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Deep learning performance on MRI prostate gland segmentation : evaluation of two commercially available algorithms compared with an expert radiologist

Thimansson, Erik LU ; Baubeta, Erik LU orcid ; Engman, Jonatan LU ; Bjartell, Anders LU and Zackrisson, Sophia LU (2024) In Journal of Medical Imaging 11(1).
Abstract

PURPOSE: Accurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models.

APPROACH: We included 123 patients in this retrospective multicenter (7 hospitals), multiscanner (8 scanners, 2 vendors, 1.5T and 3T) study comparing prostate contour assessment by 2 commercially available Food... (More)

PURPOSE: Accurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models.

APPROACH: We included 123 patients in this retrospective multicenter (7 hospitals), multiscanner (8 scanners, 2 vendors, 1.5T and 3T) study comparing prostate contour assessment by 2 commercially available Food and Drug Association (FDA)-cleared and CE-marked algorithms (DLA1 and DLA2) using an expert radiologist's manual contours as a reference standard (RSexp) in this clinical heterogeneous MRI dataset. No in-house training of the DLAs was performed before testing. Several methods for comparing segmentation overlap were used, the Dice similarity coefficient (DSC) being the most important.

RESULTS: The DSC mean and standard deviation for DLA1 versus the radiologist reference standard (RSexp) was 0.90±0.05 and for DLA2 versus RSexp it was 0.89±0.04. A paired t-test to compare the DSC for DLA1 and DLA2 showed no statistically significant difference (p=0.8).

CONCLUSIONS: Two commercially available DL algorithms (FDA-cleared and CE-marked) can perform accurate whole-gland prostate segmentation on a par with expert radiologist manual planimetry on a real-world clinical dataset. Implementing AI models in the clinical routine may free up time that can be better invested in complex work tasks, adding more patient value.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Medical Imaging
volume
11
issue
1
article number
015002
publisher
SPIE
external identifiers
  • scopus:85186361836
  • pmid:38404754
ISSN
2329-4302
DOI
10.1117/1.JMI.11.1.015002
language
English
LU publication?
yes
additional info
© 2024 The Authors.
id
74ec5248-6e34-461f-b220-e8b6955acba3
date added to LUP
2024-02-26 22:07:55
date last changed
2024-04-24 14:27:05
@article{74ec5248-6e34-461f-b220-e8b6955acba3,
  abstract     = {{<p>PURPOSE: Accurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models.</p><p>APPROACH: We included 123 patients in this retrospective multicenter (7 hospitals), multiscanner (8 scanners, 2 vendors, 1.5T and 3T) study comparing prostate contour assessment by 2 commercially available Food and Drug Association (FDA)-cleared and CE-marked algorithms (DLA1 and DLA2) using an expert radiologist's manual contours as a reference standard (RSexp) in this clinical heterogeneous MRI dataset. No in-house training of the DLAs was performed before testing. Several methods for comparing segmentation overlap were used, the Dice similarity coefficient (DSC) being the most important.</p><p>RESULTS: The DSC mean and standard deviation for DLA1 versus the radiologist reference standard (RSexp) was 0.90±0.05 and for DLA2 versus RSexp it was 0.89±0.04. A paired t-test to compare the DSC for DLA1 and DLA2 showed no statistically significant difference (p=0.8).</p><p>CONCLUSIONS: Two commercially available DL algorithms (FDA-cleared and CE-marked) can perform accurate whole-gland prostate segmentation on a par with expert radiologist manual planimetry on a real-world clinical dataset. Implementing AI models in the clinical routine may free up time that can be better invested in complex work tasks, adding more patient value.</p>}},
  author       = {{Thimansson, Erik and Baubeta, Erik and Engman, Jonatan and Bjartell, Anders and Zackrisson, Sophia}},
  issn         = {{2329-4302}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{SPIE}},
  series       = {{Journal of Medical Imaging}},
  title        = {{Deep learning performance on MRI prostate gland segmentation : evaluation of two commercially available algorithms compared with an expert radiologist}},
  url          = {{http://dx.doi.org/10.1117/1.JMI.11.1.015002}},
  doi          = {{10.1117/1.JMI.11.1.015002}},
  volume       = {{11}},
  year         = {{2024}},
}