Clinical trial NCT04605276: Threedimensional Multiparametric Ultrasound for Prostate Cancer Detection
Official title: Improving the AI-based Image Analysis Algorithm for 3D Multiparametric Ultrasound for the Detection, Grading and Localization of Prostate Cancer
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Prostate Cancer
Keywords: Contrast Enhanced Ultrasound
CEUS
Machine Learning
Artificial Intelligence
Multiparametric Ultrasound
Lead sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Sponsor class: OTHER
Collaborators: name: Angiogenesis Analytics
First posted: 2020-10-28
Enrollment: 608
Sex: MALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 1. Male patients of age ≥18 suspected for prostate cancer who are scheduled for systematic and/or targeted biopsy after mpMRI examination.
2. patients of age ≥18 with confirmed prostate cancer who are scheduled for radical prostatectomy.

Brief summary: Rationale: Current imaging techniques for the detection and grading of prostate cancer are imperfect, leading to unnecessary biopsies, suboptimal treatment decisions and missed clinically significant cancers. The hypothesis of this study is that computer assisted analysis of 3D multiparametric ultrasound (mpUS) images can accurately detect, grade and localize prostate cancer. 3D mpUS may then become a more cost-effective and more streamlined imaging strategy than the current standard: mpMRI.

Objective: The primary objective is to collect high-quality 3D mpUS and histology data, to train and improve the classifier algorithm with the goal of achieving an accurate ultrasound imaging tool for the detection of clinically significant prostate cancer.

Secondary objectives are related to the preliminary assessment of the performance of 3D mpUS with computer assisted analysis.

Study design: This is a prospective, multi-center study in men with a suspicion of prostate cancer who are scheduled for prostate biopsies, and men with confirmed prostate cancer who are scheduled to undergo a radical prostatectomy. Prior to prostate biopsies or the radical prostatectomy, 3D mpUS imaging will be performed. The ultrasound images will be analyzed and used for algorithm training using the biopsies and/or prostatecomy specimens as gold standard. Additional research coupes of pathology material (both biopsies and radical prostatectomy specimens) from study subjects will be anonymized and separately analyzed and stored in a central, independent institution. The outcome of the 3D mpUS analysis and the additional pathology evaluation are for research purposes only and will not interfere with standard patient care.

Study population: 1) Male patients of age ≥18 suspected for prostate cancer who are scheduled for systematic and/or targeted biopsy after mpMRI examination.

2) patients of age ≥18 with confirmed prostate cancer who are scheduled for radical prostatectomy.

Main study parameters/endpoints:

* Gleason/Grade group scoring based on histology. Using histology as the reference standard the accuracy of the algorithm will be optimized to be differentiating between benign tissue and various grades of malignancy.
* Localization and size of lesions at full-gland histology in the subset of patients undergoing radical prostatectomy. Correlation in tumour size and location will be optimized between 3D mpUS findings and histology of the full gland.

For the secondary objective, preliminary assessment of the performance of 3D mpUS, the following endpoints are evaluated

* Among all clinically significant detected cancers confirmed by histology, the proportion of these cancers that would have been detected by 3D mpUS will be calculated. The number of false positive findings by 3D mpUS both as an absolute count and expressed as a mean rate per patient.
* The concordance in the detection and grading of abnormalities between mpMRI and 3D mpUS by examining the frequency and type of disagreements and calculating the kappa statistic.


Eligibility criteria: Inclusion criteria

In order to be eligible to participate in this study, a subject must meet all of the following criteria:

* Men ≥18 years with a clinical suspicion of prostate cancer or confirmed prostate cancer.
* Scheduled for either systematic and/or targeted biopsy after mpMRI examination or radical prostatectomy
* Signed informed consent

Exclusion criteria

A potential subject who meets any of the following criteria will be excluded from participation in this study:

* No mpMRI performed prior to prostate biopsy or radical prostatectomy
* A history of chemotherapy for PCa or currently being treated with chemotherapy for PCa.
* A patient history that includes any of the following prostate related interventions:

  * Brachytherapy or external radiotherapy for PCa;
  * Focal therapy for prostate cancer;
  * Prostate biopsy within the last 30 days.
* Hormonal therapy for prostate cancer within the last six months
* A patient history with a cardiac right to left shunt.
* Current treatment with dobutamine
* Known severe pulmonary hypertension (pulmonary artery pressure \>90 mmHg), uncontrolled systemic hypertension or respiratory distress syndrome
* Incapable of understanding the language in which the patient information is given.

