Imaging genomics in cancer research: limitations and promises

Bai, Harrison X.; Lee, Ashley M.; Yang, Li; Zhang, Paul; Davatzikos, Christos; Maris, John M.; Diskin, Sharon J.

doi:10.1259/bjr.20151030

Cited by 94 publications

(80 citation statements)

References 103 publications

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“…Biomarkers must accurately reflect the underlying molecular cancerous machinery (33, 34) and are measured by assays often requiring tissue analysis obtained through surgery or biopsy. The limitations of this approach include its invasive nature and the fact that samples are often obtained from only a portion of a generally heterogeneous lesion and cannot completely represent the lesion’s anatomical, functional, and pathological properties (35). Various groups have proposed that image-derived indices may reliably identify important subregions non-invasively and create predictive imaging biomarkers (36).…”

Section: Potential Value Of Radiomics and Radiogenomicsmentioning

confidence: 99%

“…Various groups have proposed that image-derived indices may reliably identify important subregions non-invasively and create predictive imaging biomarkers (36). These imaging biomarkers could then act as non-invasive measurements of functional and physiological processes in vivo and provide information on treatment planning, prognosis, and therapy response (35). For example, O’Connor et al 37 predicted colorectal cancer liver metastasis shrinkage following anti-angiogenic and cytotoxic therapy by using heterogeneity of tumour vascular enhancement as a prognostic/predictive biomarker; however, such an endeavour has proven to be difficult in radiogenomics, making implementation of predictive biomarkers more difficult than expected (38).…”

Section: Potential Value Of Radiomics and Radiogenomicsmentioning

confidence: 99%

“…As with any biomarker study, a retrospective radiomics investigation must be validated against a completely independent dataset, preferably from a different institution (12). In a thorough review by Bai et al ., 35 a validation dataset was used in only eight out of 27 studies. One problem that prevents retrospective validation studies is the availability of publicly available radiomics data, which creates the need for shared databases that can be used as validation sets.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Furthermore, these three features were associated with early disease recurrence and poor overall survival (53). Beyond diagnosis, radiogenomics can have a significant impact on treatment guidance and overall survival (35). In a study of clear cell renal cell carcinoma patients, Jamshidi et al 54 developed a prognostic multigene signature termed radiogenomic risk score consisting of four CT imaging features (tumour necrosis pattern, transition zone, tumour–parenchyma interaction, and tumour–parenchyma interface) to predict disease-specific survival, independent of disease stage, disease grade, and performance status.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

et al. 2017

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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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Section: Potential Value Of Radiomics and Radiogenomicsmentioning

confidence: 99%

Section: Potential Value Of Radiomics and Radiogenomicsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

et al. 2017

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“…Radiogenomics is a relatively new omics strategy that correlates imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics [4, 6, 9, 17, 26, 27, 32, 44]. …”

Section: Radiogenomics In Breast Cancermentioning

confidence: 99%

Imaging and the completion of the omics paradigm in breast cancer

et al. 2018

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Within the field of oncology, “omics” strategies—genomics, transcriptomics, proteomics, metabolomics—have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with “omics” data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology–pathology correlation from the anatomical–histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.

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Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics

Jin,

Qiao,

Zhao

et al. 2023

Cancer Medicine

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BackgroundTo investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC).MethodsThe region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co‐expression network analysis was used to construct co‐expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features.ResultsFour hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs.ConclusionsOSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.

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Imaging genomics in cancer research: limitations and promises

Cited by 94 publications

References 103 publications

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

Imaging and the completion of the omics paradigm in breast cancer

Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics

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