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Diagnostics
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Artificial Intelligence-Driven Radiomics in Medical Diagnosis
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Artificial Intelligence-Driven Radiomics in Medical Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1967

Special Issue Editor

Dr. Shara WY Lee

E-Mail Website
Guest Editor
Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
Interests: clinical trials; immersive technology in clinical skills education; clinical applications of photobiomodulation among cancer patients; non-invasive haemodynamic monitoring in transfusion medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence (AI) and medical imaging signifies a paradigm shift in healthcare diagnostics. Traditional diagnostic methodologies, reliant on the human eye and expertise, possess inherent limitations such as inter-observer variability. AI, especially deep learning architectures like convolutional neural networks (CNNs), offers a remedy. These algorithms are adept at pattern recognition, extracting intricate features from images often imperceptible to clinicians. As a result, AI-driven models have shown remarkable proficiency in tasks ranging from tumour detection in radiographs to retinal disease classification in ophthalmic images. Moreover, AI's potential extends beyond mere diagnosis. Predictive modelling, image reconstruction, and workflow optimisation are facets undergoing rapid transformation under AI's influence. However, the marriage of AI and medical imaging is not without challenges: data privacy, algorithmic transparency, and clinical integration pose pertinent questions. This Special Issue delves into these advancements and hurdles, providing a holistic perspective on the current state and future trajectory of AI in the medical imaging domain. It underscores the pivotal role AI is positioned to play in shaping the next frontier of diagnostic medicine.

Dr. Shara WY Lee
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.

Keywords

  • deep learning
  • convolutional neural networks (CNNs)
  • medical imaging
  • diagnostic algorithms
  • image reconstruction
  • pattern recognition
  • predictive modelling
  • clinical integration
  • tumour identification
  • algorithmic transparency
  • image segmentation
  • diagnostic accuracy

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Published Papers (4 papers)

