SwePub - search: WFRF:(Walhagen Peter)

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1.	Walhagen, Peter, et al. (author) AI based prostate analysis system trained without human supervision to predict patient outcome from tissue samples 2022 In: Journal of Pathology Informatics. - : Elsevier BV. - 2229-5089 .- 2153-3539. Journal article (peer-reviewed)abstract 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 â€œground 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.

Walhagen, Peter, et al. (author)
AI based prostate analysis system trained without human supervision to predict patient outcome from tissue samples
2022
In: Journal of Pathology Informatics. - : Elsevier BV. - 2229-5089 .- 2153-3539.
Journal article (peer-reviewed)abstract
- 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 â€œground 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.

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