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Diagnostics
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Machine Learning in Radiomics: Opportunities and Challenges
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Machine Learning in Radiomics: Opportunities and Challenges

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 9612

Special Issue Editor

Dr. Hamid Abdollahi

E-Mail Website
Guest Editor
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
Interests: machine learning; radiomics; radiology; MRI; CT; PET; computer-aided diagnosis; radiobiological modelling; precision radiation oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in medical imaging technologies, alongside those made in disciplines such as computer science, have revolutionized healthcare delivery by making diagnostics and therapeutics more efficient. Radiomics is a promising, widely used, and high-throughput quantitative medical imaging method in precision medicine. Studies on conventional and deep-learning-based radiomics have opened a new horizon for improving disease diagnosis, prognosis, treatment, and follow-up. In this era, the role of artificial intelligence, including machine learning (ML) algorithms, is critical. ML algorithms can learn from imaging features and extract valuable information in terms of predictive or prognostic models. As such, the development of ML radiomics-based models will provide a great opportunity to add value to current clinical decision-support systems.

This Special Issue will explore the intersection of radiomics and ML techniques in terms of opportunities and challenges of machine learning in radiomics. We encourage all specialists in the field to bring their great ideas and discuss how this amazing field of science will improve precision medicine. We welcome all manuscripts on this topic, including original research and review articles. Of particular interest are papers that focus on opportunities and challenges in radiomics and machine learning” as they relate to:

  • Rare diseases;
  • Drug discovery;
  • Clinical oncology;
  • Nuclear medicine diagnosis and therapy.

Dr. Hamid Abdollahi
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. Diagnostics 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.

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Published Papers (4 papers)

