LEarning biOchemical Prostate cAncer Reccurance from histopathology sliDes (LEOPARD)

Prostate cancer, impacting 1.4 million men annually, is a prevalent malignancy [1]. A substantial number of these individuals undergo prostatectomy as the primary curative treatment. The efficacy of this surgery is assessed, in part, by monitoring the concentration of prostate-specific antigen (PSA) in the bloodstream. While the role of PSA in prostate cancer screening is debatable [2,3], it serves as a valuable biomarker for postprostatectomy follow-up in patients. Following successful surgery, PSA concentration is typically undetectable (<0.1 ng/mL) within 4–6 weeks [4]. However, approximately 30% of patients experience biochemical recurrence, signifying the resurgence of prostate cancer cells. This recurrence serves as a prognostic indicator for progression to clinical metastases and eventual prostate cancer-related mortality [5,6,7,8]. 
 
Current clinical practices gauge the risk of biochemical recurrence by considering the International Society of Urological Pathology (ISUP) grade, PSA value at diagnosis, and TNM staging criteria [9]. A recent European consensus guideline suggests categorizing patients into low-risk, intermediate-risk, and high-risk groups based on these factors [10]. Notably, a high ISUP grade independently assigns a patient to the intermediate (grade 2/3) or high-risk group (grade 4/5). 
 
The Gleason growth patterns, representing morphological patterns of prostate cancer, are used to categorize cancerous tissue into ISUP grade groups [11,12,13,14]. However, the ISUP grade has limitations, such as grading disagreement among pathologisdts[14] and coarse descriptors of tissue morphology. 
 
Hypothesizing that artificial intelligence, specifically deep learning, could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Reccurance from histopathology sliDes (LEOPARD ) challenge. The goal of this challenge is to yield top-performance deep learning solution to predict the likelihood of early biochemical recurrence from H&E-stained histopathological tissue sections, i.e based on morphological features. 

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