Aishah Azam1, Dario Picone2, Mrishta Brizmohun Appayya2, Balaji Ganeshan3, Nikolaos Dikaios2, Raymond Endozo3, Ashley Groves3, Hashim Ahmed4, and Shonit Punwani1
1University College London Hospital, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom, 3Institute of Nuclear Medicine, University College London, London, United Kingdom, 4Division of Surgery, University College London Hospital, London, United Kingdom
Synopsis
Approximately
30% of multiparametricMRIs for peripheral zone prostate cancer are scored as
“equivocal”. Texture analysis was performed on 66 patients with “equivocal”
mpMRIs using TexRad software to see if there were any particular textural differences
between patients with significant cancer and non-significant disease on biopsy.
We found average entropy on ADC sequences of the PZ is reduced in patients with
significant cancer (p=0.003). Using an entropy threshold of <5.99
demonstrates a sensitivity of 0.88 and specificity is 0.60 for detecting
significant PZ prostate cancer. Therefore, ADC entropy can help assess
“equivocal” studies and enable selection of patients for biopsy and
treatment. Target audience:
Radiologists, oncologists,
urologists, physicists with an interest in prostate cancer diagnostics.
Purpose:
Approximately 70% of
prostate cancers arise in the peripheral zone (PZ) (1) and these cancers tend
to be more aggressive than those found in the transition zone (2). The use of multiparametric
Magnetic Resonance Imaging (mpMRI) has transformed the traditional prostate
cancer pathway allowing the accurate localisation of prostate cancer (3). However,
in approximately 30% of patients, the mpMRI study is equivocal for tumour
(PiRADS/Likert score 3/5). It is unclear what to do when a tumour is reported
as “equivocal”, whether to perform a biopsy or to repeat the mpMRI study after
an interval; this has a direct implication for individual patient and the
health economics of prostate mpMRI as a diagnostic technique.
Image texture analysis encompasses
a mathematical approach to evaluate pixel intensity and position. This is then
used to derive textural features including entropy, skewness and kurtosis which
each have biological relevance to heterogeneity (4). Textural analysis has been applied in many
different imaging modalities and has been shown to be able to identify tumours
and even assess prognosis in brain, rectal, breast and liver ca (5-9). Our aim
was to determine whether textural analysis of peripheral zone mp-MRI signal
could help improve classification of ‘equivocal’ reported studies.
Materials and methods:
330 men enrolled in the
PICTURE study (10) underwent mpMRI of the prostate which were all prospectively
scored on a Likert scale by a radiologist with 8 years of experience blinded to
histopathological results. Patients underwent mpMRI using a 3T static magnet with
a specific combination of sequences including: T2 weighted, apparent diffusion
co-efficient (ADC) and dynamic contrast enhancement (DCE) imaging. From the
cohort, 66 patients had prostate mpMRIs scored as “equivocal” (Likert 3/5) and
were eligible for inclusion for textural analysis. All patients subsequently had a targeted
template biopsy of the primary lesion scored 3/5; and irrespective of mpMRI
score completed a full 20-core template biopsy of the remainder of their
prostate (10). In each patient, 3
representative slices (at apex, mid-gland and base for each of DCE [early
post-contrast T1], ADC and T2 weighted images) were selected for textural
analysis.
The peripheral zone was
manually segmented and then textural analysis performed using TexRAD software (TexRAD
Ltd www.texrad.com, Feedback Plc) (5). TexRad
quantified pixel distribution within the segmented PZ of the prostate gland and
this was followed by histogram analysis to generate values for entropy,
skewness and kurtosis. T2, ADC and DCE textural parameters were separately
recorded and averaged (across the 3 slices) for each patient. Textural features
were separately assessed for predicting patients with significant prostate
cancer using receiver operating characteristic (ROC) areas under curve (AUC)
analysis. Significant cancer was defined as the presence of a Gleason 4 disease
component at biopsy.
