From the Department of Physics and Astronomy, Brigham Young University, Provo, Utah.
Arch Pathol Lab Med. 2024 May 1;148(5):603-612. doi: 10.5858/arpa.2022-0460-RA.
Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading.
To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed.
The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities.
It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis.
机器学习技术在前列腺癌检测中的应用引发了这样一种猜测，即病理学家很快将被算法所取代。这篇综述涵盖了机器学习算法的发展及其在前列腺癌检测和 Gleason 分级方面的报告效果。
检查当前算法的准确性和分类能力。我们提供了对该技术的一般解释，以及它在临床实践中的应用方式。还讨论了机器学习算法在临床实践中的应用所面临的挑战。
通过系统搜索确定并收集了本综述的文献。在排序过程之前，制定了标准，以便有效地指导研究的选择。实施了四点系统，根据相关性对论文进行排名。对于被认为与我们的指标相关的论文，还对所有引用和引用的研究进行了审查。然后根据他们是否实施了二进制或多类分类方法对研究进行分类。从包含前列腺癌检测背景下的准确性、曲线下面积 (AUC) 或 κ 值的论文中提取数据。结果以可视化方式进行总结，以呈现分类能力之间的准确性趋势。
多分类任务比二进制任务更难达到高精度指标。能够为临床全切片图像 (WSI) 分配 Gleason 分级的算法在临床中的实施仍然难以实现。机器学习技术目前还不能取代病理学家，但可以作为防止误诊的重要保障。
