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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Hossam Magdy Balaha¹, Sarah M Ayyad², Ahmed Alksas¹

¹Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.

Bioengineering (Basel, Switzerland)

|June 27, 2024

Summary

This study developed a non-invasive computer-aided diagnosis system using intravoxel incoherent motion (IVIM) imaging for prostate cancer (PCa) detection. The system achieved 84.08% accuracy, showing promise for improved early diagnosis.

Keywords:

U-Net segmentation apparent diffusion coefficient (ADC)computer- aided diagnosis (CAD)intravoxel incoherent motion (IVIM)machine learning (ML)prostate cancer (PCa)

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Area of Science:

Medical Imaging
Oncology
Computer-Aided Diagnosis

Background:

Prostate cancer (PCa) presents a significant health challenge with high mortality and economic burden.
Early detection is critical for improving patient outcomes in PCa.
Intravoxel incoherent motion (IVIM) imaging offers non-invasive insights into tumor microvasculature and diffusion characteristics.

Purpose of the Study:

To develop and evaluate a non-invasive computer-aided diagnosis (CAD) system for prostate cancer (PCa) detection and diagnosis.
To leverage intravoxel incoherent motion (IVIM) parameters for enhanced PCa characterization.
To compare the diagnostic performance of IVIM parameters against the apparent diffusion coefficient (ADC).

Main Methods:

A two-step segmentation approach using three U-Net architectures was employed to extract tumor regions of interest (ROIs).
Intravoxel incoherent motion (IVIM) parameters were analyzed for their diagnostic value in differentiating PCa.
The Random Forest Classifier (RFC) was utilized to evaluate the optimal combination of IVIM parameters and classifier for PCa diagnosis.

Main Results:

The combination of central zone (CZ) and peripheral zone (PZ) IVIM features with the Random Forest Classifier (RFC) demonstrated optimal performance.
The developed CAD system achieved an overall accuracy of 84.08% and a balanced accuracy of 82.60%.
High sensitivity (93.24%), reasonable specificity (71.96%), good precision (81.48%), and an F1 score of 86.96% were reported.

Conclusions:

The proposed CAD system effectively segments and diagnoses PCa using IVIM parameters and machine learning.
This non-invasive approach shows significant potential for early detection and diagnosis of PCa.
The findings suggest that IVIM parameters, when combined with machine learning, can enhance PCa diagnostic capabilities, potentially improving patient outcomes and reducing healthcare costs.