Primary outcomes:
measure: To collect high quality 3D multiparametric ultrasound and histology data to train and improve the classifier algorithm with the goal of achieving an accurate ultrasound imaging tool for detection, grading and localization of prostate cancer.
timeFrame: The 3D multiparametric ultrasound images collected te moment the ultrasound is done. Histology findings will be collected 2 week after prostatebiopsies or radical prostatectomy. No patient follow-up is needed.
Secondary outcomes:
measure: The number of histologically proven clinically relevant cancer that can be detected by 3D multiparametric ultrasound using full mount prostate histopathology as the reference standard.
timeFrame: The 3D multiparametric ultrasound images collected the moment the ultrasound is done. Histology findings will be collected 2 week after prostatebiopsies or radical prostatectomy. No patient follow-up is needed.
measure: The frequency of false-positive result by 3D multiparametric ultrasound using full mount prostate histopathology as the reference standard.
timeFrame: The 3D multiparametric ultrasound images collected the moment the ultrasound is done. Histology findings will be collected 2 week after prostatebiopsies or radical prostatectomy. No patient follow-up is needed.
measure: The concordance between findings from mpMRI and 3D multiparametric ultrasound.
timeFrame: mpMRI is present at time of inclusion, 3D mpUS images will be collected 1-3 weeks after inclusion. The concordance between mpMRI and 3DmpUS can be evaluated after imaging and histpathology data is in.
Other outcomes:
Locations:
city: Amsterdam
country: Netherlands
facility: Amsterdam Univesity Medical Centers location VUmc
state: Noord Holland
city: Amsterdam
country: Netherlands
facility: Academic Medical Center
state: Noord-Holland
city: Amsterdam
country: Netherlands
facility: Netherlands Cancer Institute
state: North Holland
Study officials:
affiliation: Amsterdam University Medical Centers
name: Harrie Beerlage, Professor
role: PRINCIPAL_INVESTIGATOR
References:
citation: Russo G, Mischi M, Scheepens W, De la Rosette JJ, Wijkstra H. Angiogenesis in prostate cancer: onset, progression and imaging. BJU Int. 2012 Dec;110(11 Pt C):E794-808. doi: 10.1111/j.1464-410X.2012.11444.x. Epub 2012 Sep 7.
citation: van Moorselaar RJ, Voest EE. Angiogenesis in prostate cancer: its role in disease progression and possible therapeutic approaches. Mol Cell Endocrinol. 2002 Nov 29;197(1-2):239-50. doi: 10.1016/s0303-7207(02)00262-9.
citation: Mischi M, Kuenen MP, Wijkstra H. Angiogenesis imaging by spatiotemporal analysis of ultrasound contrast agent dispersion kinetics. IEEE Trans Ultrason Ferroelectr Freq Control. 2012 Apr;59(4):621-9. doi: 10.1109/TUFFC.2012.2241.
citation: Kuenen MP, Saidov TA, Wijkstra H, de la Rosette JJ, Mischi M. Spatiotemporal correlation of ultrasound contrast agent dilution curves for angiogenesis localization by dispersion imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2013 Dec;60(12):2665-9. doi: 10.1109/TUFFC.2013.2865.
citation: Postema AW, Frinking PJ, Smeenge M, De Reijke TM, De la Rosette JJ, Tranquart F, Wijkstra H. Dynamic contrast-enhanced ultrasound parametric imaging for the detection of prostate cancer. BJU Int. 2016 Apr;117(4):598-603. doi: 10.1111/bju.13116. Epub 2015 Jun 29.
citation: Postema AW, Gayet MCW, van Sloun RJG, Wildeboer RR, Mannaerts CK, Savci-Heijink CD, Schalk SG, Kajtazovic A, van der Poel H, Mulders PFA, Beerlage HP, Mischi M, Wijkstra H. Contrast-enhanced ultrasound with dispersion analysis for the localization of prostate cancer: correlation with radical prostatectomy specimens. World J Urol. 2020 Nov;38(11):2811-2818. doi: 10.1007/s00345-020-03103-4. Epub 2020 Feb 20.
citation: Mannaerts CK, Engelbrecht MRW, Postema AW, van Kollenburg RAA, Hoeks CMA, Savci-Heijink CD, Van Sloun RJG, Wildeboer RR, De Reijke TM, Mischi M, Wijkstra H. Detection of clinically significant prostate cancer in biopsy-naive men: direct comparison of systematic biopsy, multiparametric MRI- and contrast-ultrasound-dispersion imaging-targeted biopsy. BJU Int. 2020 Oct;126(4):481-493. doi: 10.1111/bju.15093. Epub 2020 May 13.
citation: Mannaerts CK, Wildeboer RR, Remmers S, van Kollenburg RAA, Kajtazovic A, Hagemann J, Postema AW, van Sloun RJG, J Roobol M, Tilki D, Mischi M, Wijkstra H, Salomon G. Multiparametric Ultrasound for Prostate Cancer Detection and Localization: Correlation of B-mode, Shear Wave Elastography and Contrast Enhanced Ultrasound with Radical Prostatectomy Specimens. J Urol. 2019 Dec;202(6):1166-1173. doi: 10.1097/JU.0000000000000415. Epub 2019 Jun 27.