Radiomics in PET/CT: More Than Meets the Eye?

Hatt, Mathieu; Tixier, Florent; Visvikis, Dimitris; Rest, Catherine Cheze Le

doi:10.2967/jnumed.116.184655

Cited by 91 publications

(58 citation statements)

References 25 publications

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“…Nevertheless, the true potential of DL is seen only in view of data having a complex, hierarchical structure. This is a challenging requirement to fulfill, and, thus, DL to date is underrepresented in the context of tumor characterization and hybrid imaging [62,63]. On the other hand, DL FIGURE 4 | Representation of Medical Big Data as the result of various information capturing systems and examinations.…”

Section: Technology Machine Learningmentioning

confidence: 99%

“…To date, in vivo disease characterization with hybrid imaging data-especially in the light of oncological applications-is performed mainly by analyzing engineered features [63,129]. This process is widely referred to as "Radiomics, " even though, this kind of approach was originally applied to morphological images only [8].…”

Section: Joint Data Explorationmentioning

confidence: 99%

“…State-of-the-art ML approaches and hybrid imaging appear to be synergistic partners [7,63,173]. There is increasing evidence for in vivo tissue characterization with both PET/CT and PET/MRI hybrid imaging [11,146,174,175].…”

Section: Radiomicsmentioning

confidence: 99%

“…Machine learning approaches operating over medical big data require a large amount of standardized data to generate accurate predictive models [10,63]. Nevertheless, access to standardized multi-center data in the field of hybrid imaging is a challenge [54,63] which necessitates multi-center standardization efforts [7].…”

Section: Standardizationmentioning

confidence: 99%

“…Nevertheless, access to standardized multi-center data in the field of hybrid imaging is a challenge [54,63] which necessitates multi-center standardization efforts [7]. Standardization of hybrid imaging techniques through patient preparation, imaging protocols as well as data evaluation is already of general interest in the field of medical imaging [22,28,55,[190][191][192][193].…”

Section: Standardizationmentioning

confidence: 99%

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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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Section: Technology Machine Learningmentioning

confidence: 99%

Section: Joint Data Explorationmentioning

confidence: 99%

Section: Radiomicsmentioning

confidence: 99%

Section: Standardizationmentioning

confidence: 99%

Section: Standardizationmentioning

confidence: 99%

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Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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A multi‐modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI

et al. 2022

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Background: Hybrid imaging (e.g., positron emission tomography [PET]/ computed tomography [CT],PET/magnetic resonance imaging [MRI]) helps one to visualize and quantify morphological and physiological tumor characteristics in a single study. The noninvasive characterization of tumor heterogeneity is essential for grading, treatment planning, and following-up oncological patients. However, conventional (CONV) image-based parameters, such as tumor diameter, tumor volume, and radiotracer activity uptake, are insufficient to describe tumor heterogeneities. Here, radiomics shows promise for a better characterization of tumors. Nevertheless, the validation of such methods demands imaging objects capable of reflecting heterogeneities in multi-modality imaging. We propose a phantom to simulate tumor heterogeneity repeatably in PET,CT,and MRI. Methods: The phantom consists of three 50-ml plastic tubes filled partially with acrylic spheres of S1: 1.6 mm, S2: 50%(1.6 mm)/50%(6.3 mm), or S3: 6.3-mm diameter. The spheres were fixed to the bottom of each tube by a plastic grid, yielding one sphere free homogeneous region and one heterogeneous (S1, S2, or S3) region per tube. A 3-tube phantom and its replica were filled with a fluorodeoxyglucose (18F) solution for test-retest measurements in a PET/CT Siemens TPTV and a PET/MR Siemens Biograph mMR system. A number of 42 radiomic features (10 first order and 32 texture features) were calculated for each phantom region and imaging modality. Radiomic features stability was evaluated through coefficients of variation (COV) across phantoms and scans for PET,CT,and MRI.Further,the Wilcoxon test was used to assess the capability of stable features to discriminate the simulated phantom regions. Results: The different patterns (S1-S3) did present visible heterogeneity in all imaging modalities. However, only for CT and MRI, a clear visual difference was present between the different patterns. Across all phantom regions in PET, CT, and MR images, 10, 16, and 21 features out of 42 evaluated features in total had a COV of 10% or less. In particular, CONV, histogram, and gray-level run length matrix features showed high repeatability for all the phantom regions and imaging modalities. Several of repeatable texture features allowed the image-based discrimination of the different phantom regions (p < 0.05). However, depending

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