Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
(2021) In Clinical Physiology and Functional Imaging 41(1). p.62-67- Abstract
Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods: A group of 399 patients with biopsy-proven PCa who had undergone 18F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to... (More)
Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods: A group of 399 patients with biopsy-proven PCa who had undergone 18F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results: The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p =.045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111; p =.63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion: This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
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- author
- Borrelli, Pablo ; Larsson, Måns ; Ulén, Johannes LU ; Enqvist, Olof LU ; Trägårdh, Elin LU ; Poulsen, Mads Hvid ; Mortensen, Mike Allan ; Kjölhede, Henrik LU ; Høilund-Carlsen, Poul Flemming and Edenbrandt, Lars LU
- organization
- publishing date
- 2021-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, fluorocholine, lymph node metastases, PCa, PET
- in
- Clinical Physiology and Functional Imaging
- volume
- 41
- issue
- 1
- pages
- 6 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:32976691
- scopus:85092607399
- ISSN
- 1475-0961
- DOI
- 10.1111/cpf.12666
- language
- English
- LU publication?
- yes
- id
- 97f94aaa-1940-42c7-a7ba-6bd101168876
- date added to LUP
- 2020-11-12 08:23:41
- date last changed
- 2024-04-17 19:15:55
@article{97f94aaa-1940-42c7-a7ba-6bd101168876, abstract = {{<p>Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods: A group of 399 patients with biopsy-proven PCa who had undergone <sup>18</sup>F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results: The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p =.045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111; p =.63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion: This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.</p>}}, author = {{Borrelli, Pablo and Larsson, Måns and Ulén, Johannes and Enqvist, Olof and Trägårdh, Elin and Poulsen, Mads Hvid and Mortensen, Mike Allan and Kjölhede, Henrik and Høilund-Carlsen, Poul Flemming and Edenbrandt, Lars}}, issn = {{1475-0961}}, keywords = {{artificial intelligence; fluorocholine; lymph node metastases; PCa; PET}}, language = {{eng}}, number = {{1}}, pages = {{62--67}}, publisher = {{John Wiley & Sons Inc.}}, series = {{Clinical Physiology and Functional Imaging}}, title = {{Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival}}, url = {{http://dx.doi.org/10.1111/cpf.12666}}, doi = {{10.1111/cpf.12666}}, volume = {{41}}, year = {{2021}}, }