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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http://dx.doi.org/10.1016/S1470-2045(19)30738-7 | DOI Listing |
Angew Chem Int Ed Engl
December 2024
Institut Chimie radicalaire ICR-UMR 7273, Facult� de Saint jerome, avenue Escadrille-Normandie-Niemen, service 562, 13397, Marseille, FRANCE.
Efforts to understand radical stability have led to considerable progress in radical chemistry. In this article, we investigated a novel approach to enhancing the radical stability of carbon-centered radicals through space electron delocalization within [2,2]-paracyclophanes. Alkoxyamines possessing a paracyclophane scaffold exploit face-to-face π-π-interactions between the aromatic rings to effectively lower bond dissociation energy (BDE) for NO-C bond homolysis.
View Article and Find Full Text PDFScand J Urol
December 2024
Department of Urology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
N/A.
View Article and Find Full Text PDFFront Genet
December 2024
Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Objective: This study evaluated the real-world efficacy and safety of combining PARP inhibitors with novel hormonal therapy (NHT) as a first-line treatment in Chinese patients with metastatic castration-resistant prostate cancer (mCRPC) harboring homologous recombination repair (HRR) gene mutations.
Methods: We enrolled 41 mCRPC patients who received at least 1 month of combined treatment with PARP inhibitors and NHT. Patients were divided into two groups: Cohort A (mutations in BRCA1, BRCA2, or ATM genes) and Cohort B (mutations in other HRR genes).
Front Oncol
December 2024
Department of Magnetic Resonance Imaging (MRI), The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Purpose: The aim of this study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived kinetic parameters with high spatiotemporal resolution in discriminating malignant from normal prostate tissues.
Methods: Fifty patients with suspicious of malignant diseases in prostate were included in this study. Regions of interest (ROI) were manually delineated by experienced radiologists.
Supraphysiological androgen (SPA) treatment can paradoxically restrict growth of castration-resistant prostate cancer with high androgen receptor (AR) activity, which is the basis for use of Bipolar Androgen Therapy (BAT) for patients with this disease. While androgens are widely appreciated to enhance anabolic metabolism, how SPA-mediated metabolic changes alter prostate cancer progression and therapy response is unknown. Here, we report that SPA markedly increased intracellular and secreted polyamines in prostate cancer models.
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