Structural and functional radiomics for lung cancer.
Introduction: Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnos...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 12; pp. 3961 - 3975 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2021
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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=152744552&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152744552 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: Nov2021 vid: 48 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152744552 149166909 10.1007/s00259-021-05242-1 152744552 ppf: 3961 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Structural and functional radiomics for lung cancer. aug: au: Wu, Guangyao Jochems, Arthur Refaee, Turkey Ibrahim, Abdalla Yan, Chenggong Sanduleanu, Sebastian Woodruff, Henry C. Lambin, Philippe affil: The D-Lab, Department of Precision Medicine, GROW–School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands sug: ab: Introduction: Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods: Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion: The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics." pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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