See the published version of this preprint at 3D Printing in Medicine.
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Research
Nicole Wake
Montefiore Hospital and Medical Center
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8441-6059
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose: This study reports on the development of a novel 3D procedure planning technique to provide pre-ablation treatment planning for partial gland prostate cryoablation (cPGA).
Methods: Twenty men scheduled for partial gland cryoablation (cPGA) underwent pre-operative image segmentation and 3D modeling of the prostatic capsule, index lesion, urethra, rectum, and neurovascular bundles based upon multi-parametric MRI data. Pre-treatment 3D planning models were designed including virtual 3D cryotherapy probes to predict and plan cryotherapy probe configuration needed to achieve confluent treatment volume. Treatment efficacy was measured with 6 month post-operative MRI, serum prostate specific antigen (PSA) at 3 and 6 months, and treatment zone biopsy results at 6 months. Outcomes from 3D planning were compared to outcomes from a series of 20 patients undergoing cPGA using traditional 2D planning techniques.
Results: 40 men underwent cPGA. The median age of the cohort undergoing 3D treatment planning was 64.8 years with a median pretreatment PSA of 6.97 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 1 (5%) GGG1, 11 (55%) GGG2, 7 (35%) GGG3, 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. The 2D treatment cohort included 20 men with a median age of 68.5 yrs, median pretreatment PSA of 6.76 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 3 (15%) GGG1, 8 (40%) GGG2, 8 (40%) GGG3, 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. 3D planning predicted the same number of cryoprobes for each group, however a greater number of cryoprobes was used in the procedure for the prospective 3D group as compared to that with 2D planning (4.10 ± 1.37 and 3.25 ± 0.44 respectively, p=0.01). At 6 months post cPGA, the median PSA was 1.68 and 2.38 ng/mL in the 3D and 2D cohorts respectively, with a larger decrease noted in the 3D cohort (75.9% reduction noted in 3D cohort and 64.8% reduction 2D cohort, p 0.48). In-field disease detection was 1/14 (7.1%) on surveillance biopsy in the 3D cohort and 3/14 (21.4%) in the 2D cohort, p=0.056) In the 3D cohort, 6 month biopsy was not performed in 4 (20%) due to undetectable PSA, negative MRI, and negative MRI Axumin PET. For the group with traditional 2D planning, treatment zone biopsy was positive in 3/14 (21.4%) of the patients, p = 0.056.
Conclusions: 3D prostate cancer models derived from mpMRI data provide novel guidance for planning confluent treatment volumes for cPGA and predicted a greater number of treatment probes than traditional 2D planning methods. This study prompts further investigation into the use of 3D treatment planning techniques as the increase of partial gland ablation treatment protocols develop.
Keywords
prostate cancer, cryotherapy, 3D planning, MRI
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CURRENT STATUS: Published
View the published article at 3D Printing in Medicine.
Version 3
Posted 13 Oct, 2020
No community comments so far
Editorial decision: Accept
On 06 Oct, 2020
Reviewer #1 agreed
On 04 Oct, 2020
Review #1 received
Received 04 Oct, 2020
Reviewers invited
Invitations sent on 03 Oct, 2020
Editor assigned
On 01 Oct, 2020
Submission checks complete
On 30 Sep, 2020
Editor invited
On 30 Sep, 2020
No comments provided
Reviewer #2 agreed
On 13 Jul, 2020
Review #1 received
Received 01 Jun, 2020
Reviewer #1 agreed
On 26 May, 2020
Editor assigned
On 25 May, 2020
Reviewers invited
Invitations sent on 25 May, 2020
Submission checks complete
On 24 May, 2020
Editor invited
On 24 May, 2020
No comments provided
Editorial decision: Major revision
On 04 Mar, 2020
Review #2 received
Received 03 Mar, 2020
Review #1 received
Received 25 Feb, 2020
Reviewer #2 agreed
On 14 Feb, 2020
Reviewers invited
Invitations sent on 13 Feb, 2020
Reviewer #1 agreed
On 13 Feb, 2020
Editor assigned
On 12 Feb, 2020
First submitted
On 11 Feb, 2020
Submission checks complete
On 11 Feb, 2020
Editor invited
On 11 Feb, 2020
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See the published version of this preprint at 3D Printing in Medicine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Nicole Wake
Montefiore Hospital and Medical Center
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8441-6059
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at 3D Printing in Medicine.
