Journal Article
. 2020 Jan; 21(2):222-232.
doi: 10.1016/S1470-2045(19)30738-7.

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study

Peter Ström 1 Kimmo Kartasalo 2 Henrik Olsson 1 Leslie Solorzano 3 Brett Delahunt 4 Daniel M Berney 5 David G Bostwick 6 Andrew J Evans 7 David J Grignon 8 Peter A Humphrey 9 Kenneth A Iczkowski 10 James G Kench 11 Glen Kristiansen 12 Theodorus H van der Kwast 7 Katia R M Leite 13 Jesse K McKenney 14 Jon Oxley 15 Chin-Chen Pan 16 Hemamali Samaratunga 17 John R Srigley 18 Hiroyuki Takahashi 19 Toyonori Tsuzuki 20 Murali Varma 21 Ming Zhou 22 Johan Lindberg 1 Cecilia Lindskog 23 Pekka Ruusuvuori 2 Carolina Wählby 24 Henrik Grönberg 25 Mattias Rantalainen 1 Lars Egevad 26 Martin Eklund 27 
  • PMID: 31926806
  •     77 citations


Background: An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.

Methods: We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50-69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.

Findings: The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994-0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972-0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95-0·97) for the independent test dataset and 0·87 (0·84-0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60-0·73).

Interpretation: An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.

Funding: Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health.

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