Results 221 to 230 of about 73,359 (292)

Building Hybrid Pharmacometric‐Machine Learning Models in Oncology Drug Development: Current State and Recommendations

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 1, January 2026.
ABSTRACT Classic and hybrid pharmacometric‐machine learning models (hPMxML) are gaining momentum for applications in clinical drug development and precision medicine, especially within the oncology therapeutic area. However, standardized workflows are needed to ensure transparency, rigor, and effective communication for broader adoption.
Anna Fochesato, Logan Brooks, Omid Bazgir, Philippe B. Pierrillas, Candice Jamois, James Lu, Francois Mercier   +6 more
wiley   +1 more source

RadioGuide-DCN: A Radiomics-Guided Decorrelated Network for Medical Image Classification. [PDF]

Bioengineering (Basel)
Guo L, Fu Y, Tan S, Wang Q, Zhang Y, Huang X, Yuan X.   +6 more
europepmc   +1 more source

Multi‐Channel Fusion Residual Network for Robust Bone Fracture Classification From Radiographs

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This research introduces a multi‐channel fusion residual network (MFResNet18) to enhance bone fracture classification from radiographs. By integrating a multi‐modal channel filter with multi‐path early feature extraction, the model enriches fracture‐specific details before deep inference. Experimental results demonstrate a classification accuracy of 99.
Sivapriya T, K. R. Sri Preethaa, Yuvaraj Natarajan, M. Shyamala Devi   +3 more
wiley   +1 more source

The Accuracy in Rupture Risk Prediction of Intracranial Aneurysms by Artificial Intelligence Algorithms Using Imaging Data From CTA and DSA: A Systematic Review and Meta‐Analysis

IET Systems Biology, Volume 20, Issue 1, January/December 2026.
The AI algorithm had good diagnostic accuracy in predicting the risk of IAs rupture based on CTA and DSA data. The multimodal feature integration and temporal improvements highlight the great potential of AI in clinical decision making. It will advance the field of IA rupture risk prediction, which will help guide future studies.
Ruixuan Zhang, Ruibo Liu, He Ma, Guangxin Chu, Ligang Chen, Guobiao Liang, Liang Ma, Hai Jin   +7 more
wiley   +1 more source

Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients. [PDF]

Insights Imaging
Chen W, Lin G, Zhou Y, Chen Y, Shi C, Zhao T, Yan Z, Peng Z, Xia S, Xu M, Chen M, Lu C, Ji J.   +12 more
europepmc   +1 more source

Integration of omics data in the diagnosis and therapy of glioblastoma

Brain Pathology, Volume 36, Issue 1, January 2026.
Integration of omics data in the diagnosis and therapy of glioblastoma. Abstract Since the 2016 update of the WHO Classification of Tumors of the Central Nervous System, omics data have been officially integrated into the diagnostic process for glioblastoma, the most prevalent and aggressive primary malignant brain tumor in adults.
Constantin Möller, Melanie Schoof, Keith L. Ligon, Ulrich Schüller   +3 more
wiley   +1 more source

Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study. [PDF]

EJNMMI Rep
Scavuzzo A, Pasini G, Perez OG, Jimenez Rios MA, Hernández MA, Pedrero-Piedras R, Pérez Montiel MD, Moreno NS, Russo G, Stefano A.   +9 more
europepmc   +1 more source

Radiomics quality score

, 2021
Candace Moore, Henry Knipe, Burak Kocak
openaire   +1 more source

Diagnostic performance of radiomics for detecting and characterising upper tract urothelial carcinoma (UTUC): a systematic review. [PDF]

World J Urol
Bruinsma J, Tharakan N, Temperley HC, Mac Curtain BM, Chau M, Bangash H.   +5 more
europepmc   +1 more source

Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features

, 2019
Usman Bashir, Bhavin Kawa, Muhammad Siddique, Sze Mun Mak, Arjun Nair, Emma McLean, Andrea Billè, Vicky Goh, Gary Cook   +8 more
openalex   +1 more source