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Recent Advances in Imaging and Interventional Techniques for Renal and...
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Recent Advances in Imaging and Interventional Techniques for Renal and Adrenal Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nephrology & Urology".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 427

Special Issue Editors

Dr. Francesco Ziglioli

E-Mail Website
Guest Editor
Department of Urology, Parma University Hospital, via Gramsci 14, 43126 Parma, Italy
Interests: renal cancer; adrenal tumors; prostate; robotic surgery; minimally-invasive techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Umberto Vittorio Maestroni

E-Mail Website
Guest Editor
Department of Urology, University Hospital of Parma, 43126 Parma, Italy
Interests: renal cancer; adrenal tumors; robotic surgery; minimally-invasive techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to present recent advances in the diagnosis and treatment of renal and adrenal pathologies. The impressive refinements in diagnostic and therapeutic techniques in this field make this a topic of the utmost importance. Multimodality and tailoring are key to achieving the successful treatment of these diseases, for which many steps forward have been achieved in recent years. For this reason, original articles that highlight the results of advanced techniques to better characterize and treat these pathologies are welcome, especially those focusing on the technological advancements of imaging, robotic surgery, and minimally invasive techniques.

Review articles (narrative reviews, systematic reviews, and meta-analyses) are also welcome, and research in general with the aim of providing comprehensive knowledge on cutting-edge techniques and minimally invasive treatments will be considered for publication.

Lastly, articles on the potential role of artificial intelligence (AI) and big data, as well as their future perspectives, may be considered for publication in this Special Issue.

Dr. Francesco Ziglioli
Dr. Umberto Vittorio Maestroni
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 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. Journal of Clinical Medicine 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

  • renal cancer
  • adrenal mass
  • robotic surgery
  • minimally invasive techniques
  • radiomics
  • tailored treatment

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Published Papers (1 paper)

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Research

14 pages, 574 KiB  
Article
Ki-67 as a Predictor of Metastasis in Adrenocortical Carcinoma: Artificial Intelligence Insights from Retrospective Imaging Data
by Andrew J. Goulian and David S. Yee
J. Clin. Med. 2025, 14(14), 4829; https://doi.org/10.3390/jcm14144829 - 8 Jul 2025
Viewed by 297
Abstract
Background/Objectives: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with poor prognosis, particularly in metastatic cases. The Ki-67 proliferation index is a recognized marker of tumor aggressiveness, yet its role in guiding diagnostic imaging and surgical decision-making remains underexplored. This study evaluates Ki-67’s [...] Read more.
Background/Objectives: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with poor prognosis, particularly in metastatic cases. The Ki-67 proliferation index is a recognized marker of tumor aggressiveness, yet its role in guiding diagnostic imaging and surgical decision-making remains underexplored. This study evaluates Ki-67’s predictive value for metastasis at diagnosis, leveraging artificial intelligence (AI) to inform personalized, minimally invasive strategies for ACC management. Methods: We retrospectively analyzed 53 patients with histologically confirmed ACC from the Adrenal-ACC-Ki67-Seg dataset in The Cancer Imaging Archive. All patients had Ki-67 indices from surgical specimens and preoperative contrast-enhanced CT scans. Descriptive statistics, t-tests, ANOVA, and multivariable logistic regression evaluated associations between Ki-67, tumor size, age, and metastasis. Random Forest classifiers—with and without the Synthetic Minority Oversampling Technique (SMOTE)—were developed to predict metastasis. A Ki-67-only model served as a baseline comparator. Model performance was assessed using the area under the curve (AUC) and DeLong’s test. Results: Patients with metastatic disease had significantly higher Ki-67 indices (mean 39.4% vs. 21.6%, p < 0.05). Logistic regression identified Ki-67 as the sole significant predictor (OR = 1.06, 95% CI: 1.01–1.12). The Ki-67-only model achieved an AUC of 0.637, while the SMOTE-enhanced Random Forest achieved an AUC of 0.994, significantly outperforming all others (p < 0.001). Conclusions: Ki-67 is significantly associated with metastasis at ACC diagnosis and demonstrates independent predictive value in regression analysis. However, integration with machine learning models incorporating tumor size and age significantly improves overall predictive accuracy, supporting AI-assisted risk stratification and precision imaging strategies in adrenal cancer care. Full article
(This article belongs to the Special Issue Recent Advances in Imaging and Interventional Techniques for Renal and Adrenal Diseases)
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