Optimizing MRI Cytometry: Preliminary Applications and Standard Procedure Enhancements in Cervical Cancer Diagnosis

Motivation: Cervical cancer arises from abnormal cell proliferation, and MRI-cytometry techniques have introduced a novel non-invasive approach to directly evaluate tissue microenvironment, previously unexplored in cervical cancer. Goal(s): To explore the clinical value of MRI cytometry-based parameters in the identification of cervical cancer and further optimize the standard process to obtain better diagnostic efficiency. Approach: MRI cytometry-based parameters are obtained by fitting the IMPULSE and JOINT models from the oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences. Results: MRI cytometry parameters can accurately differentiate cervical cancer from normal cervix, with improved diagnostic performance achieved through full layer mapping and JOINT model fitting. Impact: The MRI cytometry parameters can distinguish cervical cancer from normal cervix by directly capturing the changes in the tissue microstructure. It may further assist in preoperative risk stratification and treatment efficacy evaluation in cervical cancer in the future.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/2176
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing MRI Cytometry: Preliminary Applications and Standard Procedure Enhancements in Cervical Cancer Diagnosis

Diwei Shi, Yonglan He, Xin Gao, Fan Liu et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
AI in cancer detection
article

Optimizing MRI Cytometry: Preliminary Applications and Standard Procedure Enhancements in Cervical Cancer Diagnosis

Diwei Shi, Yonglan He, Xin Gao, Fan Liu, Zhengyu Jin, Jing Ren, Jing Chen, Yuan Li, Zhiyong Wang, Xinyu Liu, Zhilin Yuan, Hua Guo, Huadan Xue
article en

Abstract

Motivation: Cervical cancer arises from abnormal cell proliferation, and MRI-cytometry techniques have introduced a novel non-invasive approach to directly evaluate tissue microenvironment, previously unexplored in cervical cancer. Goal(s): To explore the clinical value of MRI cytometry-based parameters in the identification of cervical cancer and further optimize the standard process to obtain better diagnostic efficiency. Approach: MRI cytometry-based parameters are obtained by fitting the IMPULSE and JOINT models from the oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences. Results: MRI cytometry parameters can accurately differentiate cervical cancer from normal cervix, with improved diagnostic performance achieved through full layer mapping and JOINT model fitting. Impact: The MRI cytometry parameters can distinguish cervical cancer from normal cervix by directly capturing the changes in the tissue microstructure. It may further assist in preoperative risk stratification and treatment efficacy evaluation in cervical cancer in the future.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 87%
AI in cancer detection
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.