Quantitative Image Analysis for Radiological Image Understanding

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 2681

Special Issue Editors

Computer Graphics Center & Centro ALGORITMI, University of Minho, Campus de Azurem No. 14, 4800-058 Guimarães, Portugal
Interests: digital image processing; computer vision; pattern recognition; machine (deep) learning; data analytics; artificial intelligence; biomedical image and data analysis
Special Issues, Collections and Topics in MDPI journals
German Research Centre for Artificial Intelligence (DFKI GmbH), Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Interests: computer vision; artificial intelligence; machine learning, digital image processing; biomedical image analysis
Institute of Instrumentation for Molecular Imaging (I3M), Universitat Politècnica de València, Camino de Vera s/n, 46015 Valencia, Spain
Interests: cloud computing; high-performance computing; container-based applications

Special Issue Information

Dear Colleagues,

Biomedical image analysis is able to characterize diseases multi-parametrically. It is therefore a key component in modern frameworks and platforms, enabling innovative concepts with regard to precision imaging procedures and thus supporting tasks in precision medicine. As a main approach of the new paradigm in radiology, "quantitative imaging" aims at extracting measurable features from medical images to characterize status, severity, or degree of change of a disease, injury, or chronic condition.

Thanks to the great success of computer vision and artificial intelligence areas in the last two decades, and supported by recent developments in high-performance computing and cloud computing infrastructures, it is now possible to massively and systematically explore the enormous amounts of images and associated metadata (i.e., multiparametric data) captured by radiologists and physicians in clinical routine to identify objectively measurable features. With this, it is possible to identify patient-specific image features that have so far been withheld from evaluation, paving the way to the emergent field of quantitative radiomics. Radiomics extracts features from medical images that quantify phenotypic characteristics in an automated, high-throughput manner. Radiomics features have been demonstrated to be useful in several medical image analysis scenarios, e.g., assessing malignancy in cancer research or detecting Alzheimer’s disease in neuroscience.

The aim of this Special Issue is to provide researchers with a forum to present their original and disruptive research in the field of quantitative imaging for improving the understanding of radiological images and help to radiologists/physicians in the early detection (screening and early diagnosis) of diseases.

Prof. Dr. Miguel Angel Guevara Lopez
Dr. Gerd Reis
Prof. Dr. Ignacio Blanquer Espert
Guest Editors

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Keywords

  • biomedical image and data analysis
  • quantitative medical imaging
  • radiological image
  • pattern recognition
  • machine (deep) learning
  • data analysis
  • artificial intelligence
  • cloud computing and high-performance computing applied to precision imaging and precision medicine

Published Papers (1 paper)

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Research

9 pages, 1197 KiB  
Article
Establishing Correlations between Breast Tumor Response to Radio-Immunotherapy and Radiomics from Multi-Parametric Imaging: An Animal Study
by Anis Ahmad, Tulasigeri M. Totiger, Ana Paula Benaduce, Brian Marples and Ivaylo Bodganov Mihaylov
Appl. Sci. 2020, 10(18), 6493; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186493 - 17 Sep 2020
Cited by 3 | Viewed by 2090
Abstract
Triple-negative breast cancer (TNBC), which is a type of invasive breast cancer, is characterized by severe disease progression, poor prognosis, high recurrence rate, and short survival. We sought to gain new insight into TNBC by applying computed tomography (CT) and magnetic resonance (MR) [...] Read more.
Triple-negative breast cancer (TNBC), which is a type of invasive breast cancer, is characterized by severe disease progression, poor prognosis, high recurrence rate, and short survival. We sought to gain new insight into TNBC by applying computed tomography (CT) and magnetic resonance (MR) quantitative imaging (radiomics) approaches to predict the outcome of radio-immunotherapy treatments in a syngeneic subcutaneous murine breast tumor model. Five Athymic Nude mice were implanted with breast cancer cell lines (4T1) tumors on the right flank. The animals were CT- and MRI-imaged, tumors were contoured, and radiomics features were extracted. All animals were treated with radiotherapy (RT), followed by the administration of PD1 inhibitor. Approximately 10 days later, the animals were sacrificed, tumor volumes were measured, and histopathology evaluation was performed through Ki-67 staining. Linear regression modeling between radiomics and Ki-67 results was performed to establish a correlation between quantitative imaging and post-treatment histochemistry. There was no correlation between tumor volumes and Ki-67 values. Multiple CT- and MRI-derived features, however, correlated with histopathology with correlation coefficients greater than 0.8. MRI imaging helps in tumor delineation as well as an additional orthogonal imaging modality for quantitative imaging purposes. This is the first investigation correlating simultaneously CT- and MRI-derived radiomics to histopathology outcomes of combined radio-immunotherapy treatments in a preclinical setting applied to treatment naïve tumors. The findings indicate that imaging can guide discrimination between responding and non-responding tumors for the combined RT and ImT treatment regimen in TNBC. Full article
(This article belongs to the Special Issue Quantitative Image Analysis for Radiological Image Understanding)
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