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MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study
Hans-Jonas Meyer1, Stefan Schob2, Anne Kathrin Höhn3
1Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
Translational Oncology
|October 9, 2017
Summary
MRI texture analysis shows promise in characterizing thyroid cancer by correlating imaging features with histopathology. This noninvasive technique may aid in predicting tumor characteristics and improving clinical oncology strategies.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Thyroid cancer incidence is rising globally, necessitating advanced diagnostic tools.
- Novel imaging techniques offer potential for tumor characterization and histopathological prediction.
- Texture analysis is an emerging method for extracting detailed information from radiological images.
Purpose of the Study:
- To investigate associations between MRI texture analysis features and histopathology parameters in thyroid cancer.
- To determine if texture analysis can noninvasively reflect tumor characteristics.
Main Methods:
- Retrospective review of 13 thyroid carcinoma patient cases.
- Texture analysis performed using the MaZda program on T1-precontrast and T2-weighted MRI sequences.
- Analysis of 279 texture features per sequence, correlated with cell count, Ki67 index, and p53 count.
Main Results:
- Significant correlations found between texture features and histopathology.
- T1-weighted images showed strong correlations with cell count (e.g., S(0;1)Sum Averg, r=0.82).
- T2-weighted images revealed correlations with Ki67 index (e.g., WavEnHL_s-1, r=-0.77) and p53 count (e.g., S(1;-1)SumEntrp, r=-0.72).
Conclusions:
- MRI texture analysis from conventional sequences effectively reflects histopathological features of thyroid cancer.
- This technique presents a potential novel noninvasive modality for enhanced thyroid cancer characterization in clinical practice.
