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Diagnostics
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Radiomics and Artificial Intelligence Applications in the Diagnosis of Brain...
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Radiomics and Artificial Intelligence Applications in the Diagnosis of Brain Lesions

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5498

Editor

Dr. Calin G. Buzea

E-Mail Website
Guest Editor
1. Clinical Emergency Hospital “Prof. Dr. Nicolae Oblu”, 700309 Iasi, Romania
2. National Institute of Research and Development for Technical Physics, IFT, 700050 Iasi, Romania
Interests: theoretical physics; medical physics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Radiomics and Artificial Intelligence Applications in the Diagnosis of Brain Lesions”, delves into the revolutionary advancements in leveraging radiomics and artificial intelligence (AI) for diagnosing brain lesions. It brings together cutting-edge research, case studies, and expert perspectives that showcase how these technologies are transforming the diagnostic landscape. By exploring the interplay between quantitative imaging features and AI algorithms, this Special Issue highlights the enhanced accuracy, efficiency, and personalized approaches in detecting and characterizing brain abnormalities. It serves as a pivotal resource for radiologists, neurologists, AI researchers, and clinicians seeking to harness the full potential of these innovative methodologies.

Dr. Calin G. Buzea
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • brain lesions
  • biomarker
  • prognosis
  • diagnosis
  • radiomics
  • artificial intelligence

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Published Papers (3 papers)

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25 pages, 2110 KB  
Article
A Robust Semi-Supervised Brain Tumor MRI Classification Network for Data-Constrained Clinical Environments
by Subhash Chand Gupta, Vandana Bhattacharjee, Shripal Vijayvargiya, Partha Sarathi Bishnu, Raushan Oraon and Rajendra Majhi
Diagnostics 2025, 15(19), 2485; https://doi.org/10.3390/diagnostics15192485 - 28 Sep 2025
Cited by 1 | Viewed by 1577
Abstract
Background: The accurate classification of brain tumor subtypes from MRI scans is critical for timely diagnosis, yet the manual annotation of large datasets remains prohibitively labor-intensive. Method: We present SSPLNet (Semi-Supervised Pseudo-Labeling Network), a dual-branch deep learning framework that synergizes confidence-guided iterative pseudo-labelling [...] Read more.
Background: The accurate classification of brain tumor subtypes from MRI scans is critical for timely diagnosis, yet the manual annotation of large datasets remains prohibitively labor-intensive. Method: We present SSPLNet (Semi-Supervised Pseudo-Labeling Network), a dual-branch deep learning framework that synergizes confidence-guided iterative pseudo-labelling with deep feature fusion to enable robust MRI-based tumor classification in data-constrained clinical environments. SSPLNet integrates a custom convolutional neural network (CNN) and a pretrained ResNet50 model, trained semi-supervised using adaptive confidence thresholds (τ = 0.98 → 0.95 → 0.90) to iteratively refine pseudo-labels for unlabelled MRI scans. Feature representations from both branches are fused via a dense network, combining localized texture patterns with hierarchical deep features. Results: SSPLNet achieves state-of-the-art accuracy across labelled–unlabelled data splits (90:10 to 10:90), outperforming supervised baselines in extreme low-label regimes (10:90) by up to 5.34% from Custom CNN and 5.58% from ResNet50. The framework reduces annotation dependence and with 40% unlabeled data maintains 98.17% diagnostic accuracy, demonstrating its viability for scalable deployment in resource-limited healthcare settings. Conclusions: Statistical Evaluation and Robustness Analysis of SSPLNet Performance confirms that SSPLNet’s lower error rate is not due to chance. The bootstrap results also confirm that SSPLNet’s reported accuracy falls well within the 95% CI of the sampling distribution. Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence Applications in the Diagnosis of Brain Lesions)
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41 pages, 8582 KB  
Article
Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data
by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu and Simona Ruxandra Volovăț
Diagnostics 2025, 15(10), 1242; https://doi.org/10.3390/diagnostics15101242 - 14 May 2025
Cited by 6 | Viewed by 2988
Abstract
Background: Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are critical for guiding treatment strategies and patient counseling. Methods: We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers [...] Read more.
Background: Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are critical for guiding treatment strategies and patient counseling. Methods: We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers with structured clinical and demographic data to predict overall survival time. Our dataset includes 148 patients from three institutions, featuring expert-annotated segmentations of enhancing tumors, necrosis, and peritumoral edema. Two convolutional neural network backbones—ResNet-50 and EfficientNet-B0—were fused with fully connected layers processing tabular data. Models were trained using mean squared error loss and evaluated through stratified cross-validation and an independent held-out test set. Results: The hybrid model based on EfficientNet-B0 achieved state-of-the-art performance, attaining an R2 score of 0.970 and a mean absolute error of 3.05 days on the test set. Permutation feature importance highlighted edema-to-tumor ratio and enhancing tumor volume as the most informative predictors. Grad-CAM visualizations confirmed the model’s attention to anatomically and clinically relevant regions. Performance consistency across validation folds confirmed the framework’s robustness and generalizability. Conclusions: This study demonstrates that multimodal deep learning can deliver accurate, explainable, and clinically actionable survival predictions in brain metastases. The proposed framework offers a promising foundation for integration into real-world oncology workflows to support personalized prognosis and informed therapeutic decision-making. Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence Applications in the Diagnosis of Brain Lesions)
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Other

