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Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival

Polymeri, Eirini ; Sadik, May ; Kaboteh, Reza ; Borrelli, Pablo ; Enqvist, Olof ; Ulén, Johannes LU ; Ohlsson, Mattias LU orcid ; Trägårdh, Elin LU ; Poulsen, Mads H. and Simonsen, Jane A. , et al. (2020) In Clinical Physiology and Functional Imaging 40(2). p.106-113
Abstract

Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered... (More)

Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial intelligence, convolutional neural network, objective quantification, prostatic neoplasms
in
Clinical Physiology and Functional Imaging
volume
40
issue
2
pages
8 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:85076741263
  • pmid:31794112
ISSN
1475-0961
DOI
10.1111/cpf.12611
project
AIR Lund - Artificially Intelligent use of Registers
language
English
LU publication?
yes
id
cd7fb81c-2cd4-46bd-911a-751a270139af
date added to LUP
2020-01-08 16:40:33
date last changed
2024-05-29 05:28:56
@article{cd7fb81c-2cd4-46bd-911a-751a270139af,
  abstract     = {{<p>Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered <sup>18</sup>F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.</p>}},
  author       = {{Polymeri, Eirini and Sadik, May and Kaboteh, Reza and Borrelli, Pablo and Enqvist, Olof and Ulén, Johannes and Ohlsson, Mattias and Trägårdh, Elin and Poulsen, Mads H. and Simonsen, Jane A. and Hoilund-Carlsen, Poul Flemming and Johnsson, Åse A. and Edenbrandt, Lars}},
  issn         = {{1475-0961}},
  keywords     = {{artificial intelligence; convolutional neural network; objective quantification; prostatic neoplasms}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{106--113}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Clinical Physiology and Functional Imaging}},
  title        = {{Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival}},
  url          = {{http://dx.doi.org/10.1111/cpf.12611}},
  doi          = {{10.1111/cpf.12611}},
  volume       = {{40}},
  year         = {{2020}},
}