期刊
EUROPEAN UROLOGY FOCUS
卷 7, 期 5, 页码 995-1001出版社
ELSEVIER
DOI: 10.1016/j.euf.2020.11.001
关键词
Convolutional neural network; Machine learning; Deep learning; Prostate cancer
资金
- strategic research project eSSENCE
- Vinnova-Swelife program
- Vinnova-Medtech4Life program
- Prostatacancerforbundet
- Swedish Cancer Foundation [140 274]
- Swedish Scientific Council [D0484201]
- BioCare program at Lund University
- Skane University Hospital Research Foundations
- Government Funding of Clinical Research
- National Health Services, and Lund University (ALF)
The study aimed to develop an artificial intelligence algorithm for improved standardisation in Gleason grading in prostate cancer biopsies, using machine learning and convolutional neural networks. The algorithm showed high accuracy in detecting cancer areas and assigning Gleason patterns correctly, achieving similar results as pathologists with low intraobserver variability.
Background: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter-and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation. Objective: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies. Design, setting, and participants: A total of 698 prostate biopsy sections from 174 patients were used for training. The training sections were annotated by two senior consultant pathologists. The final algorithm was tested on 37 biopsy sections from 21 patients, with digitised slide images from two different scanners. Outcome measurements and statistical analysis: Correlation, sensitivity, and specificity parameters were calculated. Results and limitations: The algorithm shows high accuracy in detecting cancer areas (sensitivity: 100%, specificity: 68%). Compared with the pathologists, the algorithm also performed well in detecting cancer areas (intraclass correlation coefficient [ICC]: 0.99) and assigning the Gleason patterns correctly: Gleason patterns 3 and 4 (ICC: 0.96 and 0.94, respectively), and to a lesser extent, Gleason pattern 5 (ICC: 0.82). Similar results were obtained using two different scanners. Conclusions: Our AI-based algorithm can reliably detect prostate cancer and quantify the Gleason patterns in core needle biopsies, with similar accuracy as pathologists. The results are reproducible on images from different scanners with a proven low level of intraobserver variability. We believe that this AI tool could be regarded as an efficient and interactive tool for pathologists. Patient summary: We developed a sensitive artificial intelligence tool for prostate biopsies, which detects and grades cancer with similar accuracy to pathologists. This tool holds promise to improve the diagnosis of prostate cancer. (c) 2020 European Association of Urology. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
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