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Radiomics and Machine Learning for Medical Imaging
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Radiomics and Machine Learning for Medical Imaging

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: closed (1 November 2023) | Viewed by 6037

Special Issue Editor

Dr. Lei Qin

E-Mail Website
Guest Editor
1. Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
2. Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
Interests: magnetic resonance imaging; medical imaging physics; quantitative imaging biomarker; radiomics and deep learning

Special Issue Information

Dear Colleagues,

Radiomics is an emerging field that focuses on the extraction of quantitative features from radiological images to predict clinical outcomes. Deep learning algorithms can be trained to analyze large amounts of imaging data, identify subtle patterns, and generate predictive models that can be used to facilitate clinical decision making. The integration of radiomics with deep learning has the potential to revolutionize radiology by improving diagnostic accuracy, enabling personalized treatment, and facilitating the development of precision medicine. Radiomics and deep learning have already shown promising results in cancer diagnosis and treatment planning, but there are still challenges to be addressed, e.g., to standardize radiomic features, to optimize deep learning algorithms, and to deploy these tools into clinical practice. Despite these challenges, radiomics and deep learning present exciting opportunities for innovation and progress in radiology, and we encourage the submission of papers that consider innovative approaches in this field for this Special Issue.

Dr. Lei Qin
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. Journal of Clinical Medicine 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 2600 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
  • deep learning
  • radiological images
  • quantitative imaging
  • predictive models

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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9 pages, 238 KiB  
Article
Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules
by Birte Bomhals, Lara Cossement, Alex Maes, Mike Sathekge, Kgomotso M. G. Mokoala, Chabi Sathekge, Katrien Ghysen and Christophe Van de Wiele
J. Clin. Med. 2023, 12(24), 7731; https://doi.org/10.3390/jcm12247731 - 17 Dec 2023
Cited by 3 | Viewed by 1532
Abstract
Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax [...] Read more.
Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone. Full article
(This article belongs to the Special Issue Radiomics and Machine Learning for Medical Imaging)

Review

Jump to: Research

11 pages, 614 KiB  
Review
Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art
by Luigi Manco, Domenico Albano, Luca Urso, Mattia Arnaboldi, Massimo Castellani, Luigia Florimonte, Gabriele Guidi, Alessandro Turra, Angelo Castello and Stefano Panareo
J. Clin. Med. 2023, 12(24), 7669; https://doi.org/10.3390/jcm12247669 - 13 Dec 2023
Cited by 2 | Viewed by 1730
Abstract
Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission [...] Read more.
Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important role in mining additional information from medical images beyond human eyes’ resolving power. Our review provides a summary of the current status of radiomics and AI in different clinical contexts of MM. A systematic search of PubMed, Web of Science, and Scopus was conducted, including all the articles published in English that explored radiomics and AI analyses of PET/CT images in MM. The initial results have highlighted the potential role of such new features in order to improve the clinical stratification of MM patients, as well as to increase their clinical benefits. However, more studies are warranted before these approaches can be implemented in clinical routines. Full article
(This article belongs to the Special Issue Radiomics and Machine Learning for Medical Imaging)
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22 pages, 869 KiB  
Review
Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis
by Giulia Pacella, Maria Chiara Brunese, Eleonora D’Imperio, Marco Rotondo, Andrea Scacchi, Mattia Carbone and Germano Guerra
J. Clin. Med. 2023, 12(23), 7380; https://doi.org/10.3390/jcm12237380 - 28 Nov 2023
Cited by 4 | Viewed by 2261
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. [...] Read more.
Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. Methods: The PubMed database was searched for papers published in the English language no earlier than January 2018. Results: We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice. Full article
(This article belongs to the Special Issue Radiomics and Machine Learning for Medical Imaging)
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