Counterfactuals and uncertainty-based explainable paradigm for the automated detection and segmentation of renal cysts in contrast-enhanced abdominal computed tomography: A multi-center study.

Zohaib Salahuddin¹, Sheng Kuang², Abdalla Ibrahim³, Yousif Widaatalla², Razvan L Miclea⁴, Oliver Morin⁵, Spencer Behr⁵, Marnix P M Kop⁶, Tom Marcelissen⁷, Patricia Zondervan⁶, Auke Jager⁶, Philippe Lambin⁸, Henry C Woodruff⁸

¹The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands. Electronic address: z.salahuddin@maastrichtuniversity.nl.

²The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands.

³Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA.

⁴Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands.

⁵Department of Radiation Oncology, University of California, San Francisco, USA.

⁶Department of Urology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.

⁷Department of Urology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, the Netherlands.

⁸The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.

PubMed DOI

Abstract

Contrast-enhanced abdominal computed tomography (CT) scans frequently detect renal cysts, some of which may be malignant. Early and precise localization improves patient management and enables quantitative image analysis. However, current segmentation methods often lack interpretability at both feature and pixel levels, highlighting the need for an explainable approach that can detect and correct…

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MeSH Terms

Humans0Tomography, X-Ray Computed*Generative Adversarial Networks0Kidney Diseases, Cystic*Contrast Media*Radiomics0Uncertainty0Radiographic Image Interpretation, Computer-Assisted*Autoencoder0Imaging, Three-Dimensional0

Keywords

Counterfactual explanationsExplainable artificial intelligence (XAI)RadiomicsSegmentation quality controlUncertainty estimationVariational Autoencoder Generative Adversarial Network (VAE-GAN)

Article Info

PMID: 42721586
DOI: 10.1016/j.artmed.2026.103518
Received: January 20, 2026
Accepted: August 25, 2026

How to Cite

Salahuddin Z et al. Counterfactuals and uncertainty-based explainable paradigm for the automated detection and segmentation of renal cysts in contrast-enhanced abdominal computed tomography: A multi-center study. Artif Intell Med2026;182103518. doi:10.1016/j.artmed.2026.103518

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