Results:
16/66 Likert 3/5 reported
patients demonstrated significant cancer at biopsy. 15 patients had gleason
3+4, and 1 patient had Gleason 4+3. The remaining 50 patients with: Gleason
score 3+3 cancers (28 patients) and benign histology (22 patients), were
combined and classified together as non-significant disease. Mean PZ ADC
entropy was significantly lower in patients with significant cancer at biopsy
(5.698 vs. 5.883; p=0.003). There was no significant difference of mean
peripheral zone kurtosis or skewness between patients with and without
significant cancer (p=0.944, p=0.221). There was no significant difference of
any T2 or DCE textural parameters between patients with and without significant
cancer (p=0.051 to 0.742). ROC-AUC of mean peripheral zone ADC entropy
was 0.749 (95% CI: 0.613-0.884). A mean peripheral zone ADC entropy threshold
of <5.99 demonstrates a sensitivity of 0.88 and specificity is 0.60 for
detecting significant PZ prostate cancer.
Discussion:
Entropy is a first-order
statistic measure of texture irregularity and a lower entropy value in patients
with significant cancer suggests a more a homogenous peripheral zone ADC map.
Our results demonstrate that assessing the entropy of ADC of the peripheral
zone in patients that have ‘equivocal’ mpMRI studies could help to further
classify patients by selecting those patients most likely to have significant
cancer for early biopsy and subsequent treatment.
Acknowledgements
This work has been supported by the KCL-UCL Comprehensive Cancer Imaging Centre funding [Cancer Research UK (CR-UK) & Engineering and Physical Sciences Research Council (EPSRC)].The majority of this work was undertaken at University College London Hospital and University College London, which receive a proportion of funding from the NIHR Biomedical Research Centre funding scheme [Department of Health UK].References
1.
McNeal JE, Redwine EA, Freiha FS, Stamey TA. Zonal distribution of prostatic
adenocarcinoma. Correlation with histologic pattern and direction of spread. Am
J Surg Pathol. 1988;(12):897-906.
2.
Erdersdobler A, Fritz H, Schnoger S, Graefen M, Hammerer P, Huland H, et al.
Tumour grade, proliferation, apoptosis, microvessel density, p53, and bcl-2 in
prostate cancers: differences between tumours located in the transition zone
and in the peripheral zone. European Urology. 2002;(1):40-6.
3.
Fütterer JJ, Briganti A, De Visschere P, Emberton M, Giannarini G, Kirkham A,
et al. Can Clinically Significant Prostate Cancer Be Detected with
Multiparametric Magnetic Resonance Imaging? A Systematic Review of the
Literature. European Urology. 2015:1-9.
4.
Davnall F, Connie SPY, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al.
Assessment of tumor heterogeneity: an emerging tool for clinical practice?
Insights imaging. 2012;(3):573-589.
5.
Miles KA, Ganeshan B, Hayball MP. CT tecture analysis using the
filtration-histogram method: what do the measurements mean? Cancer Imaging.
2013;13(3):400-406.
6.
Eliat PA, Olivie D, Saikali S, Carsin B, Saint-Jalmes H, de Certaines JD. Can
dynamic contrast-enhanced magnetic resonance imaging combined with texture
analysis differentiate malignant glioneuronal tumors from other glioblastoma?
Neurol Res Int. 2012.
7.
Mayerhoefer ME, Schima W, Trattnig S, Pinker K, Berger-Kulemann V, Ba-Ssalamah
A. Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a
feasibility study in cysts and hemangiomas. J Magn Reson Imaging 2010;
32(2):352–359.
8.
Lopes R, Ayache A, Makni N et al. Prostate cancer characterization on MR images
using fractal features. Med Phys. 2011; 38(1):83–95.
9.
Parikh J, Selmi M, Charles-Edwards G, Glendenning J, Ganeshan B, Verma H, Mansi
J, Harries M, Tutt A, Goh V. Changes in primary breast cancer heterogeneity may
augment midtreatment MR imaging assessment of response to neoadjuvant
chemotherapy. Radiology 2014; 272(1):100-12.
10.
Simmons LA, Ahmed HU, Moore CM, et al. The PICTURE study -- prostate
imaging (multi-parametric MRI and Prostate HistoScanning™) compared to
transperineal ultrasound guided biopsy for significant prostate cancer risk
evaluation. Contemp Clin Trials. Jan 2014;37(1):69-83.