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Research

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16 pages, 2740 KiB  
Article
Radiomics Based on Single-Phase CTA for Distinguishing Left Atrial Appendage Thrombus from Circulatory Stasis in Patients with Atrial Fibrillation before Ablation
by Xue Li, Yuyan Cai, Xiaoyi Chen, Yue Ming, Wenzhang He, Jing Liu, Huaxia Pu, Xinyue Chen and Liqing Peng
Diagnostics 2023, 13(15), 2474; https://doi.org/10.3390/diagnostics13152474 - 25 Jul 2023
Viewed by 1349
Abstract
Differentiation of left atrial appendage thrombus (LAAT) and left atrial appendage (LAA) circulatory stasis is difficult when based only on single-phase computed tomography angiography (CTA) in routine clinical practice. Radiomics provides a promising tool for their identification. We retrospectively enrolled 204 (training set: [...] Read more.
Differentiation of left atrial appendage thrombus (LAAT) and left atrial appendage (LAA) circulatory stasis is difficult when based only on single-phase computed tomography angiography (CTA) in routine clinical practice. Radiomics provides a promising tool for their identification. We retrospectively enrolled 204 (training set: 144; test set: 60) atrial fibrillation patients before ablation, including 102 LAAT and 102 circulatory stasis patients. Radiomics software was used to segment whole LAA on single-phase CTA images and extract features. Models were built and compared via a multivariable logistic regression algorithm and area under of the receiver operating characteristic curves (AUCs), respectively. For the radiomics model, radiomics clinical model, radiomics radiological model, and combined model, the AUCs were 0.82, 0.86, 0.90, 0.93 and 0.82, 0.82, 0.84, 0.85 in the training set and the test set, respectively (p < 0.05). One clinical feature (rheumatic heart disease) and four radiological features (transverse diameter of left atrium, volume of left atrium, location of LAA, shape of LAA) were added to the combined model. The combined model exhibited excellent differential diagnostic performances between LAAT and circulatory stasis without increasing extra radiation exposure. The single-phase, CTA-based radiomics analysis shows potential as an effective tool for accurately detecting LAAT in patients with atrial fibrillation before ablation. Full article
(This article belongs to the Special Issue Machine Learning in Radiomics: Opportunities and Challenges)
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14 pages, 4077 KiB  
Article
Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques
by Mohammad R. Salmanpour, Seyed Masoud Rezaeijo, Mahdi Hosseinzadeh and Arman Rahmim
Diagnostics 2023, 13(10), 1696; https://doi.org/10.3390/diagnostics13101696 - 11 May 2023
Cited by 33 | Viewed by 2818
Abstract
Background: Although handcrafted radiomics features (RF) are commonly extracted via radiomics software, employing deep features (DF) extracted from deep learning (DL) algorithms merits significant investigation. Moreover, a “tensor’’ radiomics paradigm where various flavours of a given feature are generated and explored can provide [...] Read more.
Background: Although handcrafted radiomics features (RF) are commonly extracted via radiomics software, employing deep features (DF) extracted from deep learning (DL) algorithms merits significant investigation. Moreover, a “tensor’’ radiomics paradigm where various flavours of a given feature are generated and explored can provide added value. We aimed to employ conventional and tensor DFs, and compare their outcome prediction performance to conventional and tensor RFs. Methods: 408 patients with head and neck cancer were selected from TCIA. PET images were first registered to CT, enhanced, normalized, and cropped. We employed 15 image-level fusion techniques (e.g., dual tree complex wavelet transform (DTCWT)) to combine PET and CT images. Subsequently, 215 RFs were extracted from each tumor in 17 images (or flavours) including CT only, PET only, and 15 fused PET-CT images through the standardized-SERA radiomics software. Furthermore, a 3 dimensional autoencoder was used to extract DFs. To predict the binary progression-free-survival-outcome, first, an end-to-end CNN algorithm was employed. Subsequently, we applied conventional and tensor DFs vs. RFs as extracted from each image to three sole classifiers, namely multilayer perceptron (MLP), random-forest, and logistic regression (LR), linked with dimension reduction algorithms. Results: DTCWT fusion linked with CNN resulted in accuracies of 75.6 ± 7.0% and 63.4 ± 6.7% in five-fold cross-validation and external-nested-testing, respectively. For the tensor RF-framework, polynomial transform algorithms + analysis of variance feature selector (ANOVA) + LR enabled 76.67 ± 3.3% and 70.6 ± 6.7% in the mentioned tests. For the tensor DF framework, PCA + ANOVA + MLP arrived at 87.0 ± 3.5% and 85.3 ± 5.2% in both tests. Conclusions: This study showed that tensor DF combined with proper machine learning approaches enhanced survival prediction performance compared to conventional DF, tensor and conventional RF, and end-to-end CNN frameworks. Full article
(This article belongs to the Special Issue Machine Learning in Radiomics: Opportunities and Challenges)
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18 pages, 1660 KiB  
Article
Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems
by Mahdi Hosseinzadeh, Arman Gorji, Ali Fathi Jouzdani, Seyed Masoud Rezaeijo, Arman Rahmim and Mohammad R. Salmanpour
Diagnostics 2023, 13(10), 1691; https://doi.org/10.3390/diagnostics13101691 - 10 May 2023
Cited by 41 | Viewed by 3656
Abstract
Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). Methods: 297 patients were selected [...] Read more.
Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). Methods: 297 patients were selected from the Parkinson’s Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing. Results: For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively. Conclusions: We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance. Full article
(This article belongs to the Special Issue Machine Learning in Radiomics: Opportunities and Challenges)
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Review

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33 pages, 9381 KiB  
Review
Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies
by Maurizio Cè, Marius Dumitru Chiriac, Andrea Cozzi, Laura Macrì, Francesca Lucrezia Rabaiotti, Giovanni Irmici, Deborah Fazzini, Gianpaolo Carrafiello and Michaela Cellina
Diagnostics 2024, 14(22), 2473; https://doi.org/10.3390/diagnostics14222473 - 5 Nov 2024
Viewed by 742
Abstract
Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned [...] Read more.
Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)—a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation. Full article
(This article belongs to the Special Issue Machine Learning in Radiomics: Opportunities and Challenges)
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