Advances in nano research

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Machine learning-optimized nanomedicine for precision treatment of urological cancers

  • Xinghui Wu (The First Hospital of Changsha, (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University)) ;
  • Han Yan (The First Hospital of Changsha, (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University)) ;
  • Zhanyu Sheng (The First Hospital of Changsha, (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University)) ;
  • Maolin Xiang (The First Hospital of Changsha, (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University)) ;
  • Pengcheng Wang (The First Hospital of Changsha, (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University)) ;
  • Chungang Yan (The Fourth Hospital of Changsha)
  • Received : 2025.12.30
  • Accepted : 2026.02.19
  • Published : 2026.02.25
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Abstract

Nanomedicine was found as a hopeful solution to the targeted therapy of urological cancer which provides greater drug delivery and targeting. Nevertheless, it is difficult to design nanomedicine preparations to deliver the most effective therapies because of the complicated interplay between the biology of tumors and the properties of nanoparticles. This paper presents a machine learning (ML)-inspired methodology to optimization of nanomedicines in urological treatment of cancer. The model can be used to determine the efficacy of different formulations of nanomedicine in the treatment of various stages of urological cancers by combining the predictions of the treatment responses with clinical data including the tumor size, the drug loading and the properties of the nanoparticles. We are showing how ML algorithms can optimize the size of nanoparticles, drug loading and ligand targeting to individual treatment strategies. A cascading of 3D visuals and statistical studies were used to explain the determinants of the important variables that affected treatment reaction as part of tumor size, dose of drug, and the existence of targeting ligands. We have demonstrated that the optimal nanoparticle formulations based on the unique characteristics of tumours enhance therapeutic efficacy and minimise toxicity which can lead to a means of effective and individualised cancer therapy. This paper demonstrates how ML can inform the design of personalized nanomedicines, which will be a major breakthrough in the context of precision oncology of urological cancers.

Keywords

Acknowledgement

Natural Science Foundation of Changsha City (grant kq2208452).

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