scholarly journals Radiomics: from qualitative to quantitative imaging

2020 ◽  
Vol 93 (1108) ◽  
pp. 20190948 ◽  
Author(s):  
William Rogers ◽  
Sithin Thulasi Seetha ◽  
Turkey A. G. Refaee ◽  
Relinde I. Y. Lieverse ◽  
Renée W. Y. Granzier ◽  
...  
Keyword(s):  
Quantitative Imaging ◽  
Quantitative Information ◽  
Clinical Applications ◽  
Machine Learning Algorithms ◽  
Biological Information ◽  
Imaging Data ◽  
Multistage Process ◽  
Advantages And Disadvantages ◽  
Future Direction ◽  
Biological Outcomes

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms “handcrafted and deep,” is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Minimally Invasive Mini-orbitozygomatic Approach for Clipping an Anterior Communicating Artery Aneurysm: Virtual Reality Surgical Planning

2020 ◽  
Author(s):  
Nicolás González Romo ◽  
Franco Ravera Zunino
Keyword(s):  
Virtual Reality ◽  
Minimally Invasive ◽  
Surgical Planning ◽  
Artery Aneurysm ◽  
Patient Specific ◽  
Anterior Communicating Artery ◽  
Imaging Data ◽  
Advantages And Disadvantages ◽  
Orbitozygomatic Approach ◽  
Skull Base Anatomy

AbstractVirtual reality (VR) has increasingly been implemented in neurosurgical practice. A patient with an unruptured anterior communicating artery (AcoA) aneurysm was referred to our institution. Imaging data from computed tomography angiography (CTA) was used to create a patient specific 3D model of vascular and skull base anatomy, and then processed to a VR compatible environment. Minimally invasive approaches (mini-pterional, supraorbital and mini-orbitozygomatic) were simulated and assessed for adequate vascular exposure in VR. Using an eyebrow approach, a mini-orbitozygomatic approach was performed, with clip exclusion of the aneurysm from the circulation. The step-by-step process of VR planning is outlined, and the advantages and disadvantages for the neurosurgeon of this technology are reviewed.

Material Properties and Design Aspects of Folding Bicycle Frame

2011 ◽  
Vol 264-265 ◽  
pp. 777-782 ◽  
Author(s):  
M.A. Maleque ◽  
M.S. Hossain ◽  
S. Dyuti
Keyword(s):  
Material Properties ◽  
Raw Material ◽  
New Materials ◽  
Materials Properties ◽  
Advantages And Disadvantages ◽  
Bicycle Frame ◽  
Successful Design ◽  
Future Direction ◽  
The Relationship ◽  
Comprehensive Study

successful design of folding bicycle should take into account the function, material properties, and fabrication process. There are some other factors that should be considered in anticipating the behavior of materials for folding bicycle. In order to understand the relationship between material properties and design of a folding bicycle and also for the future direction in new materials with new design, a comprehensive study on the design under different conditions are essential. Therefore, a systematic study on the relationship between material properties and design for folding bicycle has been performed. The advantages and disadvantages matrix between conventional bicycle and folding bicycle is presented for better understanding of the materials properties and design. It was found that the materials properties of the folding bicycle frame such as fatigue and tensile strength are the important properties for the better performance of the frame. The relationship between materials properties and design is not straight forward because the behavior of the material in the finished product could be different from that of the raw material. The swing hinge technique could be a better technique in the design for the folding bicycle frame.

Reliability of the Multi-Dimensional Voice Program for the Analysis of Voice Samples of Subjects With Dysarthria

1999 ◽  
Vol 8 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Ray D. Kent ◽  
Houri K. Vorperian ◽  
Joseph R. Duffy
Keyword(s):  
Computer Program ◽  
Clinical Assessment ◽  
Quantitative Information ◽  
Speech Disorders ◽  
Clinical Applications ◽  
Automatic Analysis ◽  
Acoustic Parameters ◽  
Assessment And Treatment ◽  
Computer Based ◽  
Quantitative Assessments

Computer-based analysis systems are increasingly available for the clinical assessment of speech and voice functions. These systems have the potential to provide immediate quantitative information to assist clinical assessment and treatment. The Multi-Dimensional Voice Program (MDVP) is a computer program that can calculate as many as 33 acoustic parameters from a voice sample. The MDVP appears to have potential for rapid quantitative assessments of voice in both research and clinical applications. This report evaluates the robustness and reliability of MDVP for vocal analyses of 32 individuals with dysarthria of various etiologies. It is concluded that the reliability is generally very good and that MDVP has potential as a tool for the semi-automatic analysis of voice samples in dysarthria. Some parameters appear to hold particular value in the description of voice qualities in these speech disorders.

