The value of morphological magnetic resonance imaging features combined with diffusion kurtosis imaging for predicting Ki-67 expression levels in high-grade gliomas

The combination of the mMRI and DKI is useful for noninvasive preoperative prediction of HGG Ki-67 expression levels, and the nomogram may help in clinical decision-making for HGG patients.

Authors

Institutions

Publication Details

Journal
Quantitative Imaging in Medicine and Surgery
Published
2025-04-01
DOI
https://doi.org/10.21037/qims-24-2035
Citations
2
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
3.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The value of morphological magnetic resonance imaging features combined with diffusion kurtosis imaging for predicting Ki-67 expression levels in high-grade gliomas

Huan Huang, Guangxiang Chen, Ping Wang, Yang Lu
2 citations
Quantitative Imaging in Medicine and Surgery
Glioma Diagnosis and Treatment
3.25
article

The value of morphological magnetic resonance imaging features combined with diffusion kurtosis imaging for predicting Ki-67 expression levels in high-grade gliomas

Huan Huang, Guangxiang Chen, Ping Wang, Yang Lu
article en
2 citations

Abstract

The combination of the mMRI and DKI is useful for noninvasive preoperative prediction of HGG Ki-67 expression levels, and the nomogram may help in clinical decision-making for HGG patients.

Quantitative Imaging in Medicine and SurgeryVol. 15(4)
Southwest Medical University (CN), Affiliated Hospital of Southwest Medical University (CN)
Zero hunger
Openalex Percentile: Top 10%
Glioma Diagnosis and Treatment
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.