NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures

Colen, Rivka R.; Foster, Ian; Gatenby, Robert A.; Giger, M.; Gillies, Robert J.; Gutman, David; Heller, Matthew T.; Jain, Rajan; Madabhushi, Anant; Madhavan, Subha; Napel, Sandy; Rao, Arvind; Saltz, Joel; Tatum, James L.; Verhaak, Roeland; Whitman, Gary J.

doi:10.1016/j.tranon.2014.07.007

Cited by 71 publications

(55 citation statements)

References 56 publications

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“…15,16 It is important to understand the difference between ‘radiomics’ and ‘radiogenomics’. 17 ‘Radiomics’ refers to the high-throughput extraction of quantitative features from images, i.e., conversion of images to mineable data, and subsequently using these data for decision support, including patient outcome. ‘Radiogenomics’ or ‘imaging genomics’ refers to the study of the associations between radiomic data (imaging features) and genomic patterns.…”

Section: Introductionmentioning

confidence: 99%

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

et al. 2016

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Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to ≤5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

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Section: Introductionmentioning

confidence: 99%

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

et al. 2016

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“…The National Cancer Institute has recently published a report that articulates approaches taken by the pathology and radiology research community to linking imaging phenotypes with large-scale genomic analyses. 22 Characterization of tissue morphology–related phenotypes is a complex effort that requires the development and deployment of pipelines consisting of image analysis, feature extraction, and machine learning algorithms. 23 A number of groups have developed tools designed to create, explore, and quantify rich morphologic and molecular characterizations of tissue samples at microscopic resolution and to incorporate other data sets in cancer analyses, including genomic and proteomic analyses.…”

Section: Key Conclusion: the Impact Of Computational Pathology On Rementioning

confidence: 99%

Computational Pathology: A Path Ahead

Louis

Feldman

Carter

et al. 2015

Archives of Pathology &Amp; Laboratory Medicine

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107

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Context We define the scope and needs within the new discipline of computational pathology, a discipline critical to the future of both the practice of pathology and, more broadly, medical practice in general. Objective To define the scope and needs of computational pathology. Data Sources A meeting was convened in Boston, Massachusetts, in July 2014 prior to the annual Association of Pathology Chairs meeting, and it was attended by a variety of pathologists, including individuals highly invested in pathology informatics as well as chairs of pathology departments. Conclusions The meeting made recommendations to promote computational pathology, including clearly defining the field and articulating its value propositions; asserting that the value propositions for health care systems must include means to incorporate robust computational approaches to implement data-driven methods that aid in guiding individual and population health care; leveraging computational pathology as a center for data interpretation in modern health care systems; stating that realizing the value proposition will require working with institutional administrations, other departments, and pathology colleagues; declaring that a robust pipeline should be fostered that trains and develops future computational pathologists, for those with both pathology and non-pathology backgrounds; and deciding that computational pathology should serve as a hub for data-related research in health care systems. The dissemination of these recommendations to pathology and bioinformatics departments should help facilitate the development of computational pathology.

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“…A relatively new field, “radiogenomics” or “imaging genomics” emerged as part of the hybrid, multidisciplinary initiative to leverage and correlate phenotypic and genotypic traits of biological disorders [3,37,38]. …”

Section: Genomics and Imaging Meta-analysismentioning

confidence: 99%

“RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment

Katrib¹,

Hsu²,

Bui³

et al. 2016

Quant. Biol.

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Recent advances in quantitative imaging and “omics” technology have generated a wealth of mineable biological “big data”. With the push towards a P4 “predictive, preventive, personalized, and participatory” approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose “radiotranscriptomics” as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

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NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures

Cited by 71 publications

References 56 publications

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Computational Pathology: A Path Ahead

“RADIOTRANSCRIPTOMICS”: A synergy of imaging and transcriptomics in clinical assessment

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