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Diagnostics
Special Issues
AI-Enhanced Medical Imaging: A New Era in Oncology
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AI-Enhanced Medical Imaging: A New Era in Oncology

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1847

Special Issue Editors

Dr. Laurent Dercle

E-Mail Website
Guest Editor
Department of Radiology, Columbia University Medical Center and New York Presbyterian Hospital, New York, NY, USA
Interests: artificial intelligence; radiomics; immunotherapy
Special Issues, Collections and Topics in MDPI journals
Dr. Laetitia Vercellino

E-Mail Website
Guest Editor
Department of Nuclear Medicine, Saint-Louis Hospital, AP-HP, Paris, France
Interests: PET imaging; nuclear medicine; oncology; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

"AI-Enhanced Medical Imaging: A New Era in Oncology" presents a comprehensive overview of the dynamic and rapidly evolving field of artificial intelligence in medical imaging for cancer care. This Special Issue brings together a diverse range of contributions that explore the fundamental principles and cutting-edge advancements in AI-enhanced imaging methodologies. From the measurement of tissue dielectric properties and the development of novel imaging techniques, to the modeling of electromagnetic scattering and the experimental validation of these methodologies in both laboratory and in vivo settings, this Special Issue covers all aspects of this exciting research area.

Furthermore, this Special Issue delves into the clinical applications and trials of AI-enhanced medical imaging in oncology. Contributions cover a wide range of topics, including breast cancer imaging, neuroimaging, and the biomedical sensing and monitoring of vital parameters. The Special Issue also includes research on microwave imaging, microwave radiometry, combined imaging modalities, electrical property tomography, and low-frequency imaging methods such as electric impedance tomography, contrast-enhanced imaging, and bioradar. By gathering these diverse contributions, this Special Issue aims to provide a comprehensive and in-depth understanding of the transformative impact of AI on medical imaging in cancer care, ultimately paving the way for a new era of precision medicine.

Dr. Laurent Dercle
Dr. Laetitia Vercellino
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 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-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

  • AI-enhanced medical imaging
  • oncology
  • precision medicine
  • cancer care
  • image analysis
  • diagnostic accuracy
  • therapeutic planning
  • clinical trials
  • experimental validation
  • imaging methodologies
  • electromagnetic scattering
  • microwave imaging
  • combined modalities
  • electrical property tomography
  • low-frequency imaging

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Published Papers (3 papers)

