Related Experiment Video
Updated: Nov 8, 2025

15:48
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
22.8K
Role of diffusion-weighted MRI in differentiating between benign and malignant bone lesions: a prospective study
T M M Mansour1, M M El-Barody2, H Tammam3
1Radio-diagnosis Department, Faculty of Medicine, Al-Azhar University, Assuit, Egypt.
Clinical Radiology
|April 18, 2021
Summary
Diffusion-weighted MRI effectively differentiates benign and malignant bone tumors using apparent diffusion coefficient (ADC) values. An ADC cutoff range of 0.78-0.86 × 10-3 mm2/s accurately predicts malignancy.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Distinguishing benign from malignant bone tumors is crucial for appropriate patient management.
- Diffusion-weighted magnetic resonance imaging (DW-MRI) offers potential for non-invasive tissue characterization.
Purpose of the Study:
- To evaluate the diagnostic capability of DW-MRI in differentiating benign and malignant bony tumors.
- To determine the effectiveness of apparent diffusion coefficient (ADC) values in this differentiation.
Main Methods:
- Prospective study of 62 patients with suspected bony lesions.
- Inclusion of clinical examination, radiography, CT, ultrasonography, and 1.5-T MRI.
- Post-processing analysis using a Philips Extended MRI workspace workstation.
Main Results:
- Benign lesions showed ADC values ranging from 0.85-2.44 × 10-3 mm2/s.
- Malignant lesions exhibited ADC values between 0.42-2.4 × 10-3 mm2/s, with metastatic lesions averaging 0.71 × 10-3 mm2/s.
- Significant differences were observed in mean, minimum, and maximum ADC values between benign and malignant tumors.
Conclusions:
- DW-MRI, specifically ADC values, can effectively differentiate benign from malignant bone tumors.
- An optimal ADC cut-off range of 0.78-0.86 × 10-3 mm2/s demonstrated high sensitivity (89.47%), specificity (97.22%), and accuracy (94.55%) for predicting malignancy.

