Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland : a method comparison study
(2019) In Clinical Physiology and Functional Imaging 39(6). p.399-406- Abstract
Aim: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). Methods: A convolutional neural network (CNN) was trained for automated measurements in 18F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax), mean standardized uptake value of voxels considered abnormal (SUVmean) and volume of abnormal voxels (Volabn). The product SUVmean × Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed.... (More)
Aim: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). Methods: A convolutional neural network (CNN) was trained for automated measurements in 18F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax), mean standardized uptake value of voxels considered abnormal (SUVmean) and volume of abnormal voxels (Volabn). The product SUVmean × Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue. Results: The mean (range) weight of the prostate specimens was 44 g (20–109), while CNN-estimated volume was 62 ml (31–108) with a mean difference of 13·5 g or ml (95% CI: 9·78–17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Volabn (ml) and TLU were 0·37 (−0·01 to 0·75), −0·08 (−0·30 to 0·14), 1·40 (−2·26 to 5·06) and 9·61 (−3·95 to 23·17), respectively. PET findings Volabn and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Conclusion: Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.
(Less)
- author
- organization
- publishing date
- 2019-08-22
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- agreement, choline, convolutional neural network, diagnostic imaging, positron emission tomography, prostatic neoplasms
- in
- Clinical Physiology and Functional Imaging
- volume
- 39
- issue
- 6
- pages
- 399 - 406
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85071942747
- pmid:31436365
- ISSN
- 1475-0961
- DOI
- 10.1111/cpf.12592
- language
- English
- LU publication?
- yes
- id
- c4ab635d-1147-45d6-bcab-ec8012d49eb5
- date added to LUP
- 2019-09-23 14:06:50
- date last changed
- 2024-10-02 12:53:11
@article{c4ab635d-1147-45d6-bcab-ec8012d49eb5,
abstract = {{<p>Aim: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). Methods: A convolutional neural network (CNN) was trained for automated measurements in <sup>18</sup>F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUV<sub>max</sub>), mean standardized uptake value of voxels considered abnormal (SUV<sub>mean</sub>) and volume of abnormal voxels (Vol<sub>abn</sub>). The product SUV<sub>mean</sub> × Vol<sub>abn</sub> was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue. Results: The mean (range) weight of the prostate specimens was 44 g (20–109), while CNN-estimated volume was 62 ml (31–108) with a mean difference of 13·5 g or ml (95% CI: 9·78–17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Vol<sub>abn</sub> (ml) and TLU were 0·37 (−0·01 to 0·75), −0·08 (−0·30 to 0·14), 1·40 (−2·26 to 5·06) and 9·61 (−3·95 to 23·17), respectively. PET findings Vol<sub>abn</sub> and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Conclusion: Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.</p>}},
author = {{Mortensen, Mike A. and Borrelli, Pablo and Poulsen, Mads Hvid and Gerke, Oke and Enqvist, Olof and Ulén, Johannes and Trägårdh, Elin and Constantinescu, Caius and Edenbrandt, Lars and Lund, Lars and Høilund-Carlsen, Poul Flemming}},
issn = {{1475-0961}},
keywords = {{agreement; choline; convolutional neural network; diagnostic imaging; positron emission tomography; prostatic neoplasms}},
language = {{eng}},
month = {{08}},
number = {{6}},
pages = {{399--406}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Clinical Physiology and Functional Imaging}},
title = {{Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland : a method comparison study}},
url = {{http://dx.doi.org/10.1111/cpf.12592}},
doi = {{10.1111/cpf.12592}},
volume = {{39}},
year = {{2019}},
}