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Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival
Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
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2019 (English)In: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097XArticle in journal (Refereed) In press
Abstract [en]

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. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine

Place, publisher, year, edition, pages
Blackwell Publishing, 2019.
Keywords [en]
artificial intelligence, convolutional neural network, objective quantification, prostatic neoplasms
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging Medical Laboratory and Measurements Technologies Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:hh:diva-41541DOI: 10.1111/cpf.12611Scopus ID: 2-s2.0-85076741263OAI: oai:DiVA.org:hh-41541DiVA, id: diva2:1390966
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-03Bibliographically approved

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Ohlsson, Mattias

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Medical Image ProcessingRadiology, Nuclear Medicine and Medical ImagingMedical Laboratory and Measurements TechnologiesCardiac and Cardiovascular Systems

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