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Research

19 pages, 3431 KB  
Article
Computed Tomography Radiomics and Machine Learning for Prediction of Histology-Based Hepatic Steatosis Scores
by Winston T. Chu, Hui Wang, Marcelo A. Castro, Venkatesh Mani, C. Paul Morris, Thomas C. Friedrich, David H. O’Connor, Courtney L. Finch, Ji Hyun Lee, Philip J. Sayre, Gabriella Worwa, Anya Crane, Jens H. Kuhn, Ian Crozier, Jeffrey Solomon and Claudia Calcagno
Diagnostics 2025, 15(18), 2310; https://doi.org/10.3390/diagnostics15182310 - 11 Sep 2025
Abstract
Background/Objective: Computed tomography (CT) can be used to non-invasively assess the health of the liver; however, radiologist evaluation and simple thresholding alone are insufficient for diagnosis of hepatic steatosis, necessitating biopsies. This study explored CT radiomics and machine learning to enable non-invasive, objective, [...] Read more.
Background/Objective: Computed tomography (CT) can be used to non-invasively assess the health of the liver; however, radiologist evaluation and simple thresholding alone are insufficient for diagnosis of hepatic steatosis, necessitating biopsies. This study explored CT radiomics and machine learning to enable non-invasive, objective, and quantitative prediction of steatosis severity across the macaque liver. Methods: In this retrospective study, CT images of 42 crab-eating macaques (age [yr] = 6.1 ± 1.7; sex [male/female] = 26/16) with varying degrees of hepatic steatosis were analyzed, and the results were compared to histology-based steatosis scores of livers from the same animals. After extracting radiomic features, a thorough array of statistical analyses, feature selection techniques, and machine learning models were applied to identify a distinct radiomic signature of histologically defined hepatic steatosis. Results: We identified 12 radiomic features that correlated with steatosis scores, and hierarchical clustering based on radiomic attributes alone revealed clusters roughly aligning with steatosis severity groups. The k-nearest neighbors model architecture best predicted histopathologic steatosis scores in both classification and regression tasks (area under the receiver operating characteristic curve [AUC ROC] = 0.89 ± 0.09; root-mean-square error [RMSE] = 0.60 ± 0.10). Feature analyses identified seven key radiomic features (six first-order features and one gray-level co-occurrence matrix feature) that were most important when predicting steatosis. Conclusions: We identified a CT radiomic signature of steatosis and demonstrated that histology-based steatosis scores can be predicted non-invasively and objectively using machine learning and CT radiomics as a potential alternative to invasive core biopsies. Given the strong similarities in liver structure, liver function, and hepatic steatosis pathophysiology between macaques and humans, these findings have the potential to translate to humans. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Radiomics in Medical Diagnosis)
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12 pages, 1042 KB  
Article
Prediction of Immunotherapy Response in Hepatocellular Carcinoma Patients Using Pretreatment CT Images
by Ji Hye Min, Pin-Jung Chen, Touseef Ahmad Qureshi, Sehrish Javed, Yibin Xie, Linda Azab, Lixia Wang, Hyun-seok Kim, Debiao Li and Ju Dong Yang
Diagnostics 2025, 15(16), 2090; https://doi.org/10.3390/diagnostics15162090 - 20 Aug 2025
Viewed by 605
Abstract
Background/Aims: Predicting treatment response to immunotherapy in hepatocellular carcinoma (HCC) is essential to improve clinical outcomes with personalized treatment strategies. This study aims to develop an AI-driven prediction model using radiomic analysis from the liver and viable HCCs on pretreatment CT to differentiate [...] Read more.
Background/Aims: Predicting treatment response to immunotherapy in hepatocellular carcinoma (HCC) is essential to improve clinical outcomes with personalized treatment strategies. This study aims to develop an AI-driven prediction model using radiomic analysis from the liver and viable HCCs on pretreatment CT to differentiate responders from non-responders. Methods: HCC patients who received immunotherapy between 2016 and 2023 with pretreatment CT scans were included. Radiomic features were extracted from the whole liver and the viable HCCs on the portal venous phase CT prior to immunotherapy. Multiple machine learning models were trained for binary classification to predict treatment response, initially using liver features (Model 1), and subsequently including both liver and tumor features (Model 2). Model performance was evaluated using three-fold cross-validation. Results: Among 55 HCC patients (median age, 69; 76.4% male) who received immunotherapy, 21 (38.2%) were responders and 34 (61.8%) non-responders by mRECIST criteria. Over 5000 radiomic features were extracted from pretreatment CT scans of the liver and viable tumors, of which approximately 100 were predictive of treatment response. Model 1 (liver) achieved an average accuracy of 77%, sensitivity of 76%, and specificity of 78%. Model 2 (liver and tumor) demonstrated improved performance, with accuracy, sensitivity, and specificity of 86%, 70%, and 94%, respectively, supporting the value of combined liver–tumor radiomics in treatment response prediction. Conclusions: This pilot study developed an AI-based model using CT-derived radiomic features to predict immunotherapy response in HCC patients. The approach may offer a non-invasive strategy to support personalized treatment planning using pretreatment CT scans. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Radiomics in Medical Diagnosis)
