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01-09-2020 | Prostate Cancer | Urogenital

Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy

Authors: Marc R. Liechti, Urs J. Muehlematter, Aurelia F. Schneider, Daniel Eberli, Niels J. Rupp, Andreas M. Hötker, Olivio F. Donati, Anton S. Becker

Published in: European Radiology | Issue 9/2020

Abstract

Objectives

To assess interreader agreement of manual prostate cancer lesion segmentation on multiparametric MR images (mpMRI). The secondary aim was to compare tumor volume estimates between MRI segmentation and transperineal template saturation core needle biopsy (TTSB).

Methods

We retrospectively reviewed patients who had undergone mpMRI of the prostate at our institution and who had received TTSB within 190 days of the examination. Seventy-eight cancer lesions with Gleason score of at least 3 + 4 = 7 were manually segmented in T2-weighted images by 3 radiologists and 1 medical student. Twenty lesions were also segmented in apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) series. First, 20 volumetric similarity scores were computed to quantify interreader agreement. Second, manually segmented cancer lesion volumes were compared with TTSB-derived estimates by Bland-Altman analysis and Wilcoxon testing.

Results

Interreader agreement across all readers was only moderate with mean T2 Dice score of 0.57 (95%CI 0.39–0.70), volumetric similarity coefficient of 0.74 (0.48–0.89), and Hausdorff distance of 5.23 mm (3.17–9.32 mm). Discrepancy of volume estimate between MRI and TTSB was increasing with tumor size. Discrepancy was significantly different between tumors with a Gleason score 3 + 4 vs. higher grade tumors (0.66 ml vs. 0.78 ml; p = 0.007). There were no significant differences between T2, ADC, and DCE segmentations.

Conclusions

We found at best moderate interreader agreement of manual prostate cancer segmentation in mpMRI. Additionally, our study suggests a systematic discrepancy between the tumor volume estimate by MRI segmentation and TTSB core length, especially for large and high-grade tumors.

Key Points

• Manual prostate cancer segmentation in mpMRI shows moderate interreader agreement.

• There are no significant differences between T2, ADC, and DCE segmentation agreements.

• There is a systematic difference between volume estimates derived from biopsy and MRI.

Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34PubMed

Schroder FH, Hugosson J, Roobol MJ et al (2014) Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. Lancet 384:2027–2035

Bozzini G, Colin P, Nevoux P et al (2013) Focal therapy of prostate cancer: energies and procedures. Urol Oncol 31:155–167PubMedCrossRef

Fütterer JJ, Briganti A, De Visschere P et al (2015) Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol 68:1045–1053PubMedCrossRef

Kasivisvanathan V, Rannikko AS, Borghi M et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 378:1767–1777PubMedPubMedCentralCrossRef

Ahmed HU, El-Shater Bosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389(10071):815–822. https://doi.org/10.1016/S0140-6736(16)32401-1PubMedCrossRef

Donaldson IA, Alonzi R, Barratt D et al (2015) Focal therapy: patients, interventions, and outcomes - a report from a consensus meeting. Eur Urol 67:771–777PubMedPubMedCentralCrossRef

Khalvati F, Clark T, Haider MA et al (2017) Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. J Med Imaging (Bellingham) 4(4):041307

Cheng R, Roth HR, Lay N et al (2017) Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J Med Imaging (Bellingham) 4(4):041302. https://doi.org/10.1117/1.JMI.4.4.041302CrossRef

10.

Mooij G, Bagulho I, Huisman H (2018) Automatic segmentation of prostate zones. arXiv preprint arXiv:1806.07146

11.

Litjens G, Toth R, van de Ven W et al (2014) Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal 18(2):359–373. https://doi.org/10.1016/j.media.2013.12.002PubMedCrossRef

12.

Cao R, Bajgiran AM, Mirak SA et al (2019) Joint prostate cancer detection and Gleason score prediction in mp-MRI via FocalNet. IEEE Trans Med Imaging 38(11):2496–2506. https://doi.org/10.1109/tmi.2019.2901928PubMedCrossRef

13.

Schelb P, Kohl S, Radtke JP et al (2019) Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology 293(3):607–617. https://doi.org/10.1148/radiol.2019190938PubMedCrossRef

14.

Pierorazio PM, Walsh PC, Partin AW, Epstein JI (2013) Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int 111:753–760PubMedPubMedCentralCrossRef

15.

Barentsz JO, Richenberg J, Clements R et al (2012) ESUR prostate MR guidelines 2012. Eur Radiol 22(4):746–757. https://doi.org/10.1007/s00330-011-2377-yPubMedPubMedCentralCrossRef

16.

Mortezavi A, Märzendorfer O, Donati OF et al (2018) Diagnostic accuracy of multiparametric magnetic resonance imaging and fusion guided targeted biopsy evaluated by transperineal template saturation prostate biopsy for the detection and characterization of prostate cancer. J Urol 200:309–318PubMedCrossRef

17.