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40 pages, 6204 KB  
Systematic Review
Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis
by Lama Almudaimeegh, Kholoud Alwashmi and Zuhal Y. Hamd
Diagnostics 2026, 16(17), 2806; https://doi.org/10.3390/diagnostics16172806 - 31 Aug 2026
Viewed by 205
Abstract
Background: Magnetic resonance imaging (MRI) is central to brain tumor segmentation and histological grading, and deep learning (DL) has transformed both tasks. Existing reviews rarely span the 2017–2026 architectural arc from CNNs and U-Net variants to transformers and foundation models or appraise [...] Read more.
Background: Magnetic resonance imaging (MRI) is central to brain tumor segmentation and histological grading, and deep learning (DL) has transformed both tasks. Existing reviews rarely span the 2017–2026 architectural arc from CNNs and U-Net variants to transformers and foundation models or appraise reproducibility and clinical-translation readiness. Methods: This preregistered systematic review (PRISMA 2020, PRISMA-S) searched Google Scholar, PubMed/MEDLINE, and IEEE Xplore on 21 May 2026 (January 2017–May 2026). A single reviewer performed screening, extraction and QUADAS-AI appraisal with repeated checks on separate days; therefore, the synthesis is presented as a transparent descriptive review rather than a pooled meta-analysis. Records were screened against a priori eligibility criteria; primary experimental studies entered the synthesis and review articles formed a contextual corpus. Methodological quality was appraised using an adapted QUADAS framework (“QUADAS-AI”). Heterogeneity precluded statistical pooling, so a benchmark-driven comparative synthesis was conducted. Results: The search retrieved 33,982 records (Google Scholar 23,400; PubMed/MEDLINE 5349; IEEE Xplore 5233). After deduplication and screening, 141 full texts were assessed; one report published before the eligibility window was excluded, leaving 140 included studies: 117 primary (39 contributed to the BraTS Dice benchmark sub-set) and 23 contextual reviews. U-Net variants (42%) and hybrid CNN–transformer architectures (40%) dominate, followed by CNN classifiers (14%) and vision transformers (3%). On BraTS 2021, nnU-Net and Swin UNETR reach DSC 0.93/0.90/0.85 (whole tumor/core/enhancing); reported external evaluations show 2.5–15 percentage-point performance drops under domain shift. Conclusions: External generalizability, uncertainty quantification, reproducibility, and prospective validation remain weak; ten research priorities are proposed. Registration: OSF, DOI 10.17605/OSF.IO/C2QA6 (https://osf.io/c2qa6); registered 20 May 2026. Full article
(This article belongs to the Special Issue Radiomics and Artificial Intelligence Applications in the Diagnosis of Brain Lesions)
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