Application of Radiomics in Clinical Diagnosis

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 735

Special Issue Editors


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Guest Editor
Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
Interests: radiomics; clinical diagnosis; medical imaging

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Guest Editor Assistant
Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
Interests: radiomics; clinical diagnosis; medical imaging; imaging biomarkers

Special Issue Information

Dear Colleagues,

Radiomics has emerged as a powerful and transformative approach in medical imaging, enabling the extraction of high-dimensional quantitative features from standard radiological images. This Special Issue explores the growing role of radiomics in enhancing clinical diagnosis, offering insights into how advanced imaging analytics can improve disease detection, characterization, and prognostication across a broad spectrum of pathologies.

The contributions in this Special Issue encompass original research articles, reviews, and case studies that demonstrate the integration of radiomics with clinical, histopathological, and molecular data. Emphasis is placed on the development and validation of radiomic biomarkers, their role in decision support systems, and their impact on personalized medicine. Studies span a variety of imaging modalities, including CT, MRI, PET, and ultrasound, highlighting applications in oncology, neurology, cardiology, and beyond.

Through multidisciplinary collaboration and technological innovation, this collection underscores the potential of radiomics to bridge the gap between medical imaging and precision diagnostics, paving the way for more accurate, reproducible, and individualized patient care.

Dr. Alfonso Reginelli
Guest Editor

Dr. Vittorio Patanè
Guest Editor Assistant

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Keywords

  • radiomics
  • clinical diagnosis
  • medical imaging
  • quantitative imaging biomarkers
  • artificial intelligence
  • machine learning
  • image analysis
  • precision medicine
  • computer-aided diagnosis
  • multimodal imaging
  • feature extraction
  • predictive modeling
  • diagnostic imaging
  • integrative diagnostics
  • imaging biomarkers

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Published Papers (1 paper)

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Research

14 pages, 1906 KB  
Article
AI-Based HRCT Quantification in Connective Tissue Disease-Associated Interstitial Lung Disease
by Anna Russo, Vittorio Patanè, Alessandra Oliva, Vittorio Viglione, Linda Franzese, Giulio Forte, Vasiliki Liakouli, Fabio Perrotta and Alfonso Reginelli
Diagnostics 2025, 15(17), 2179; https://doi.org/10.3390/diagnostics15172179 - 28 Aug 2025
Viewed by 572
Abstract
Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in [...] Read more.
Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy. Full article
(This article belongs to the Special Issue Application of Radiomics in Clinical Diagnosis)
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