Advancing the Diagnosis and Treatment of Urological Diseases through Big Data

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Editor: Hao Chi

Submission Deadline: 28 February 2026 (Status: Open)

Special Issue Editor

Dr. Hao Chi Email | Website
John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
Interests: oncology; urologic diseases; multi-omics; precision medicine; immune microenvironment; machine learning; bioinformatics

Special Issue Information

Dear Colleagues,

Big data analytics has emerged as a transformative force in modern medicine, providing unprecedented insights into urological diseases, refining diagnostic precision, and optimizing therapeutic strategies. Integrating multi-source data, including genomics, proteomics, clinical records, imaging data, and real-world evidence, facilitates biomarker discovery, disease progression prediction, and personalized therapeutic development.

This Special Issue focuses on groundbreaking research elucidating the transformative role of big data in enhancing the diagnosis and therapeutic management of urological diseases. We encourage submissions that explore innovative applications of big data analytics, including machine learning, artificial intelligence, and network-based methodologies, to address key challenges in urology.

This Special Issue will cover a broad range of topics related to the application of big data in urology, including but not limited to:

Innovative Approaches in Big Data Analytics for Urological Disease Diagnosis

Multi-Omics Integration in Urological Diseases

Predictive Models for Treatment Response in Urology

Personalized Therapeutic Strategies for Urological Conditions

Artificial Intelligence in Urological Clinical Decision-Making

Biomarker Discovery through Big Data in Urological Diseases

Hao Chi
Guest Editor

Keywords

big data; urologic diseases; precision medicine; multi-omics integration; machine learning; biomarker

Manuscript Submission Information

Manuscripts should be submitted via our online editorial system at https://www.aeurologia.com/journalx_aedu/authorLogOn.action by registering and logging in to this website. Once you are registered, click here to start your submission. Manuscripts can be submitted now or up until the deadline. All papers will go through peer-review process. Accepted papers will be published in the journal (as soon as accepted) and meanwhile listed together on the special issue website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts will be thoroughly refereed through a double-blind peer-review process. Please visit the Instruction for Authors page before submitting a manuscript. Submitted manuscripts should be well formatted in good English.

Journal

Archivos Españoles de Urología (1)

Publication year

2026 (1)

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Review (1)

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Review
Artificial Intelligence and Big Data in Urological Oncology: From Radiomics to Real-World Evidence

Stamatios Katsimperis, Lazaros Tzelves, Ioannis Kyriazis, Panagiotis Neofytou, Sotirios Kapsalos-Dedes, Georgios Feretzakis, Andreas Skolarikos

Archivos Españoles de Urología. 2026, 79(1): 1-12. https://doi.org/10.56434/j.arch.esp.urol.20267901.1

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Background: Artificial intelligence (AI) and big data are transforming urological oncology by enhancing diagnostic precision, prognostic assessment and treatment personalisation for prostate, bladder and kidney cancer.

Methods: We searched PubMed and MEDLINE up to September 2025 for English-language, peer-reviewed human studies using terms including “artificial intelligence”, “deep learning”, “radiomics”, “real-world evidence” and “urological oncology”.

Results: AI-driven radiomics and deep learning models have demonstrated high accuracy in detecting and characterising urological malignancies by using magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) and histopathology. In prostate, bladder and kidney cancers, AI-driven radiomics and deep learning models have demonstrated high diagnostic performance, with reported area under the curves (AUCs) typically ranging from 0.80 to 0.95 for lesion detection, staging and risk stratification. Sensitivities and specificities in cystoscopic image analysis often exceed 90%, but radiogenomic models for renal cancer achieve mutation prediction accuracies of 85%–95%.

Conclusions: AI and big data are reshaping urological oncology by integrating diagnostic imaging, pathology and real-world practice. Their continued integration promises a precise, equitable and adaptive model of cancer care. Despite these robust results, most studies rely on retrospective or single-centre datasets with limited external validation, raising concerns about generalisability. Future progress will depend on multicentre standardisation, federated learning frameworks and incorporation of multimodal real-world data to facilitate clinically robust and implementable AI systems.

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