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Updated: Apr 29, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

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Published on: March 21, 2025

Prostate Cancer Classification Using Quantum Machine Learning on Multi-parametric MRI.

Peng Chen^1,2, Mojtaba Safari³, Rowan Barker-Clarke¹

¹Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland, OH 44106, USA.

Proceedings of Spie--The International Society for Optical Engineering

|April 28, 2026

Summary

This summary is machine-generated.

Quantum machine learning, specifically amplitude-encoded quantum support vector machines (QSVM), shows promise for classifying prostate cancer lesions. This approach outperformed classical methods in a study using radiomics features from MRI scans.

Keywords:

Multiparametric MRI Prostate Cancer Quantum machine learning Radiomics

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Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Area of Science:

Medical Imaging and Machine Learning
Quantum Computing Applications

Background:

Accurate prostate cancer lesion classification is crucial for patient management.
Multiparametric MRI (T2W and ADC) radiomics features aid diagnosis.
Classical machine learning models (SVM, RF, XGBoost) show promise but may miss complex patterns.

Purpose of the Study:

To compare classical machine learning classifiers with quantum support vector machine (QSVM) variants for prostate cancer lesion classification.
To evaluate the performance of different QSVM encoding strategies (amplitude, angle, projected quantum kernel) in a clinical imaging context.

Main Methods:

Radiomics features were extracted from T2-weighted (T2W) and apparent diffusion coefficient (ADC) MRI images of 299 prostate lesions.
Classifiers compared included SVM-RBF, Random Forests (RF), XGBoost, and three QSVM variants (amplitude encoding, angle encoding, angle encoding with projected quantum kernel).
A nested stratified cross-validation pipeline with feature selection and hyperparameter optimization was utilized.

Main Results:

Amplitude-encoded QSVM achieved the highest mean AUC (0.799 ± 0.082), surpassing SVM-RBF (0.608 ± 0.244).
QSVM performance matched or exceeded RF (0.728 ± 0.083) and XGBoost (0.720 ± 0.065) while offering improved sensitivity at similar specificity.
Quantum machine learning demonstrated competitive or superior performance in this small-sample, low-dimensional clinical imaging dataset.

Conclusions:

Qubit-efficient QSVMs, particularly amplitude encoding, show significant potential for enhancing prostate cancer lesion classification accuracy.
Quantum machine learning offers a valuable alternative for uncovering complex patterns in clinical imaging data that may be missed by classical methods.
These findings support the future integration of quantum computing in diagnostic radiology for improved patient outcomes.