.;Oxford Univ Hosp NHS Fdn Trust, Dept Urol, Oxford OX3 7LE, England.
-
- Lundeberg, Joakim (författare)
- KTH,Science for Life Laboratory, SciLifeLab,Genteknologi
-
- Wählby, Carolina, professor, 1974- (författare)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Science for Life Laboratory, SciLifeLab,Bildanalys och människa-datorinteraktion
-
visa färre...
-
(creator_code:org_t)
- 2021-09-28
- 2021
- Engelska.
-
Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:19
- Relaterad länk:
-
https://www.mdpi.com...
-
visa fler...
-
https://doi.org/10.3...
-
https://uu.diva-port... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
https://urn.kb.se/re...
-
http://kipublication...
-
visa färre...
Abstract
Ämnesord
Stäng
- Simple Summary Prostate cancer has very varied appearances when examined under the microscope, and it is difficult to distinguish clinically significant cancer from indolent disease. In this study, we use computer analyses inspired by neurons, so-called 'neural networks', to gain new insights into the connection between how tissue looks and underlying genes which program the function of prostate cells. Neural networks are 'trained' to carry out specific tasks, and training requires large numbers of training examples. Here, we show that a network pre-trained on different data can still identify biologically meaningful regions, without the need for additional training. The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes. This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions. Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- prostate cancer
- morphological features
- spatial transcriptomics
- deep learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
-
Cancers
(Sök värdpublikationen i LIBRIS)
Till lärosätets databas
- Av författaren/redakt...
-
Chelebian, Eduar ...
-
Avenel, Christop ...
-
Kartasalo, Kimmo
-
Marklund, Maja
-
Tanoglidi, Anna
-
Mirtti, Tuomas
-
visa fler...
-
Colling, Richard
-
Erickson, Andrew
-
Lamb, Alastair D ...
-
Lundeberg, Joaki ...
-
Wählby, Carolina ...
-
visa färre...
- Om ämnet
-
- NATURVETENSKAP
-
NATURVETENSKAP
-
och Data och informa ...
-
och Datavetenskap
-
- TEKNIK OCH TEKNOLOGIER
-
TEKNIK OCH TEKNO ...
-
och Medicinteknik
-
och Medicinsk bildbe ...
-
- MEDICIN OCH HÄLSOVETENSKAP
-
MEDICIN OCH HÄLS ...
-
och Klinisk medicin
-
och Radiologi och bi ...
-
- MEDICIN OCH HÄLSOVETENSKAP
-
MEDICIN OCH HÄLS ...
-
och Klinisk medicin
-
och Cancer och onkol ...
- Artiklar i publikationen
-
Cancers
- Av lärosätet
-
Kungliga Tekniska Högskolan
-
Uppsala universitet
-
Karolinska Institutet