Version 3
Posted 13 Oct, 2020
No community comments so far
Editorial decision: Accept
On 06 Oct, 2020
Reviewer #1 agreed
On 04 Oct, 2020
Review #1 received
Received 04 Oct, 2020
Reviewers invited
Invitations sent on 03 Oct, 2020
Editor assigned
On 01 Oct, 2020
Submission checks complete
On 30 Sep, 2020
Editor invited
On 30 Sep, 2020
No comments provided
Reviewer #2 agreed
On 13 Jul, 2020
Review #1 received
Received 01 Jun, 2020
Reviewer #1 agreed
On 26 May, 2020
Editor assigned
On 25 May, 2020
Reviewers invited
Invitations sent on 25 May, 2020
Submission checks complete
On 24 May, 2020
Editor invited
On 24 May, 2020
No comments provided
Editorial decision: Major revision
On 04 Mar, 2020
Review #2 received
Received 03 Mar, 2020
Review #1 received
Received 25 Feb, 2020
Reviewer #2 agreed
On 14 Feb, 2020
Reviewers invited
Invitations sent on 13 Feb, 2020
Reviewer #1 agreed
On 13 Feb, 2020
Editor assigned
On 12 Feb, 2020
First submitted
On 11 Feb, 2020
Submission checks complete
On 11 Feb, 2020
Editor invited
On 11 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at 3D Printing in Medicine.
Purpose: This study reports on the development of a novel 3D procedure planning technique to provide pre-ablation treatment planning for partial gland prostate cryoablation (cPGA).
Methods: Twenty men scheduled for partial gland cryoablation (cPGA) underwent pre-operative image segmentation and 3D modeling of the prostatic capsule, index lesion, urethra, rectum, and neurovascular bundles based upon multi-parametric MRI data. Pre-treatment 3D planning models were designed including virtual 3D cryotherapy probes to predict and plan cryotherapy probe configuration needed to achieve confluent treatment volume. Treatment efficacy was measured with 6 month post-operative MRI, serum prostate specific antigen (PSA) at 3 and 6 months, and treatment zone biopsy results at 6 months. Outcomes from 3D planning were compared to outcomes from a series of 20 patients undergoing cPGA using traditional 2D planning techniques.
Results: 40 men underwent cPGA. The median age of the cohort undergoing 3D treatment planning was 64.8 years with a median pretreatment PSA of 6.97 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 1 (5%) GGG1, 11 (55%) GGG2, 7 (35%) GGG3, 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. The 2D treatment cohort included 20 men with a median age of 68.5 yrs, median pretreatment PSA of 6.76 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 3 (15%) GGG1, 8 (40%) GGG2, 8 (40%) GGG3, 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. 3D planning predicted the same number of cryoprobes for each group, however a greater number of cryoprobes was used in the procedure for the prospective 3D group as compared to that with 2D planning (4.10 ± 1.37 and 3.25 ± 0.44 respectively, p=0.01). At 6 months post cPGA, the median PSA was 1.68 and 2.38 ng/mL in the 3D and 2D cohorts respectively, with a larger decrease noted in the 3D cohort (75.9% reduction noted in 3D cohort and 64.8% reduction 2D cohort, p 0.48). In-field disease detection was 1/14 (7.1%) on surveillance biopsy in the 3D cohort and 3/14 (21.4%) in the 2D cohort, p=0.056) In the 3D cohort, 6 month biopsy was not performed in 4 (20%) due to undetectable PSA, negative MRI, and negative MRI Axumin PET. For the group with traditional 2D planning, treatment zone biopsy was positive in 3/14 (21.4%) of the patients, p = 0.056.
Conclusions: 3D prostate cancer models derived from mpMRI data provide novel guidance for planning confluent treatment volumes for cPGA and predicted a greater number of treatment probes than traditional 2D planning methods. This study prompts further investigation into the use of 3D treatment planning techniques as the increase of partial gland ablation treatment protocols develop.
Figure 1
Figure 2
Figure 3
Figure 4
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