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Diagnostics
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Advanced Computer-Aided Diagnosis Using Medical Images
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Advanced Computer-Aided Diagnosis Using Medical Images

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: closed (30 September 2024) | Viewed by 9695

Special Issue Editors

Dr. Sara Harsini

E-Mail Website
Guest Editor
BC Cancer Research Institute, Vancouver, BC, Canada
Interests: medical imaging; molecular imaging; PET/CT; theranostics; cancer imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Fereshteh Yousefirizi

E-Mail Website
Guest Editor
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
Interests: medical image analysis; artificial intelligence; deep learning; PET/CT; radiomics; ultrasound
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on Advanced Computer-Aided Diagnosis Using Medical Images focuses on the latest developments in its application to medical imaging and cancer research. This Special Issue is open to and welcomes contributions with a wide range of topics including, but not limited to: diffusion models, federated learning, contrastive learning, active learning, semi-supervised learning, graph neural networks, large language models (transformers such as ChatGPT) for CAD, radiomics, generalizability, and privacy considerations in the deployment of advanced deep learning models for CAD. The goal of this Special Issue is to showcase novel approaches or significant improvements in existing CAD methods and discuss the challenges and opportunities associated with the deployment of AI approaches in medical imaging.

Dr. Sara Harsini
Dr. Fereshteh Yousefirizi
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. 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.

Keywords

  • machine/deep learning
  • quantitative imaging
  • radio/genomics
  • bioinformatics
  • medical image analysis
  • computer-aided diagnosis

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Published Papers (3 papers)

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Research

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20 pages, 2515 KiB  
Article
Detection of Thymoma Disease Using mRMR Feature Selection and Transformer Models
by Mehmet Agar, Siyami Aydin, Muharrem Cakmak, Mustafa Koc and Mesut Togacar
Diagnostics 2024, 14(19), 2169; https://doi.org/10.3390/diagnostics14192169 - 29 Sep 2024
Cited by 2 | Viewed by 1554
Abstract
Background: Thymoma is a tumor that originates in the thymus gland, a part of the human body located behind the breastbone. It is a malignant disease that is rare in children but more common in adults and usually does not spread outside the [...] Read more.
Background: Thymoma is a tumor that originates in the thymus gland, a part of the human body located behind the breastbone. It is a malignant disease that is rare in children but more common in adults and usually does not spread outside the thymus. The exact cause of thymic disease is not known, but it is thought to be more common in people infected with the EBV virus at an early age. Various surgical methods are used in clinical settings to treat thymoma. Expert opinion is very important in the diagnosis of the disease. Recently, next-generation technologies have become increasingly important in disease detection. Today’s early detection systems already use transformer models that are open to technological advances. Methods: What makes this study different is the use of transformer models instead of traditional deep learning models. The data used in this study were obtained from patients undergoing treatment at Fırat University, Department of Thoracic Surgery. The dataset consisted of two types of classes: thymoma disease images and non-thymoma disease images. The proposed approach consists of preprocessing, model training, feature extraction, feature set fusion between models, efficient feature selection, and classification. In the preprocessing step, unnecessary regions of the images were cropped, and the region of interest (ROI) technique was applied. Four types of transformer models (Deit3, Maxvit, Swin, and ViT) were used for model training. As a result of the training of the models, the feature sets obtained from the best three models were merged between the models (Deit3 and Swin, Deit3 and ViT, Deit3 and ViT, Swin and ViT, and Deit3 and Swin and ViT). The combined feature set of the model (Deit3 and ViT) that gave the best performance with fewer features was analyzed using the mRMR feature selection method. The SVM method was used in the classification process. Results: With the mRMR feature selection method, 100% overall accuracy was achieved with feature sets containing fewer features. The cross-validation technique was used to verify the overall accuracy of the proposed approach and 99.22% overall accuracy was achieved in the analysis with this technique. Conclusions: These findings emphasize the added value of the proposed approach in the detection of thymoma. Full article
(This article belongs to the Special Issue Advanced Computer-Aided Diagnosis Using Medical Images)
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17 pages, 2389 KiB  
Article
Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
by Yu-De Wang, Chi-Ping Huang, You-Rong Yang, Hsi-Chin Wu, Yu-Ju Hsu, Yi-Chun Yeh, Pei-Chun Yeh, Kuo-Chen Wu and Chia-Hung Kao
Diagnostics 2023, 13(21), 3380; https://doi.org/10.3390/diagnostics13213380 - 3 Nov 2023
Cited by 2 | Viewed by 2012
Abstract
Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients’ clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival [...] Read more.
Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients’ clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment. Methods: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model. Clinical information and radiomics features of BS were obtained. Survival-related clinical features and radiomics features were included in the ML model training. Using the pyradiomics software, 128 radiomics features from each BS image’s region of interest, validated by experts, were extracted. Four textural matrices were also calculated: GLCM, NGLDM, GLRLM, and GLSZM. Five training models (Logistic Regression, Naive Bayes, Random Forest, Support Vector Classification, and XGBoost) were applied using K-fold cross-validation. Recurrence was defined as either a rise in PSA levels, radiographic progression, or death. To assess the classifier’s effectiveness, the ROC curve area and confusion matrix were employed. Results: Of the 354 patients, 101 patients were categorized into the recurrence group with more advanced disease status compared to the non-recurrence group. Key clinical features including tumor stage, radical prostatectomy, initial PSA, Gleason Score primary pattern, and radiotherapy were used for model training. Random Forest (RF) was the best-performing model, with a sensitivity of 0.81, specificity of 0.87, and accuracy of 0.85. The ROC curve analysis showed that predictions from RF outperformed predictions from other ML models with a final AUC of 0.94 and a p-value of <0.001. The other models had accuracy ranges from 0.52 to 0.78 and AUC ranges from 0.67 to 0.84. Conclusions: The study showed that ML based on clinical features and radiomics features of BS improves the prediction of PCa recurrence after initial treatment. These findings highlight the added value of ML techniques for risk classification in PCa based on clinical features and radiomics features of BS. Full article
(This article belongs to the Special Issue Advanced Computer-Aided Diagnosis Using Medical Images)
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Review

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39 pages, 4286 KiB  
Review
Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens
by Elmira Yazdani, Parham Geramifar, Najme Karamzade-Ziarati, Mahdi Sadeghi, Payam Amini and Arman Rahmim
Diagnostics 2024, 14(2), 181; https://doi.org/10.3390/diagnostics14020181 - 14 Jan 2024
Cited by 14 | Viewed by 5024
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). [...] Read more.
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed. Full article
(This article belongs to the Special Issue Advanced Computer-Aided Diagnosis Using Medical Images)
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