Epstein JI, Egevad L, Amin MB et al (2016) The 2014 international society of urological pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma definition of grading patterns and proposal for a new grading system. Am J Surg Pathol 40:244–252PubMed

18.

Becker AS, Cornelius A, Reiner CS et al (2017) Direct comparison of PI-RADS version 2 and version 1 regarding interreader agreement and diagnostic accuracy for the detection of clinically significant prostate cancer. Eur J Radiol 94:58–63. https://doi.org/10.1016/j.ejrad.2017.07.016PubMedCrossRef

19.

Ahmed HU, Hu Y, Carter T et al (2011) Characterizing clinically significant prostate cancer using template prostate mapping biopsy. J Urol 186:458–464PubMedCrossRef

20.

Becker AS, Chaitanya K, Schawkat K et al (2019) Variability of manual segmentation of the prostate in axial T2-weighted MRI: a multi-reader study. Eur J Radiol 121:108716. https://doi.org/10.1016/j.ejrad.2019.108716PubMedCrossRef

21.

Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):29. https://doi.org/10.1186/s12880-015-0068-xPubMedPubMedCentralCrossRef

22.

Piert M, Shankar PR, Montgomery J et al (2018) Accuracy of tumor segmentation from multi-parametric prostate MRI and 18F-choline PET/CT for focal prostate cancer therapy applications. EJNMMI Res 8:1–14CrossRef

23.

Wysock JS, Lepor H (2017) Multi-parametric MRI imaging of the prostate—implications for focal therapy. Transl Androl Urol 6:453–463PubMedPubMedCentralCrossRef

24.

Cussenot O, Boudghene F, Racoceanu D et al (2017) Comparison of semi-automated and manual methods to measure the volume of prostate cancer on magnetic resonance imaging. Diagn Interv Imaging 98:423–428PubMedCrossRef

25.

Merisaari H, Jambor I, Ettala O et al (2019) IMPROD biparametric MRI in men with a clinical suspicion of prostate cancer (IMPROD trial): sensitivity for prostate cancer detection in correlation with whole-mount prostatectomy sections and implications for focal therapy. J Magn Reson Imaging 50(5):1641–1650. https://doi.org/10.1002/jmri.26727PubMedCrossRef

26.

Bettermann AS, Zamboglou C, Kiefer S et al (2019) [68Ga-]PSMA-11 PET/CT and multiparametric MRI for gross tumor volume delineation in a slice by slice analysis with whole mount histopathology as a reference standard – implications for focal radiotherapy planning in primary prostate cancer. Radiother Oncol 141:214–219. https://doi.org/10.1016/j.radonc.2019.07.005PubMedCrossRef

27.

Le Nobin J, Orczyk C, Deng FM et al (2014) Prostate tumour volumes: evaluation of the agreement between magnetic resonance imaging and histology using novel co-registration software. BJU Int 114:E105–E112PubMedPubMedCentralCrossRef

28.

Gibson E, Bauman GS, Romagnoli C et al (2016) Toward prostate cancer contouring guidelines on magnetic resonance imaging: dominant lesion gross and clinical target volume coverage via accurate histology fusion. Int J Radiat Oncol Biol Phys 96:188–196PubMedCrossRef

29.

Song Y, Zhang YD, Yan X et al (2018) Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging 48:1570–1577PubMedCrossRef

30.

Tsehay Y, Lay N, Wang X et al (2017) Biopsy-guided learning with deep convolutional neural networks for prostate cancer detection on multiparametric MRI. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

31.

Yetik IS, Liu X, Wernick MN et al (2010) Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med Phys 37:1873–1883PubMedCrossRef

32.

Tian Z, Liu L, Fei B (2017) Deep convolutional neural network for prostate MR segmentation. Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351L. https://doi.org/10.1117/12.2254621

33.

Arumainayagam N, Ahmed HU, Moore CM et al (2013) Multiparametric MR imaging for detection of clinically significant prostate cancer: a validation cohort study with transperineal template prostate mapping as the reference standard. Radiology 268(3):761–769. https://doi.org/10.1148/radiol.13120641PubMedCrossRef

34.

Barth BK, Cornelius A, Nanz D et al (2016) Comparison of image quality and patient discomfort in prostate MRI: pelvic phased array coil vs. endorectal coil. Abdom Radiol (NY) 41:2218–2226CrossRef

35.

Fütterer JJ (2017) Multiparametric MRI in the detection of clinically significant prostate cancer. Korean J Radiol 18:597–606PubMedPubMedCentralCrossRef

Title: Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy
Authors: Marc R. Liechti
Urs J. Muehlematter
Aurelia F. Schneider
Daniel Eberli
Niels J. Rupp
Andreas M. Hötker
Olivio F. Donati
Anton S. Becker
Publication date: 01-09-2020
Publisher: Springer Berlin Heidelberg
Keywords: Prostate Cancer
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Prostate Cancer
Published in: European Radiology / Issue 9/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06786-w

At a glance: The STEP trials

Springer Medicine

Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy

Abstract

Objectives

Methods

Results

Conclusions

Key Points

At a glance: The STEP trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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