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Artificial Intelligence for Personalised Medicine

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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 (1 August 2022) | Viewed by 17515

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Special Issue Editors

Assoc. Prof. Ahmad Chaddad

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Guest Editor

School of Artificial Intelligence, GUET, Guilin, China
Project Director, LDI, McGill University, Montreal, QC, Canada
Interests: radiomics and radiogenomics; artificial intelligence; biomedical imaging

Prof. Tamim Niazi

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Co-Guest Editor

Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
Interests: artificial intelligence and image-guided radiotherapy

Prof. Dr. Christian Desrosiers

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Co-Guest Editor

Laboratory for Imagery Vision and Artificial Intelligence, Ecole de Technologie Superieure, Montreal, QC H3C 1K3, Canada
Interests: computer vision; biomedical imaging and artificial intelligence

Special Issue Information

Dear Colleagues,

We are inviting manuscript submissions to our Special Issue on Artificial Intelligence for Personalised Medicine.

Radiomics uses artificial intelligence (AI) to analyze medical imaging for details and clinical inferences beyond what radiologists can appreciate through reading a scan. Specifically, radiomic analysis is a noninvasive technique for characterizing tumor lesions (or structure abnormalities) in terms of image texture signatures. It can be integrated into almost any clinical study involving image data and thus has the potential to become an essential tool for diagnosis, treatment, and follow-up of patient diseases. Specifically, radiomics combined with administrative health data are steadily growing for solving clinical issues in the applications for tumor grading, prognosis, and genomic profiling.

This Special Issue aims to present advances and original radiomic models for personalised medicine. It will be an opportunity to exchange the results of research and of new developed techniques in this biomedical research field with promising prospects.

Dr. Ahmad Chaddad
Prof. Tamim Niazi
Prof. Christian Desrosiers

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Radiomics and radiogenomics
Segmentation and classification
Data-driven biomarker discovery for disease detection
Deep learning models for medical image analysis
Image registration and fusion

Published Papers (5 papers)

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Research

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18 pages, 5927 KiB

Open AccessArticle

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

by Ludovic Venet, Sarthak Pati, Michael D. Feldman, MacLean P. Nasrallah, Paul Yushkevich and Spyridon Bakas

Appl. Sci. 2021, 11(4), 1892; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041892 - 21 Feb 2021

Cited by 8 | Viewed by 2989

Abstract

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)

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14 pages, 3042 KiB

Open AccessArticle

Multimodal Ensemble-Based Segmentation of White Matter Lesions and Analysis of Their Differential Characteristics across Major Brain Regions

by Saima Rathore, Tamim Niazi, Muhammad Aksam Iftikhar, Ashish Singh, Batool Rathore, Michel Bilello and Ahmad Chaddad

Appl. Sci. 2020, 10(6), 1903; https://0-doi-org.brum.beds.ac.uk/10.3390/app10061903 - 11 Mar 2020

Cited by 1 | Viewed by 2412

Abstract

White matter lesions (WML) are common in a variety of brain pathologies, including ischemia affecting blood vessels deeper inside the brain’s white matter, and show an abnormal signal in T1-weighted and FLAIR images. The emergence of personalized medicine requires quantification and analysis of differential characteristics of WML across different brain regions. Manual segmentation and analysis of WMLs is laborious and time-consuming; therefore, automated methods providing robust, reproducible, and fast WML segmentation and analysis are highly desirable. In this study, we tackled the segmentation problem as a voxel-based classification problem. We developed an ensemble of different classification models, including six models of support vector machine, trained on handcrafted and transfer learning features, and five models of Residual neural network, trained on varying window sizes. The output of these models was combined through majority-voting. A series of image processing operations was applied to remove false positives in a post-processing step. Moreover, images were mapped to a standard atlas template to quantify the spatial distribution of WMLs, and a radiomic analysis of all the lesions across different brain regions was carried out. The performance of the method on multi-institutional WML Segmentation Challenge dataset (n = 150) comprising T1-weighted and FLAIR images was >90% within data of each institution, multi-institutional data pooled together, and across-institution training–testing. Forty-five percent of lesions were found in the temporal lobe of the brain, and these lesions were easier to segment (95.67%) compared to lesions in other brain regions. Lesions in different brain regions were characterized by their differential characteristics of signal strength, size/shape, heterogeneity, and texture (p < 0.001). The proposed multimodal ensemble-based segmentation of WML showed effective performance across all scanners. Further, the radiomic characteristics of WMLs of different brain regions provide an in vivo portrait of phenotypic heterogeneity in WMLs, which points to the need for precision diagnostics and personalized treatment. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)

