Sökning: WFRF:(Lennartz Maximilian)
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AI based prostate a...
AI based prostate analysis system trained without human supervision to predict patient outcome from tissue samples
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- Walhagen, Peter (författare)
- Spearpoint Analytics AB
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- Bengtsson, Ewert, 1948- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Uppsala university
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- Lennartz, Maximilian (författare)
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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- Sauter, Guido (författare)
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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- Busch, Christer (författare)
- Uppsala universitet,Institutionen för kirurgiska vetenskaper
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(creator_code:org_t)
- Elsevier BV, 2022
- 2022
- Engelska.
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Ingår i: Journal of Pathology Informatics. - : Elsevier BV. - 2229-5089 .- 2153-3539.
- Relaterad länk:
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https://www.scienced...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In order to plan the best treatment for prostate cancer patients the aggressiveness of the tumor is graded based on visual assessment of tissue biopsies according to the Gleason scale. Recently a number of AI models have been developed that can be trained to do this grading as well as human pathologists. But the accuracy of the AI grading will be limited by the accuracy of the subjective âground truthâ Gleason grades used for the training. We have trained an AI to predict patient outcome directly based on image analysis of a large biobank of tissue samples with known outcome without input of any human knowledge about cancer grading. The model has shown similar and in some cases better ability to predict patient outcome on an independent test-set than expert pathologists doing the conventional grading.
Nyckelord
- prostate cancer grading
- artificial intelligence based cancer grading
- predicting prostate cancer recurrence
- Computerized Image Processing
- Datoriserad bildbehandling
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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