Technical Challenges in the Clinical Application of Radiomics

Shaikh, Faiq; Kolowitz, Brian J.; Awan, Omer A.; Aerts, Hugo J.W.L.; Reden, Anna von; Halabi, Safwan; Md, Sohaib A Mohiuddin; Malik, Sana; Shrestha, Rasu B.; Deible, Christopher

doi:10.1200/cci.17.00004

Cited by 27 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lung cancer is the most harmful cancer, and its prevalence continues to increase worldwide 129. Radiomics has been widely used in the diagnosis, treatment evaluation, and prognosis in lung cancer 130, 131.…”

Section: Applications Of Radiomics In Diagnosis and Treatment Of Oncomentioning

confidence: 99%

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

et al. 2019

View full text Add to dashboard Cite

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

show abstract

Section: Applications Of Radiomics In Diagnosis and Treatment Of Oncomentioning

confidence: 99%

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

et al. 2019

View full text Add to dashboard Cite

show abstract

“…While the field of radiogenomics holds great promise, there are a number of limitations that the field, as a whole, needs to overcome [184].…”

Section: Limitations and Challenges Facing Radiogenomicsmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

View full text Add to dashboard Cite

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

show abstract

“…Two different set of features were implemented to describe the information of the findings in each sequence, i.e., perceptual and radiomic features. Perceptual features refer to the properties of an image that can be captured by human perception [ 54 ], while radiomic features represent a quantitative approach to the analysis of medical images aimed at explaining the morphological and functional properties of a lesion [ 55 ].…”

Section: Experimental Setupmentioning

confidence: 99%

A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

Areiza-Laverde

Castro-Ospina

Hernández

et al. 2020

Sensors

View full text Add to dashboard Cite

Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.

show abstract

Technical Challenges in the Clinical Application of Radiomics

Cited by 27 publications

References 11 publications

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Radiogenomics: bridging imaging and genomics

A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

Contact Info

Product

Resources

About