Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell–Renal-Cell-Carcinoma: Proof-of-Concept Study

Yin, Qinye; Hung, Sheng-Che; Wang, Li; Lin, Weili; Fielding, Julia R.; Rathmell, W. Kimryn; Khandani, Amir H.; Woods, Michael; Milowsky, Matthew I.; Brooks, Samira A.; Wallen, Eric; Shen, Dinggang

doi:10.1038/srep43356

Cited by 56 publications

(42 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Associations between tumor vascularity, VEGF expression and PET/MRI features in primary clear-cell-renal-cell-carcinoma have been discussed in Yin et al [32]. The authors reported the highest correlation of tumor microvascular density and PET/MRI features compared to PET or MRI features alone.…”

Section: Holomicsmentioning

confidence: 85%

“…Furthermore, holistic approaches that combine clinical and imaging information become more popular [30][31][32]. Current technological advances support the collection of large-scale, heterogeneous information from living organisms, not only by hybrid imaging, but also by genomics [31], proteomics [33], or histopathology [34].…”

Section: Machine Learning For Medical Big Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Holomicsmentioning

confidence: 85%

Section: Machine Learning For Medical Big Data Analysismentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Yin et al recently reported that textural features derived from the time-series modality DCE-MRI, which represents tumor spatiotemporal enhancement patterns (model-free parameters), of nine primary clear-cell-renal-cell carcinomas correlated significantly with MVD. 45 However, no further potential explanation was shown. Considering our pathological results, correlation between textural features of DCE-MRI parameter maps and pathological indices need to be verified in larger cohorts.…”

Section: Discussionmentioning

confidence: 98%

“…The potential explanation of these results could be the homogeneous appearance of DCE‐MRI between astrocytoma and oligodendroglioma/oligoastrocytoma. However, the 2016 WHO classification of central nervous system tumors has focused on the genetic subtypes of grade II and III gliomas, including 1p/19q co‐deletion and IDH1 mutation status, and a newly published study has successfully differentiated between genetic subtypes of gliomas using perfusion and diffusion MRI . These recent classification and study may explain why limited studies have successfully discriminated between astrocytoma and oligodendroglioma/oligoastrocytoma regardless of genetic statues.…”

Section: Discussionmentioning

confidence: 99%

“…However, our results indicated that significant and moderate correlations were found between cellular proliferation (Ki‐67 index), Entropy, and IDM of four DCE‐MRI parameter maps. Yin et al recently reported that textural features derived from the time‐series modality DCE‐MRI, which represents tumor spatiotemporal enhancement patterns (model‐free parameters), of nine primary clear‐cell‐renal‐cell carcinomas correlated significantly with MVD . However, no further potential explanation was shown.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation