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Artificial Intelligence and Radiomics in Computer-Aided Diagnosis

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9525

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Special Issue Editors

Dr. Cecilia Di Ruberto

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Guest Editor

Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy
Interests: computer vision; image retrieval; biomedical image analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Loddo

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Guest Editor

Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: computer vision; image processing; machine learning; deep learning; artificial intelligence; medical image analysis; biomedical image analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Lorenzo Putzu

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy
Interests: computer vision; medical image analysis; shape analysis and matching; image retrieval and classification
Special Issues, Collections and Topics in MDPI journals

Dr. Albert Comelli

E-Mail Website
Guest Editor

1. Ri.MED Foundation, via Bandiera 11, 90133 Palermo, Italy
2. Research Affiliate Long Term, Laboratory of Computational Computer Vision (LCCV), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Interests: biomedical image processing and analysis; radiomics; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Alessandro Stefano

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Guest Editor

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
Interests: non-invasive imaging techniques: positron emission tomography (PET), computerized tomography (CT), and magnetic resonance (MR); radiomics and artificial intelligence in clinical health care applications; processing, quantification, and correction methods for ex vivo and in vivo medical images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, healthcare systems collect and provide most medical data in digital form. The availability of medical data enables a large number of artificial intelligence applications, and there is a growing interest in the quantitative analysis of clinical images using techniques such as positron emission tomography, computerized tomography, and magnetic resonance imaging mainly applied to texture analysis and radiomics. In particular, thanks to machine and deep learning, researchers can generate insights to improve the discovery of new therapeutic tools, support diagnostic decisions, aid in the rehabilitation process, etc. However, the increasing amount of available data may lead to a more significant effort to make a diagnosis. Moreover, this task is even more challenging due to the high inter/intra patient variability, the availability of various imaging techniques, and the need to consider data from multiple sensors and sources.

To address the problems described, radiologists and pathologists today use tools to assist them in analysing biomedical images. They are known as computer-aided diagnosis (CAD) systems and they allow us to mitigate or eliminate the difficulties caused by inter- and intra-observer variability, represented by various assessments of the same region under the same assumptions by the same physician at different times and various assessments of the same region by several physicians, thanks to appropriate algorithms. This Special Issue aims to provide an overview of recent advances in the field of biomedical image processing in medical imaging using machine learning, deep learning, artificial intelligence, and radiomics features. In particular, the ultimate goal is to analyse how these techniques can be employed in the typical medical image processing workflow from image acquisition to classification, including retrieval, disease detection, prediction, and classification.

This Special Issue deals with, but is not limited to, the following topics:

Biomedical image processing
Machine and deep learning techniques for image analysis (i.e., segmentation of cells, tissues, organs, lesions; classification of cells, diseases, tumours, etc.)
Image registration techniques
Image preprocessing techniques
Image-based 3D reconstruction
Computer-aided detection and diagnosis systems (CADs)
Biomedical image analysis
Radiomics and artificial intelligence for personalised medicine
Multimodality fusion (e.g., MRI, PET, CT, ultrasound) for diagnosis, image analysis and image-guided intervention
Machine and deep learning as tools to support medical diagnoses and decisions
Image retrieval (e.g., context-based retrieval, lesion similarity)
CAD architectures
Advanced architecture for biomedical image remote processing, elaboration and transmission
3D vision, virtual, augmented and mixed reality applicationa in remote surgery
Image processing techniques for privacy-preserving AI in medicine.

Prof. Dr. Cecilia Di Ruberto
Dr. Andrea Loddo
Dr. Lorenzo Putzu
Dr. Albert Comelli
Dr. Alessandro Stefano
Guest Editors

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

biomedical image processing
biomedical image classification
biomedical image retrieval
CAD systems
deep learning
machine learning
disease analysis

Published Papers (5 papers)

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Research

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20 pages, 5800 KiB

Open AccessArticle

A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection

by Nelson Faria, Sofia Campelos and Vítor Carvalho

Appl. Sci. 2023, 13(11), 6571; https://doi.org/10.3390/app13116571 - 29 May 2023

Cited by 2 | Viewed by 1967

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, and its diagnosis must be carried out as soon as possible to increase the survival rate. The development of computer-aided diagnosis systems can improve the accuracy of lung cancer diagnosis while reducing the workload of pathologists. The purpose of this study was to develop a learning algorithm (CancerDetecNN) to evaluate the presence or absence of tumor tissue in lung whole-slide images (WSIs) while reducing the computational cost. Three existing deep neural network models, including different versions of the CancerDetecNN algorithm, were trained and tested on datasets of tumor and non-tumor tiles extracted from lung WSIs. The fifth version of CancerDetecNN (CancerDetecNN Version 5) outperformed all existing convolutional neural network (CNN) models in the provided dataset, achieving higher precision (0.972), an area under the curve (AUC) of 0.923, and an F1-score of 0.897, while requiring 1 h and 51 min less for training than the best compared CNN model (ResNet-50). The results for CancerDetecNN Version 5 surpass the results of some architectures used in the literature, but the relatively small size and limited diversity of the dataset used in this study must be considered. This paper demonstrates the potential of CancerDetecNN Version 5 for improving lung cancer diagnosis since it is a dedicated model for lung cancer that leverages domain-specific knowledge and optimized architecture to capture unique characteristics and patterns in lung WSIs, potentially outperforming generic models in this domain and reducing the computational cost. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics in Computer-Aided Diagnosis)

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14 pages, 3842 KiB

Open AccessArticle

Three-Dimensional Anatomical Analysis of Muscle–Skeletal Districts

by Martina Paccini, Giuseppe Patanè and Michela Spagnuolo

Appl. Sci. 2022, 12(23), 12048; https://doi.org/10.3390/app122312048 - 25 Nov 2022

Viewed by 940

Abstract

This work addresses the patient-specific characterisation of the morphology and pathologies of muscle–skeletal districts (e.g., wrist, spine) to support diagnostic activities and follow-up exams through the integration of morphological and tissue information. We propose different methods for the integration of morphological information, retrieved from the geometrical analysis of 3D surface models, with tissue information extracted from volume images. For the qualitative and quantitative validation, we discuss the localisation of bone erosion sites on the wrists to monitor rheumatic diseases and the characterisation of the three functional regions of the spinal vertebrae to study the presence of osteoporotic fractures. The proposed approach supports the quantitative and visual evaluation of possible damages, surgery planning, and early diagnosis or follow-up studies. Finally, our analysis is general enough to be applied to different districts. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics in Computer-Aided Diagnosis)

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14 pages, 1040 KiB

Open AccessArticle

An Investigation on Radiomics Feature Handling for HNSCC Staging Classification

by Nadia Brancati, Massimo La Rosa, Giuseppe De Pietro, Giusy Esposito, Marika Valentino, Marco Aiello and Marco Salvatore

Appl. Sci. 2022, 12(15), 7826; https://doi.org/10.3390/app12157826 - 4 Aug 2022

Cited by 1 | Viewed by 1275

Abstract

The incidence of Head and Neck Squamous Cell Carcinoma (HNSCC) has been growing in the last few decades. Its diagnosis is usually performed through clinical evaluation and analyzing radiological images, then confirmed by histopathological examination, an invasive and time-consuming operation. The recent advances in the artificial intelligence field are leading to interesting results in the early diagnosis, personalized treatment and monitoring of HNSCC only by analyzing radiological images, without performing a tissue biopsy. The large amount of radiological images and the increasing interest in radiomics approaches can help to develop machine learning (ML) methods to support diagnosis. In this work, we propose an ML method based on the use of radiomics features, extracted from CT and PET images, to classify the disease in terms of pN-Stage, pT-Stage and Overall Stage. After the extraction of radiomics features, a selection step is performed to remove dataset redundancy. Finally, ML methods are employed to complete the classification task. Our pipeline is applied on the “Head-Neck-PET-CT” TCIA open-source dataset, considering a cohort of 201 patients from four different institutions. An AUC of

97 %

83 %

and

93 %

in terms of pN-Stage, pT-Stage and Overall Stage classification, respectively, is achieved. The obtained results are promising, showing the potential efficiency of the use of radiomics approaches in staging classification. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics in Computer-Aided Diagnosis)

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16 pages, 4670 KiB

Open AccessArticle

A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype

by Antonio Brunetti, Nicola Altini, Domenico Buongiorno, Emilio Garolla, Fabio Corallo, Matteo Gravina, Vitoantonio Bevilacqua and Berardino Prencipe

Appl. Sci. 2022, 12(12), 5829; https://doi.org/10.3390/app12125829 - 8 Jun 2022

Cited by 6 | Viewed by 2099

Abstract

Lung cancer is one of the deadliest diseases worldwide. Computed Tomography (CT) images are a powerful tool for investigating the structure and texture of lung nodules. For a long time, trained radiologists have performed the grading and staging of cancer severity by relying on radiographic images. Recently, radiomics has been changing the traditional workflow for lung cancer staging by providing the technical and methodological means to analytically quantify lesions so that more accurate predictions could be performed while reducing the time required from each specialist to perform such tasks. In this work, we implemented a pipeline for identifying a radiomic signature composed of a reduced number of features to discriminate between adenocarcinomas and other cancer types. In addition, we also investigated the reproducibility of this radiomic study analysing the performances of the classification models on external validation data. In detail, we first considered two publicly available datasets, namely D1 and D2, composed of n = 262 and n = 89 samples, respectively. Ten significant features, according to univariate AUC evaluated on D1, were retained. Mann–Whitney U tests recognised three of these features to have a statistically different distribution, with a p-value < 0.05. Then, we collected n = 51 CT images from patients with lung nodules at the Azienda Ospedaliero—Universitaria “Policlinico Riuniti” in Foggia. Resident radiologists manually annotated the lung lesions in images to allow the subsequent analysis of the malignancy regions. We designed a pipeline for feature extraction from the Volumes of Interest in order to generate a third dataset, i.e., D3. Several experiments have been performed showing that the selected radiomic signature not only allowed the discrimination of lung adenocarcinoma from other cancer types independently from the input dataset used for training the models, but also allowed reaching good classification performances also on external validation data; in fact, the radiomic signature computed on D1 and evaluated on the local cohort allowed reaching an AUC of 0.70 (

p < 0.001

) for the task of predicting the histological subtype. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics in Computer-Aided Diagnosis)

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Review

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31 pages, 9114 KiB

Open AccessReview

Digital Medical X-ray Imaging, CAD in Lung Cancer and Radiomics in Colorectal Cancer: Past, Present and Future

by Jacobo Porto-Álvarez, Gary T. Barnes, Alex Villanueva, Roberto García-Figueiras, Sandra Baleato-González, Emilio Huelga Zapico and Miguel Souto-Bayarri

Appl. Sci. 2023, 13(4), 2218; https://doi.org/10.3390/app13042218 - 9 Feb 2023

Cited by 5 | Viewed by 2356

Abstract

Computed tomography (CT) introduced medicine to digital imaging. This occurred in the early 1970s and it was the start of the digital medical imaging revolution. The resulting changes and improvements in health care associated with digital imaging have been marked, are occurring now, and are likely to continue into the future. Before CT, medical images were acquired, stored, and displayed in analog form (i.e., on film). Now essentially all medical images are acquired and stored digitally. When they are not viewed by computer, they are converted to an analog image to be seen. The application of computer algorithms and the processing of digital medical images improves the visualization of diagnostically important details and aids diagnosis by extracting significant quantitative information. Examples of this can be seen with CAD and radiomics applications in the diagnosis of lung and colorectal cancer, respectively. The objectives of this article are to point out the key aspects of the digital medical imaging revolution, to review its current status, to discuss its clinical translation in two major areas: lung and colorectal cancer, and to provide future directions and challenges of these techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics in Computer-Aided Diagnosis)

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