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Related Experiment Video

Updated: Jun 28, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

A novel method for analyzing DSCE-images with an application to tumor grading.

Johannes Slotboom¹, Ralph Schaer, Christoph Ozdoba

¹Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland. johannes.slotboom@insel.ch

Investigative Radiology

|November 13, 2008

Summary

A new Dynamic pixel intensity Histogram Analysis (DHA) method effectively grades gliomas using MRI data. The histogram width parameter shows the strongest correlation with histologic grade, aiding in differentiating tumor severity.

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Area of Science:

Radiology and Medical Imaging
Oncology
Biophysics

Background:

Glioma grading is crucial for treatment planning and prognosis.
Dynamic-susceptibility-contrast-enhanced (DSCE) MRI provides valuable information on tumor vascularity.
Novel quantitative analysis methods are needed to improve glioma grading accuracy.

Purpose of the Study:

To develop and validate a novel analysis method, Dynamic pixel intensity Histogram Analysis (DHA), for processing DSCE-MRI time-series.
To evaluate the efficacy of DHA in prospective glioma grading.

Main Methods:

DHA involves computing and fitting pixel intensity histograms of arbitrary-shaped regions in DSCE-MRI difference-image time-series.
The Levenberg-Marquardt algorithm was used for fitting.
Time-dependent histogram parameters (center-position and width) were analyzed during bolus passage in 25 glioma patients.

Main Results:

Histogram center-position and width parameters showed significant discrimination between glioma grades during bolus outflow.
The histogram width parameter demonstrated high significance in differentiating grade-II from grade-III (P < 0.00022), grade-II from grade-IV (P < 8.3x10⁻⁶), and grade-III from grade-IV (P < 0.00063) gliomas.

Conclusions:

Dynamic pixel intensity Histogram Analysis (DHA) is an accessible method for glioma grading.
The histogram width parameter is identified as the most reliable indicator for histologic glioma grade.