Journal Browser

► Journal Browser

Special Issue Editors

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

Published Papers (2 papers)

Download All Papers

Research

Jump to: Review

17 pages, 2389 KiB

Open AccessArticle

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 - 03 Nov 2023

Cited by 1 | Viewed by 968

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

39 pages, 4286 KiB

Open AccessReview

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 1 | Viewed by 1943

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). 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)

► Show Figures