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20 pages, 4576 KB  
Article
Enhanced HoVerNet Optimization for Precise Nuclei Segmentation in Diffuse Large B-Cell Lymphoma
by Gei Ki Tang, Chee Chin Lim, Faezahtul Arbaeyah Hussain, Qi Wei Oung, Aidy Irman Yajid, Sumayyah Mohammad Azmi and Yen Fook Chong
Diagnostics 2025, 15(15), 1958; https://doi.org/10.3390/diagnostics15151958 - 4 Aug 2025
Viewed by 522
Abstract
Background/Objectives: Diffuse Large B-Cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and demands precise segmentation and classification of nuclei for effective diagnosis and disease severity assessment. This study aims to evaluate the performance of HoVerNet, a deep learning model, [...] Read more.
Background/Objectives: Diffuse Large B-Cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and demands precise segmentation and classification of nuclei for effective diagnosis and disease severity assessment. This study aims to evaluate the performance of HoVerNet, a deep learning model, for nuclei segmentation and classification in CMYC-stained whole slide images and to assess its integration into a user-friendly diagnostic tool. Methods: A dataset of 122 CMYC-stained whole slide images (WSIs) was used. Pre-processing steps, including stain normalization and patch extraction, were applied to improve input consistency. HoVerNet, a multi-branch neural network, was used for both nuclei segmentation and classification, particularly focusing on its ability to manage overlapping nuclei and complex morphological variations. Model performance was validated using metrics such as accuracy, precision, recall, and F1 score. Additionally, a graphic user interface (GUI) was developed to incorporate automated segmentation, cell counting, and severity assessment functionalities. Results: HoVerNet achieved a validation accuracy of 82.5%, with a precision of 85.3%, recall of 82.6%, and an F1 score of 83.9%. The model showed powerful performance in differentiating overlapping and morphologically complex nuclei. The developed GUI enabled real-time visualization and diagnostic support, enhancing the efficiency and usability of DLBCL histopathological analysis. Conclusions: HoVerNet, combined with an integrated GUI, presents a promising approach for streamlining DLBCL diagnostics through accurate segmentation and real-time visualization. Future work will focus on incorporating Vision Transformers and additional staining protocols to improve generalizability and clinical utility. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Radiomics in Medical Diagnosis)
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15 pages, 1361 KB  
Article
Radiomics with Clinical Data and [18F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study
by Gijs D. van Praagh, Francine Vos, Stijn Legtenberg, Marjan Wouthuyzen-Bakker, Ilse J. E. Kouijzer, Erik H. J. G. Aarntzen, Jean-Paul P. M. de Vries, Riemer H. J. A. Slart, Lejla Alic, Bhanu Sinha and Ben R. Saleem
Diagnostics 2025, 15(15), 1944; https://doi.org/10.3390/diagnostics15151944 - 2 Aug 2025
Viewed by 517
Abstract
Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [18F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on [...] Read more.
Objective: We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [18F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). Methods: Three ML models were developed: one based on pre-treatment criteria to diagnose a vascular graft infection (“MAGIC-light features”), another using radiomics features from diagnostic [18F]FDG-PET scans, and a third combining both datasets. The training set included 92 patients (72 iVGEI-positive, 20 iVGEI-negative), and the external test set included 20 iVGEI-positive and 12 iVGEI-negative patients. The abdominal aorta and iliac arteries in the PET/CT scans were automatically segmented using SEQUOIA and TotalSegmentator and manually adjusted, extracting 96 radiomics features. The best-performing models for the MAGIC-light features and PET-radiomics features were selected from 343 unique models. Most relevant features were combined to test three final models using ROC analysis, accuracy, sensitivity, and specificity. Results: The combined model achieved the highest AUC in the test set (mean ± SD: 0.91 ± 0.02) compared with the MAGIC-light-only model (0.85 ± 0.06) and the PET-radiomics model (0.73 ± 0.03). The combined model also achieved a higher accuracy (0.91 vs. 0.82) than the diagnosis based on all the MAGIC criteria and a comparable sensitivity and specificity (0.70 and 1.00 vs. 0.76 and 0.92, respectively) while providing diagnostic information at the initial presentation. The AUC for the combined model was significantly higher than the PET-radiomics model (p = 0.02 in the bootstrap test), while other comparisons were not statistically significant. Conclusions: This study demonstrated the potential of ML models in supporting diagnostic decision making for iVGEI. A combined model using pre-treatment clinical features and PET-radiomics features showed high diagnostic performance and specificity, potentially reducing overtreatment and enhancing patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Radiomics in Medical Diagnosis)
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