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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). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1066

Special Issue 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 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

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

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Published Papers (1 paper)

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Research

41 pages, 8582 KiB  
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
Viewed by 847
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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