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21 pages, 11652 KiB

Open AccessArticle

Image Magnification Based on Bicubic Approximation with Edge as Constraint

by Linlin Ji, Rui Zhang, Huijian Han and Ahmad Chaddad

Appl. Sci. 2020, 10(5), 1865; https://0-doi-org.brum.beds.ac.uk/10.3390/app10051865 - 09 Mar 2020

Cited by 2 | Viewed by 2580

Abstract

Image magnification can be reduced to construct an approximation surface with data points in the image while keeping image details and edge features. In this paper, a new image magnification method is proposed by constructing piecewise bicubic polynomial surfaces constrained by edge features. The main innovation includes three parts. First, on the small adjacent area of each pixel, the new method constructs a quadratic polynomial sampling patch to approximate pixels on the small neighborhood with edge features as constraints. All quadric polynomial sampling patches are weighted to generate piecewise whole bicubic polynomial sampling surface. Second, a technique for calculating the error image is proposed: the error image is used to construct a correction surface to improve the accuracy and visual effect of the magnified image. Finally, in order to improve the accuracy of the approximation surface, a technology of balancing polynomial coefficients is put forward. Experimental results show that, compared with other methods, the proposed method makes better use of the local feature information of the image, which not only improves the PSNR/SSIM numerical accuracy of the magnified image but also improves the visual effect of the magnified image. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)

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Review

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25 pages, 1088 KiB

Open AccessReview

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

by Ronan Thenault, Kevin Kaulanjan, Thomas Darde, Nathalie Rioux-Leclercq, Karim Bensalah, Marie Mermier, Zine-eddine Khene, Benoit Peyronnet, Shahrokh Shariat, Benjamin Pradère and Romain Mathieu

Appl. Sci. 2020, 10(18), 6428; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186428 - 15 Sep 2020

Cited by 11 | Viewed by 4518

Abstract

Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)

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11 pages, 719 KiB

Open AccessFeature PaperReview

Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

by Nicolas Giraud, Paul Sargos, Nicolas Leduc, Olivier Saut, Te Vuong and Veronique Vendrely

Appl. Sci. 2020, 10(6), 1988; https://0-doi-org.brum.beds.ac.uk/10.3390/app10061988 - 14 Mar 2020

Cited by 3 | Viewed by 3640

Abstract

Anal squamous cell carcinoma (ASCC) is an uncommon yet rising cancer worldwide. Definitive chemo-radiation (CRT) remains the best curative treatment option for non-metastatic cases in terms of local control, recurrence-free and progression-free survival. Still, despite overall good results, with 80% five-year survival, a subgroup of ASCC patients displays a high level of locoregional and/or metastatic recurrence rates, up to 35%, and may benefit from a more aggressive strategy. Beyond initial staging, there is no reliable marker to predict recurrence following CRT. Imaging, mostly positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI), bears an important role in the diagnosis and follow-up of ASCC. The routine use of radiomics may enhance the quality of information derived from these modalities. It is thought that including data derived from radiomics into the input flow of machine learning algorithms may improve the prediction of recurrence. Although some studies have shown glimmers of hope, more data is needed before offering practitioners tools to identify high-risk patients and enable extensive clinical application, especially regarding the matters of imaging normalization, radiomics process standardization and access to larger patient databases with external validation in order to allow results extrapolation. The aim of this review is to present a critical overview from this data. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)

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