eprintid: 10204523
rev_number: 9
eprint_status: archive
userid: 699
dir: disk0/10/20/45/23
datestamp: 2025-02-11 13:32:52
lastmod: 2025-02-11 13:32:52
status_changed: 2025-02-11 13:32:52
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Tang, Jiajia
creators_name: Zhang, Jie
creators_name: Li, Yang
creators_name: Hu, Yongzhi
creators_name: He, Doudou
creators_name: Ni, Hao
creators_name: Zhang, Jiulou
creators_name: Wu, Feiyun
creators_name: Tang, Yuxia
creators_name: Wang, Shouju
title: Interpretable Radiomics Model Predicts Nanomedicine Tumor Accumulation Using Routine Medical Imaging
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F59
keywords: artificial intelligence, machine learning, nanomedicine accumulation, radiomics
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2025-02-07
date_type: published
publisher: Wiley
official_url: https://doi.org/10.1002/adma.202416696
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2359679
doi: 10.1002/adma.202416696
lyricists_name: Ni, Hao
lyricists_id: HNIXX56
actors_name: Ni, Hao
actors_id: HNIXX56
actors_role: owner
full_text_status: restricted
publication: Advanced Materials
issn: 0935-9648
citation:        Tang, Jiajia;    Zhang, Jie;    Li, Yang;    Hu, Yongzhi;    He, Doudou;    Ni, Hao;    Zhang, Jiulou;             ... Wang, Shouju; + view all <#>        Tang, Jiajia;  Zhang, Jie;  Li, Yang;  Hu, Yongzhi;  He, Doudou;  Ni, Hao;  Zhang, Jiulou;  Wu, Feiyun;  Tang, Yuxia;  Wang, Shouju;   - view fewer <#>    (2025)    Interpretable Radiomics Model Predicts Nanomedicine Tumor Accumulation Using Routine Medical Imaging.                   Advanced Materials        10.1002/adma.202416696 <https://doi.org/10.1002/adma.202416696>.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10204523/3/Ni_AM_Manuscript-clear-accepted%20version.pdf