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

scholarly journals Spatial regulation of clathrin-mediated endocytosis through position-dependent site maturation

2019 ◽  
Author(s):  
Ross TA Pedersen ◽  
Julian E Hassinger ◽  
Paul Marchando ◽  
David G Drubin
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Transition Point ◽  
Quantitative Imaging ◽  
Imaging Data ◽  
Cortical Actin ◽  
Initiation Phase ◽  
Spatial Regulation ◽  
Polarized Cells ◽  
Endocytosis Pathway ◽  
Dependent Site

AbstractDuring clathrin-mediated endocytosis (CME), over 50 different proteins assemble on the plasma membrane to reshape it into a cargo-laden vesicle. It has long been assumed that cargo triggers local CME site assembly in Saccharomyces cerevisiae based on the discovery that cortical actin patches clustered near exocytic sites are CME sites. Quantitative imaging data reported here lead to a radically different view of which CME steps are regulated and which steps are deterministic. We quantitatively and spatially describe progression through the CME pathway and pinpoint a cargo-sensitive regulatory transition point that governs progression from the initiation phase of CME to the internalization phase. Thus, site maturation, rather than site initiation, accounts for the previously observed polarized distribution of actin patches in this organism. While previous studies suggested that cargo ensures its own internalization by regulating either CME initiation rates or frequency of abortive events, our data instead identify maturation through a checkpoint in the pathway as the cargo-sensitive step.SummaryPedersen, Hassinger, et al. investigate steps of the clathrin-mediated endocytosis pathway that are subject to regulation. They report position-dependent differences in endocytic site maturation rates in polarized cells and suggest that cargo controls endocytic internalization through tuning site maturation rather than site initiation.

scholarly journals Quantitative Imaging Informatics for Cancer Research

2020 ◽  
pp. 444-453 ◽  
Author(s):  
Andrey Fedorov ◽  
Reinhard Beichel ◽  
Jayashree Kalpathy-Cramer ◽  
David Clunie ◽  
Michael Onken ◽  
...  
Keyword(s):  
Cancer Research ◽  
Best Practices ◽  
Cancer Imaging ◽  
Quantitative Imaging ◽  
Imaging Informatics ◽  
Imaging Data ◽  
Communication Interface ◽  
3D Slicer ◽  
And Training ◽  
Data Commons

PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

2019 ◽  
Vol 24 (34) ◽  
pp. 3998-4006
Author(s):  
Shijie Fan ◽  
Yu Chen ◽  
Cheng Luo ◽  
Fanwang Meng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Precision Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Accelerated Studies ◽  
Applications Of Machine Learning

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

Study of Fault Diagnosis Based on Manifold Learning

2013 ◽  
Vol 312 ◽  
pp. 650-654 ◽  
Author(s):  
Yi Lin He ◽  
Guang Bin Wang ◽  
Fu Ze Xu
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Linear Manifold ◽  
Rotating Machinery ◽  
Dimensional Manifold ◽  
Advantages And Disadvantages ◽  
Non Linear ◽  
Future Direction ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis

Characteristic signals in rotating machinery fault diagnosis with the issues of complex and difficult to deal with, while the use of non-linear manifold learning method can effectively extract low-dimensional manifold characteristics embedded in the high-dimensional non-linear data. It greatly maintains the overall geometric structure of the signals and improves the efficiency and reliability of the rotating machinery fault diagnosis. According to the development prospects of manifold learning, this paper describes four classical manifold learning methods and each advantages and disadvantages. It reviews the research status and application of fault diagnosis based on manifold learning, as well as future direction of researches in the field of manifold learning fault diagnosis.

scholarly journals Sources and Clinical Applications of Mesenchymal Stem Cells: State-of-the-art review

2018 ◽  
Vol 18 (3) ◽  
pp. 264 ◽  
Author(s):  
Roberto Berebichez-Fridman ◽  
Pablo R. Montero-Olvera
Keyword(s):  
Tissue Engineering ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Regenerative Medicine ◽  
Adult Stem Cells ◽  
Current Knowledge ◽  
Clinical Applications ◽  
The Body ◽  
Differentiation Potential ◽  
Advantages And Disadvantages

First discovered by Friedenstein in 1976, mesenchymal stem cells (MSCs) are adult stem cells found throughout the body that share a fixed set of characteristics. Discovered initially in the bone marrow, this cell source is considered the gold standard for clinical research, although various other sources—including adipose tissue, dental pulp, mobilised peripheral blood and birth-derived tissues—have since been identified. Although similar, MSCs derived from different sources possess distinct characteristics, advantages and disadvantages, including their differentiation potential and proliferation capacity, which influence their applicability. Hence, they may be used for specific clinical applications in the fields of regenerative medicine and tissue engineering. This review article summarises current knowledge regarding the various sources, characteristics and therapeutic applications of MSCs.Keywords: Mesenchymal Stem Cells; Adult Stem Cells; Regenerative Medicine; Cell Differentiation; Tissue Engineering.

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