Gleason grading of prostate cancer using artificial intelligence: lessons learned from the PANDA challenge

Kimmo Kartasalo, Peter Ström, Martin Eklund, Wouter Bulten, Hans Pinckaers, Geert Litjens, Po-Hsuan Cameron Chen, Kunal Nagpal, Pekka Ruusuvuori

Keywords: Computational pathology, prostate cancer, Gleason grading, challenge

TL;DR: Summary of the findings from the Prostate cancer grade assessment (PANDA) challenge

Abstract: Assessing prostate biopsies is crucial for the clinical management of patients with suspected prostate cancer, but is associated with complications such as inter-observer variability. The PANDA challenge aimed at mitigating these issues through development and rigorous validation of image analysis algorithms for the task. In this short paper, we summarize the key insights gained from PANDA from the viewpoints of algorithm development and challenge organisation.

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Paper Type: recently published or submitted journal contributions

Primary Subject Area: Application: Histopathology

Secondary Subject Area: Detection and Diagnosis

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