What can artificial intelligence teach us about the molecular mechanisms underlying disease?
While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumou...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 46; no. 13; pp. 2715 - 2722 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139867251&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139867251 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Dec2019 vid: 46 iid: 13 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139867251 143957286 10.1007/s00259-019-04370-z 139867251 ppf: 2715 ppct: 7 formats: fmt: @attributes: type: P tig: atl: What can artificial intelligence teach us about the molecular mechanisms underlying disease? aug: au: Cook, Gary J. R. Goh, Vicky affil: Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, SE1 7EH, London, UK sug: ab: While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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