Detection of Prostate Cancer Using Temporal Sequences of Ultrasound Data:

A Large Clinical Feasibility Study

S. Azizi1, F. Imani1, S. Ghavidel2, A. Tahmasebi3, J. T. Kwak4, S. Xu4, B. Turkbey4, P. Choyke4, P. Pinto4, B. Wood4, P. Mousavi2, and P. Abolmaesumi1

1The University of British Columbia, Vancouver, BC, Canada

2School of Computing, Queen's University, Kingston, ON, Canada 3Philips Research North America, Cambridge, MA, United States

4National Institutes of Health, Bethesda, MD, United States

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Prostate Cancer (PCa)

1 in 7 men will be diagnosed with PCa in his lifetime.

Accurate detection of high grade cancer is essential for clinical management.

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Prostate Specific Antigen (PSA)

Digital Rectal Examination (DRE)

Core Needle Biopsy

Images from book “Fast Facts: Prostate Cancer ” (7th edition) by Kirby, Roger S. Azizi et al.

Objective

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MR/TRUS Fusion New Technology

?

MR Target

Unnecessary Biopsy Accuracy

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Ultrasound-based Tissue Typing

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Doppler imaging [Potdevin’01][Tang’03][Goosen’03][Nelson’07][Xie’13]

Results are variable across centres.

Elastography [Ophir’00][Pallewin’07][Miyagava’09][Correas’11]

Not all cancer are stiff and all stiff lesions are not cancerous.

Spectral and texture-based [Scheipers’03][Lizzi’03][Feleppa’07,09,15]

Limited success based on the analysis of a single TRUS image.

Images from [Correas’11,13] S. Azizi et al.

Temporal Enhanced Ultrasound (TeUS)

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Cancer

Benign

Feature Learning

Classification

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Data Acquisition

We used Philips UroNav during MRI-guided targeted TRUS biopsies.

Data was acquired at the National Institutes of Health (NIH), Maryland.

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MR-Ultrasound fusion (UroNav)

Taking biopsy & histological processing

Temporal Enhanced US data

GS 3+2 GS 3+3 GS 4+3 GS 4+4 GS 5+4 MR Target

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Data Division

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Distance to segmented prostate boundary

Agreement between Pathologies

Axial: GS 3+3

Sagittal: GS 4+4

(Ground Truth)

d = 11 mm d = 7 mm d = 3 mm d = 1 mm UroNav MR/US fusion system

registration accuracy: 2.4 ± 1.2 mm

Distance to boundary ≥ 3 mm

Target

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Data Division

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Distance to boundary more than 3 mm

Axial and sagittal pathologies agreement

D2-A 156 115

D2-C 168 115

D1 32 27

255 Cores

158 Patients

Trai

nin

g Te

st D

atas

et

D2-B 117 91 D2-B

D2-A

D2-C

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Method: Automatic Feature Learning

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Training Dataset RF Time Series 50 Spectral Features

Histopathology Results Binary Labels

20 ROIs

Target

Deep Belief Network (DBN)

Visible layer Hidden Layers

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Method: Training Classifier

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Training Dataset RF Time Series 50 Spectral Features

Histopathology Results Binary Labels

20 ROIs

Target

Deep Belief Network (DBN)

Learned Features

Support Vector Machine (SVM) Classifier

Benign Cancerous

Pc = 0 Pc = 1.0

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Method: Test on a new subject

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Test Data RF Time Series 50 Spectral Features

Trained DBN

Learned Features

Trained SVM

Cancer Likelihood Map

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Results: MR Aggressiveness Level

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AUC = 0.89 at 5 mm distance for moderate MR suspicion level.

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0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

All MR levels Moderate MR High MR

Are

a U

nd

er

the

RO

C C

urv

e (

AU

C)

D2-C: Bi-plane Matching PathologyD2-A: d > 3 mmD2-B: A Ʌ C

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Results: Tumor Size

AUC of 0.77 for cores with MR-tumor-size smaller than 1.5 cm

AUC of 0.93 for cores with MR-tumor-size larger than 2 cm.

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Results: Gleason Scores

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0

2

4

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10

12

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GS 3+3 GS 3+4 GS 4+3 GS 4+4 GS 4+5

Nu

mb

er o

f C

ore

s

Pathology

Correctly Identified

Misclassified

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Test Dataset: Benign Cores

RF Time Series

50 Spectral Features

Test Dataset: Cancerous Cores

RF Time Series

50 Spectral Features

Results: Feature Visualization

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Trained DBN

Trained DBN

Back Propagation

Back Propagation

Absolute Difference Differentiating

Frequencies

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Results: Feature Visualization

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First neuron in the third hidden layer Sixth neuron in the third hidden layer

Top two most differentiating features:

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TeUS Simulation

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Colormaps

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Low MR Level, Benign Target High MR Level, Cancerous Target

Moderate MR Level, Benign Target Moderate MR Level, Cancerous Target

Target Target

Target

Target

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Summary and Future Works

Using temporal enhanced US in a fusion prostate biopsy study an AUC of 0.80 for moderately scored mp-MRI targets is achieved.

Temporal enhanced US combined with mp-MRI has the potential to reduce the number of unnecessary biopsies.

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0.7

0.72

0.74

0.76

0.78

0.8

0.82

0.84

All MR levels Moderate MR High MR

Area Under the Curve (AUC) for different MR suspicion levels

MR Target

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Summary and Future Works

Tissue-dependent features of the prostate can be automatically extracted and classified using deep learning techniques.

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MR Target

S. Azizi, et al., “Classifying Cancer Grades Using Temporal Ultrasound for Transrectal Prostate Biopsy,” MICCAI 2016.

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Thank You

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