@article{discovery10204523,
         journal = {Advanced Materials},
            year = {2025},
       publisher = {Wiley},
           title = {Interpretable Radiomics Model Predicts Nanomedicine Tumor Accumulation Using Routine Medical Imaging},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           month = {February},
             url = {https://doi.org/10.1002/adma.202416696},
            issn = {0935-9648},
        abstract = {Accurately predicting nanomedicine accumulation is critical for guiding patient stratification and optimizing treatment strategies in the context of precision medicine. However, non-invasive prediction of nanomedicine accumulation remains challenging, primarily due to the complexity of identifying relevant imaging features that predict accumulation. Here, a novel non-invasive method is proposed that utilizes standard-of-care medical imaging modalities, including computed tomography and ultrasound, combined with a radiomics-based model to predict nanomedicine accumulation in tumor. The model is validated using a test dataset consisting of seven tumor xenografts in mice and three sizes of gold nanoparticles, achieving an area under the receiver operating characteristic curve of 0.851. The median accumulation levels of tumors predicted as "high accumulators" are 2.69 times greater than those predicted as "low accumulators". Analysis of this machine-learning-driven interpretable radiomics model revealed imaging features that are strongly correlated with dense stroma, a recognized biological barrier to effective nanomedicine delivery. Radiomics-based prediction of tumor accumulation holds promise for stratifying patient and enabling precise tailoring of nanomedicine treatment strategies.},
        keywords = {artificial intelligence, machine learning, nanomedicine accumulation, radiomics},
          author = {Tang, Jiajia and Zhang, Jie and Li, Yang and Hu, Yongzhi and He, Doudou and Ni, Hao and Zhang, Jiulou and Wu, Feiyun and Tang, Yuxia and Wang, Shouju}
}