Rakesh Shiradkar1, Ruyuan Zuo1, Amr Mahran2, Lin Li1, Britt Conroy2,3, Lee Ponsky2,3, Sree Harsha Tirumani2,3, and Anant Madabhushi1
1Case Western Reserve University, Cleveland, OH, United States, 2UH Cleveland Medical Center, Cleveland, OH, United States, 3Case Western Reserve University School of Medicine, Cleveland, OH, United States
Synopsis
Serial MRI allows for
non-invasive monitoring of prostate cancer patients on Active Surveillance
(AS). However, repeat biopsies continue to be defacto standard for AS
monitoring due to limitations of MRI. In this study, we sought to compute delta
changes in radiomic features on serial bi-parametric MRI and evaluate their
associations with biopsy upgrading. We observed that delta radiomic features
that quantify underlying spatial, gradient based heterogeneity were associated
with biopsy upgrading. On univariable and multivariable analysis with routine
clinical variables, we observed that none of the clinical variables were
significant while delta radiomic classifier predictions were significant and
independently predictive.
Introduction
Active Surveillance (AS) is
increasingly emerging1 as a viable option for prostate
cancer (PCa) patients who are at low risk of disease progression and do not
need immediate treatment. AS involves periodic monitoring of patients via
repeat prostate specific antigen (PSA) measurements and biopsies, which however
are expensive and cause immense discomfort2. Serial MRI3 may allow for non-invasive
monitoring of AS patients and has been shown to add incremental benefit. Bi-parametric
MRI (bpMRI) including T2-weighted (T2W) and diffusion weighted sequences (DWI)
is gaining importance4 as a rapid imaging protocol
for PCa assessment. Radiomics from prostate bpMRI quantify the underlying
heterogeneity and have been shown to be associated with PCa aggressiveness5–7. In this study, we
hypothesize that differences in radiomics or delta radiomics (DeRads) across
serial MRI may capture signatures associated with progression of PCa. The goal
of this study is to evaluate associations between DeRads and upgrading on systematic
biopsy following initial screening of PCa patients on AS.Methods
This retrospective, IRB approved
and HIPAA compliant study comprised N =
32 PCa patients on AS who underwent a screening 3T multi-parametric MRI (mpMRI)
scan followed by a second MRI scan (mean interval = 16 months). They underwent
transrectal, ultrasound guided systematic biopsy after screening MRI and
subsequently when presented with elevated PSA levels. Patients were considered
to have a biopsy upgrade (AS+) when their subsequent biopsy resulted in higher
Gleason Grade Group (GGG) and no upgrade (AS–) when their GGG remained the same
or lower. Biopsy used to determine upgrading was after or close to the second
MRI scan. An experienced radiologist delineated PCa regions of interest (ROIs)
on bpMRI from baseline and second MRI scan. Radiomic features including 1st
and 2nd order statistics, Haralick, Gabor, Co-occurrence of local
anisotropic gradient orientations (CoLlAGe8) and Laws were extracted from
ROIs and distribution statistics (mean, variance, skewness and kurtosis) of
radiomics within ROIs were computed. DeRads were computed as difference between
radiomic features within ROIs on serial bpMRI scans (screening and second MRI).
Support vector machine (SVM) classifier (CΔ) was trained with DeRads in a
3-fold cross validation framework. Association between DeRads and biopsy upgrading
was assessed for statistical significance (p<0.05)
using Wilcoxon rank-sum test and classification performance in terms of area
under the receiver operating characteristics (AUC) curve. Comparison with
routine clinical variables including age, PSA, PIRADS-v2 (Prostate Imaging Reporting
and Data System), GGG and race was performed in terms of univariable and
multivariable analysis.Results
Of the N = 32 patients, 18
experienced biopsy upgrading and 14 had no upgrade. Radiomic features including
CoLlAGe from T2W and gradient, Haralick from ADC showed statistically
significant differences between AS+ and AS– patients (Figure 1). Machine
learning classifiers trained using radiomic features from baseline (CT1) and subsequent MRI (CT2), resulted in
cross-validation AUCs of 0.83, 95% Confidence Interval (CI): (0.62-0.94 CI) and
0.92, 95% CI: (0.76-0.97) respectively in distinguishing AS+ and AS– patients.
Classifier CΔ resulted in cross-validation AUC = 0.94, 95%CI:
(0.80-0.98) which was significantly higher (p<0.05)
than CT1 and CT2 (Figure 2). On
univariable analysis, none of the clinical variables were able to significantly
differentiate AS+ and AS– patients. On multivariable analysis, CΔ was a significant
predictor of biopsy upgrading and provided independent value compared to other
clinical variables (Figure 2).Discussion
Radiomic features
quantify underlying heterogeneity on imaging that is not discernible on routine
imaging. Changes in radiomic feature of prostate cancer lesions or delta
radiomics (DeRads) may help quantify changes in tissue morphology that could
potentially capture information related to disease progression in prostate cancer (PCa) patients on Active Surveillance (AS). We observed
DeRads to show significant differences between patients with (AS+) and without (AS–)
biopsy upgrading. CoLlAGe features quantify intensity gradient based
heterogeneity while gradient and Haralick features quantify spatial intensity
distribution based heterogeneity. As shown in Figure 3, there was increased
heterogeneity in AS+ patients on second MRI compared to AS- patients. A
majority of significant DeRads features were derived from ADC maps which was
also observed in previous studies9,10 that showed texture analysis
of ADC maps to be beneficial in predicting pathological upgrading of PCa
lesions. The classification performance using DeRads was significantly better
than using radiomics from baseline or second MRI scan individually. This
suggests that change in radiomic features may be better associated with biopsy
upgrading than individual MRI scans. On univariable analysis, PIRADS-v2 from
baseline MRI was the only clinical variable with high AUC and was nearly
significant (p=0.08). A recent study11 has also
shown that higher PIRADS is associated with increased risk of biopsy upgrading.
