A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme

Li, Yongling; Bai, Hao; Chen, Yinsheng; Sun, Qinjun; Liu, Lei; Zhou, Sijie; Wang, Guoliang; Liang, Chaofeng; Li, Zhicheng

doi:10.1038/s41598-017-14753-7

Cited by 107 publications

(104 citation statements)

References 25 publications

(28 reference statements)

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…At the time neither the image quality nor the computational capacity was sufficient to operate with textural features. Thanks to recent advances in imaging and computational fields, several groups have investigated the potential of textural features in light of in vivo disease characterization [8,28,56,[148][149][150]. Routine clinical evaluation of PET images relies on the statistical analysis of Standardized Uptake Values (SUV) [151].…”

Section: Radiomicsmentioning

confidence: 99%

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: Radiomicsmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Owing to the imbalance of sample size between two groups, the AUC could better evaluate the comprehensive performance of the classifier for the differentiation task . Therefore, the subset consisting of 19 top‐ranked features was determined as the optimal subset …”

Section: Resultsmentioning

confidence: 99%

“…For instance, the gray scales of T 2 W and DW images were 1024 and 256, respectively. Prior to CM and RLM features extraction, gray scale discretization and normalization is needed . However, this process is directly related to image information preservation and diagnostic performance variation of features extracted.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis

Zhang

Tian

et al. 2018

Magnetic Resonance Imaging

105

View full text Add to dashboard Cite

Background Preoperative discrimination between nonmuscle‐invasive bladder carcinomas (NMIBC) and the muscle‐invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). Purpose To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. Study Type Retrospective, radiomics. Population Fifty‐four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W) and multi‐b‐value diffusion‐weighted (DW) sequences. Assessment A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM‐RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. Statistical Tests Chi‐square test and Student's t‐test were applied on clinical characteristics to analyze the significant differences between patient groups. Results Of the 1104 features, an optimal subset involving 19 features was selected from T2W and DW sequences, which outperformed the other two subsets selected from T2W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM‐RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. Data Conclusion The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2W sequence or DW sequence only. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489–1498.

show abstract

“…Together, semantic and computational feature classes are the basis of the new field of "radiomics," 7,8 defined as the "high-throughput extraction of quantitative features that results in the conversion of images into mineable data," 9,10 and feature prominently in what today is called quantitative imaging. [27][28][29][30] This includes both "conventional radiomics" (ie, machine computation of human-engineered image features) and artificial intelligence, or "deep learning," (DL) to automatically discover the most informative features for image phenotype description from the image and clinical data.…”

Section: Introductionmentioning

confidence: 99%

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Napel

Jardim‐Perassi

et al. 2018

Cancer

148

View full text Add to dashboard Cite

Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

show abstract

A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme

Cited by 107 publications

References 25 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Contact Info

Product

Resources

About