Radiomics in PET: principles and applications

Cook, Gary; Siddique, Musib; Taylor, Benjamin; Yip, Connie; Chicklore, Sugama

doi:10.1007/s40336-014-0064-0

Cited by 114 publications

(94 citation statements)

References 56 publications

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“…Limitations owing to spatial resolution, noise, motion artefacts, image acquisition parameters and reconstruction methods are expected to lead to a degradation of the extracted textural features and should be addressed. A recent review on texture analysis in PET by Cook et al 48 focussing on technical factors and clinical application of radiomics in PET, supports this conclusion.…”

Section: Texture Analysis In Positron Emission Tomographymentioning

confidence: 88%

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Alobaidli

McQuaid²,

South³

et al. 2014

BJR

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Cite this article as: Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014;87:20140369. REVIEW ARTICLEThe role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning ABSTRACTPredicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

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Section: Texture Analysis In Positron Emission Tomographymentioning

confidence: 88%

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Alobaidli

McQuaid²,

South³

et al. 2014

BJR

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“…There is increasing evidence for in vivo tissue characterization with both PET/CT and PET/MRI hybrid imaging [11,146,174,175]. In one study, PET, CT, and PET/CT features were used to predicting local tumor control in head and neck cancer [129] by multivariate cox regression with a confidence interval (CI) CI CT and CI PET/CT of 0.73, however, CT-based radiomics overestimated the probability of tumor control in the poor prognostic groups.…”

Section: Radiomicsmentioning

confidence: 99%

“…Hybrid imaging can help describe the overall heterogeneity of tumors on both morphological and metabolic levels [10,11]. Therefore, hybrid imaging appears to be a key technology to build accurate, in vivo tumor characterization models [7].…”

Section: Introduction Hybrid Imagingmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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“…Thus, previous studies have investigated the associations between these mutations and SUV measures from PET images; however, there have been conflicting findings (18)(19)(20)(21)(22). These inconsistent reports may be due to the fact that simple SUV measures fail to capture the spatial relationships between image voxels, which may be more informative of the biology of these mutations and describe the degree of tumor heterogeneity (23,24). Highly heterogeneous tumors are often NOS 5 not otherwise specified.…”

mentioning

confidence: 99%

“…Therefore, accurate quantification of tumor heterogeneity from PET images may provide important information for the identification of mutation status and precision medicine. Heterogeneity in the tumor phenotype can be quantitatively described through radiomic features (23,27,28), which use advanced mathematic models to quantify the spatial relationship between image voxels (29).…”

mentioning

confidence: 99%

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

et al. 2016

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PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18 F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18 F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR1] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC 5 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDR Wilcoxon , FDR Noether ) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDR Wilcoxon 5 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR1 vs. EGFR-negative, AUC 5 0.67, FDR Noether 5 0.0032), as well as differentiating between KRAS-positive and EGFR1 (AUC 5 0.65, FDR Noether 5 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC 5 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

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Radiomics in PET: principles and applications

Cited by 114 publications

References 56 publications

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

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