We observed that delta radiomics classifier predictions were significant and also
independent predictor of upgrading on multivariable analysis further suggesting
that DeRads may encode signatures associated with disease progression that may
not be captured by routine clinical variables. Our study is limited by a
smaller sample size, lack of an independent validation set and variations in
follow up of AS patients. These will be addressed in our future work which is
part of our ongoing study.Conclusion
Delta radiomics features derived from serial bi-parametric MRI
of the prostate may be predictive of biopsy upgrading in Active Surveillance
patients.Acknowledgements
Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers 1U24CA199374-01, R01CA202752-01A1R01CA208236-01A1R01 CA216579-01A1R01 CA220581-01A11U01 CA239055-01
National Institute for Biomedical Imaging and Bioengineering 1R43EB028736-01
National Center for Research Resources under award number 1 C06 RR12463-01
VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service
The DoD Breast Cancer Research Program Breakthrough Level 1 Award W81XWH-19-1-0668
The DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558)
The DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440)The DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329)
The Ohio Third Frontier Technology Validation FundThe Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and The Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.
The DoD Prostate Cancer Research Program Idea Development Award W81XWH-18-1-0524
References
- Chen RC, Rumble RB,
Loblaw DA, Finelli A, Ehdaie B, Cooperberg MR, Morgan SC, Tyldesley S,
Haluschak JJ, Tan W, Justman S, Jain S. Active Surveillance for the Management
of Localized Prostate Cancer (Cancer Care Ontario Guideline): American Society
of Clinical Oncology Clinical Practice Guideline Endorsement. J Clin Oncol.
2016 Jun 20;34(18):2182–2190. doi:10.1200/JCO.2015.65.7759
-
Brooks
JV, Ellis SD, Morrow E, Kimminau KS, Thrasher JB. Patient Factors That
Influence How Physicians Discuss Active Surveillance With Low-Risk Prostate
Cancer Patients: A Qualitative Study. Am J Mens Health. 2018;12(5):1719–1727.
doi:10.1177/1557988318785741 PMID: 29973123 PMCID: PMC6142114
-
Felker
ER, Wu J, Natarajan S, Margolis DJ, Raman SS, Huang J, Dorey F, Marks LS.
Serial Magnetic Resonance Imaging in Active Surveillance of Prostate Cancer:
Incremental Value. J Urol. 2016 May;195(5):1421–1427.
doi:10.1016/j.juro.2015.11.055
-
Steinkohl
F, Pichler R, Junker D. Short review of biparametric prostate MRI. Memo.
2018;11(4):309–312. doi:10.1007/s12254-018-0458-1 PMID: 30595756 PMCID:
PMC6280777
-
Shiradkar
R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS, Madabhushi A. Radiomic
features from pretreatment biparametric MRI predict prostate cancer biochemical
recurrence: Preliminary findings. J Magn Reson Imaging JMRI. 2018 May 7;
doi:10.1002/jmri.26178 PMID: 29734484
-
Ginsburg
S, Ali S, Lee G, Basavanhally A, Madabhushi A. Variable importance in nonlinear
kernels (VINK): classification of digitized histopathology. Med Image Comput
Comput-Assist Interv MICCAI Int Conf Med Image Comput Comput-Assist Interv.
2013. p. 238–245. PMID: 24579146
-
Viswanath
SE, Bloch NB, Chappelow JC, Toth R, Rofsky NM, Genega EM, Lenkinski RE,
Madabhushi A. Central gland and peripheral zone prostate tumors have
significantly different quantitative imaging signatures on 3 Tesla endorectal,
in vivo T2-weighted MR imagery. J Magn Reson Imaging JMRI. 2012 Jul;36(1):213–224.
doi:10.1002/jmri.23618 PMID: 22337003 PMCID: PMC3366058
- Prasanna
P, Tiwari P, Madabhushi A. Co-occurrence of Local Anisotropic Gradient
Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep. 2016 Nov
22;6:37241. doi:10.1038/srep37241 PMID: 27872484 PMCID: PMC5118705
-
Rozenberg
R, Thornhill RE, Flood TA, Hakim SW, Lim C, Schieda N. Whole-Tumor Quantitative
Apparent Diffusion Coefficient Histogram and Texture Analysis to Predict
Gleason Score Upgrading in Intermediate-Risk 3 + 4 = 7 Prostate Cancer. AJR
Am J Roentgenol. 2016 Apr;206(4):775–782. doi:10.2214/AJR.15.15462
PMID: 27003049
-
Sadoughi
N, Krishna S, McInnes MDF, Flood TA, Breau RH, Morash C, Schieda N. ADC Metrics
From Multiparametric MRI: Histologic Downgrading of Gleason Score 9 or 10
Prostate Cancers Diagnosed at Nontargeted Transrectal Ultrasound-Guided Biopsy.
AJR Am J Roentgenol. 2018;211(3):W158–W165.
doi:10.2214/AJR.17.18958 PMID: 29995495
- Kornberg
Z, Cowan JE, Westphalen AC, Cooperberg MR, Chan JM, Zhao S, Shinohara K,
Carroll PR. Genomic Prostate Score, PI-RADSTM version 2 and
Progression in Men with Prostate Cancer on Active Surveillance. J Urol.
2019;201(2):300–307. doi:10.1016/j.juro.2018.08.047 PMID: 30179620