Division of Urological Cancers, Department of Translational Medicine, Lund University, Malmö, Sweden; Department of Pathology, Skåne University Hospital, Malmö, Sweden.
Centre for Mathematical Sciences, Lund University, Lund, Sweden.
Eur Urol Focus. 2021 Sep;7(5):995-1001. doi: 10.1016/j.euf.2020.11.001. Epub 2020 Dec 7.
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.
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.
Correlation, sensitivity, and specificity parameters were calculated.
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.
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.
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.
格里森分级是前列腺癌的标准诊断方法，对于确定预后和治疗至关重要。由于缺乏专家病理学家、观察者间和观察者内的可变性以及格里森分级的劳动强度，因此需要开发一种用户友好的工具来实现稳健的标准化。
开发一种基于机器学习和卷积神经网络的人工智能 (AI) 算法，作为提高前列腺癌活检中格里森分级标准化的工具。
设计、设置和参与者：总共使用了 174 名患者的 698 个前列腺活检切片进行训练。训练切片由两名高级顾问病理学家进行注释。最终算法在来自 21 名患者的 37 个活检切片上进行了测试，这些切片使用来自两个不同扫描仪的数字化幻灯片图像。
计算了相关性、敏感性和特异性参数。
该算法在检测癌区方面具有很高的准确性（敏感性：100%，特异性：68%）。与病理学家相比，该算法在检测癌区方面表现良好（组内相关系数 [ICC]：0.99），并且能够正确分配格里森模式：模式 3 和 4（ICC：0.96 和 0.94），程度较小的模式 5（ICC：0.82）。使用两种不同的扫描仪也得到了类似的结果。
我们的基于人工智能的算法可以可靠地检测前列腺癌并量化核心针活检中的格里森模式，其准确性与病理学家相似。这些结果在具有低观察者内可变性的不同扫描仪的图像上具有可重复性。我们相信，这种人工智能工具可以被视为病理学家的一种有效且互动的工具。
我们开发了一种用于前列腺活检的敏感人工智能工具，它可以以与病理学家相似的准确性检测和分级癌症。该工具有望改善前列腺癌的诊断。