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Research

10 pages, 466 KB  
Article
Patient and Public Perceptions of Artificial Intelligence in Breast Imaging and Clinical Decision-Making: An Exploratory Cross-Sectional Survey Study
by Alia Hussein, Mariam Rizk, Kefah Mokbel and Amtul R. Carmichael
Diagnostics 2026, 16(9), 1376; https://doi.org/10.3390/diagnostics16091376 - 1 May 2026
Abstract
Background/Objectives: Artificial intelligence (AI) shows promise in supporting mammography interpretation and triaging referrals, potentially enhancing breast screening. However, successful AI integration depends on patient acceptance and trust. This study explores patient and public perceptions of AI in breast imaging and clinical decision-making [...] Read more.
Background/Objectives: Artificial intelligence (AI) shows promise in supporting mammography interpretation and triaging referrals, potentially enhancing breast screening. However, successful AI integration depends on patient acceptance and trust. This study explores patient and public perceptions of AI in breast imaging and clinical decision-making to identify knowledge gaps and guide communication strategies. Methods: Paper surveys were distributed to women attending the Breast Care Unit at Queen’s Hospital, Burton, and the London Breast Institute between August and December 2025. Demographic data, levels of trust and comfort with AI, and concerns about AI were collected. Responses were analysed using descriptive statistics, Pearson’s Chi-square tests with Cramér’s V and thematic analysis. Results: One hundred and twenty participants completed the survey. Fifty percent would accept AI alongside clinicians for interpretation of mammograms or ultrasound scans, significantly associated with no previous breast cancer diagnosis (p = 0.02; Cramér’s V = 0.22, 2 degrees of freedom (df)) and technological comfort (p < 0.001; Cramér’s V = 0.42, 1 df). Lower acceptance was found among those with prior diagnosis and low comfort with technology. Acceptance of AI-assisted triage (44.5%) was also significantly associated with technological comfort (p = 0.008; Cramér’s V = 0.30, 1 df). Eighty percent reported no knowledge of AI use in breast clinics, and only 37% would trust AI findings. Qualitative analysis identified three themes: (1) clinician oversight as indispensable, (2) the knowledge gap as a barrier to acceptance, and (3) concerns about operational risks and accountability. Conclusions: Although patients were generally receptive to AI, acceptance was conditional on clinician supervision. Limited awareness and concerns about diagnostic accuracy remain barriers to implementation. Educational initiatives should precede widespread adoption to support informed and confident patient acceptance of AI-assisted imaging and decision-making. Full article
(This article belongs to the Special Issue AI-Enhanced Medical Imaging: A New Era in Oncology)
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11 pages, 2083 KB  
Article
Peritumoral Fat Radiomics for Dual Prediction of TNM Stage and Histological Grade in Clear Cell Renal Cell Carcinoma: Discovery of Target-Specific Optimal Imaging Distances
by Abdulrahman Al Mopti, Abdulsalam Alqahtani, Ali H. D. Alshehri and Ghulam Nabi
Diagnostics 2026, 16(7), 1099; https://doi.org/10.3390/diagnostics16071099 - 5 Apr 2026
Viewed by 478
Abstract
Background/Objectives: Perirenal fat (PRF) constitutes a critical yet understudied component of the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). While radiomics enables non-invasive tissue characterization, whether PRF-derived features can simultaneously predict both TNM stage and histological grade, and whether optimal peritumoral [...] Read more.
Background/Objectives: Perirenal fat (PRF) constitutes a critical yet understudied component of the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). While radiomics enables non-invasive tissue characterization, whether PRF-derived features can simultaneously predict both TNM stage and histological grade, and whether optimal peritumoral distances differ between these distinct biological targets, remains unexplored in the literature. Methods: This multi-cohort retrospective study included 474 histopathologically confirmed ccRCC patients from three independent datasets (2007–2023). Automated nnU-Net segmentation delineated tumors and kidneys. Concentric PRF regions were systematically generated at 1–10 mm radial distances, yielding 18 distinct regions of interest. From each ROI, 1409 radiomic features were extracted using PyRadiomics. Sequential feature selection employed correlation filtering, SHAP-guided elimination, and LASSO regularization. Multiple machine learning classifiers underwent hyperparameter optimization with rigorous cross-cohort validation. Results: Systematic ROI screening revealed target-specific optimal distances: 4 mm PRF for TNM staging versus 10 mm PRF for histological grading. For staging, the integrated model (tumor + PRF radiomics + clinical variables) achieved AUC 0.829 (95% CI 0.781–0.877), sensitivity 80.2%, and specificity 67.8%. For grading, the combined model achieved AUC 0.780 (95% CI 0.598–0.962), sensitivity 79.7%, and specificity 63.3%, significantly outperforming all single-compartment models (DeLong p < 0.001). Conclusions: This study establishes that PRF radiomics enables accurate simultaneous non-invasive prediction of both TNM stage and histological grade in ccRCC. The novel discovery that optimal peritumoral distances differ substantially by prediction target (4 mm versus 10 mm) suggests distinct biological underpinnings for stage- and grade-related microenvironmental alterations, with important methodological implications for radiomic model development in oncology. Full article
(This article belongs to the Special Issue AI-Enhanced Medical Imaging: A New Era in Oncology)
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18 pages, 2654 KB  
Article
Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation
by Xudong Hu, Lingde Feng, Bingzhong Jing, Linna Luo, Wencheng Tan, Yin Li, Xinyi Zheng, Xinxin Huang, Shiyong Lin, Huiling Wu and Longjun He
Diagnostics 2026, 16(2), 215; https://doi.org/10.3390/diagnostics16020215 - 9 Jan 2026
Cited by 1 | Viewed by 644
Abstract
Objectives: To benchmark the performance of DeepSeek-R1 against three other advanced AI reasoning models (GPT-4o, Qwen3, Grok-3) in automatically extracting T/N staging from esophageal cancer endoscopic ultrasound (EUS) complex medical reports, and to evaluate the impact of language (Chinese/English) and prompting strategy (with/without [...] Read more.
Objectives: To benchmark the performance of DeepSeek-R1 against three other advanced AI reasoning models (GPT-4o, Qwen3, Grok-3) in automatically extracting T/N staging from esophageal cancer endoscopic ultrasound (EUS) complex medical reports, and to evaluate the impact of language (Chinese/English) and prompting strategy (with/without designed prompt) on model accuracy and robustness. Methods: We retrospectively analyzed 625 EUS reports for T-staging and 579 for N-staging, which were collected from 663 patients at the Sun Yat-sen University Cancer Center between 2018 and 2020. A 2 × 2 factorial design (Language × Prompt) was employed under a zero-shot setting. The performance of the models was evaluated using accuracy, and the odds ratio (OR) was calculated to quantify the comparative performance advantage between models across different scenarios. Results: Performance was evaluated across four scenarios: (1) Chinese with-prompt, (2) Chinese without-prompt, (3) English with-prompt, and (4) English without-prompt. In both T and N-staging tasks, DeepSeek-R1 demonstrated superior overall performance compared to the competitors. For T-staging, the average accuracy was (DeepSeek-R1 vs. GPT-4o vs. Qwen3 vs. Grok-3: 91.4% vs. 84.2% vs. 89.5% vs. 81.3%). For N-staging, the respective average accuracy was 84.2% vs. 65.0% vs. 68.4% vs. 51.9%. Notably, N-staging proved more challenging than T-staging for all models, as indicated by lower accuracy. This superiority was most pronounced in the Chinese without-prompt T-staging scenario, where DeepSeek-R1 achieved significantly higher accuracy than GPT-4o (OR = 7.84, 95% CI [4.62–13.30], p < 0.001), Qwen3 (OR = 5.00, 95% CI [2.85–8.79], p < 0.001), and Grok-3 (OR = 6.47, 95% CI [4.30–9.74], p < 0.001). Conclusions: This study validates the feasibility and effectiveness of large language models (LLMs) for automated T/N staging from EUS reports. Our findings confirm that DeepSeek-R1 possesses strong intrinsic reasoning capabilities, achieving the most robust performance across diverse conditions, with the most pronounced advantage observed in the challenging English without-prompt N-staging task. By establishing a standardized, objective benchmark, DeepSeek-R1 mitigates inter-observer variability, and its deployment provides a reliable foundation for guiding precise, individualized treatment planning for esophageal cancer patients. Full article
(This article belongs to the Special Issue AI-Enhanced Medical Imaging: A New Era in Oncology)
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