The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging
Open accessAbstract
Tumour heterogeneity in cancers has been observed at the histological and genetic levels, and increased levels of intra-tumour genetic heterogeneity have been reported to be associated with adverse clinical outcomes. This review provides an overview of radiomics, radiogenomics, and habitat imaging, and examines the use of these newly emergent fields in assessing tumour heterogeneity and its implications. It reviews the potential value of radiomics and radiogenomics in assisting in the diagnosis of cancer disease and determining cancer aggressiveness. This review discusses how radiogenomic analysis can be further used to guide treatment therapy for individual tumours by predicting drug response and potential therapy resistance and examines its role in developing radiomics as biomarkers of oncological outcomes. Lastly, it provides an overview of the obstacles in these emergent fields today including reproducibility, need for validation, imaging analysis standardisation, data sharing and clinical translatability and offers potential solutions to these challenges towards the realisation of precision oncology.
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Citations
Cites
Radiomics: Images Are More than Pictures, They Are Data
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Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
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“Indeed, recent genomics work has highlighted the presence of intra-tumour genetic and transcriptomic variation (1, 3, 7, 38, 55, 56).”
BackgroundRadiomics: extracting more information from medical images using advanced feature analysis
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“Furthermore, when scientists at Amgen (Thousand Oaks, CA, USA) tried to replicate 53 landmark studies in the basic science of cancer, they were able to reproduce the original results of just six (41).”
BackgroundDistant metastasis occurs late during the genetic evolution of pancreatic cancer
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Radiomics: the process and the challenges
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“First, it requires standardisation of imaging protocols, including image acquisition and post-processing, as well as robust segmentation algorithms that require minimal operator input (13).”
BackgroundMethodInfluentialIntratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics
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Spatial and temporal diversity in genomic instability processes defines lung cancer evolution
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Cited by
Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., & Cook, G. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.222893 · Cited by 1,777
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D'Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 710
Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment
Pinker, K., Chin, J., Melsaether, A. N., Morris, E. A., & Moy, L. (2018) · Radiology · doi:10.1148/radiol.2018172171 · Cited by 275
Learning from scanners: Bias reduction and feature correction in radiomics
Zhovannik, I., Bussink, J., Traverso, A., Shi, Z., Kalendralis, P., Wee, L., Dekker, A., Fijten, R., & Monshouwer, R. (2019) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2019.07.003 · Cited by 75
Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma
Ahmed, A. A., Elmohr, M. M., Fuentes, D., Habra, M. A., Fisher, S. B., Perrier, N. D., Zhang, M., & Elsayes, K. M. (2020) · Clinical Radiology · doi:10.1016/j.crad.2020.01.012 · Cited by 41
Metadata
- Authors
- Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J. D., Snyder, A., Weigelt, B., & Vargas, H. A.
- Venue
- Clinical Radiology
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 72
- Issue
- 1
- Pages
- 3-10
- ISSN
- 0009-9260 (ISSN-L)
- OpenAlex
- W2530141817
- Cited by
- 369
- References
- 53
- FWCI
- 22.60
- Type
- journal article
- Language
- en
- Open access
- green
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- Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J. D., Snyder, A., Weigelt, B., & Vargas, H. A. (2017). Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clinical Radiology. https://doi.org/10.1016/j.crad.2016.09.013
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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