Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms “in the Wild”

IF 7.1 1区 医学 Q1 PATHOLOGY
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引用次数: 0

Abstract

The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)–based algorithms employing deep learning have shown their ability to match pathologists’ performance in assigning Gleason scores, with the potential to enhance pathologists’ grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.

评估基于人工智能的 "野生 "格里森分级算法。
活检格里森评分是前列腺癌患者重要的预后指标。然而,病理学家之间的差异很大。基于人工智能(AI)的深度学习算法已显示出与病理学家的格里森评分相匹配的能力,有望提高病理学家评分的准确性。Gleason 人工智能算法在研究中的表现大多是在通用基准数据集或公开挑战赛中报告的。相比之下,许多商业算法是在临床研究中进行评估的,其数据并未公开发布。由于商业人工智能供应商通常不公布公共基准的性能,因此很难对研究和商业人工智能进行比较。本研究旨在利用真实世界的数据评估和比较排名靠前的公共算法和商业算法的性能。我们通过众包策划了一个多样化的全切片前列腺活检图像数据集,其中包含各种格里森评分和不同来源的图像。预测结果来自 PANDA 挑战赛中排名靠前的五种公共算法和两种商业格里森分级算法。此外,十位病理学家在读者研究中对数据集进行了评估。总体而言,病理学家之间的成对二次加权卡帕值从 0.777 到 0.916 不等。公共算法和商业算法与病理学家的一致性都很高,二次加权卡帕在 0.617 到 0.900 之间。商业算法的表现与顶级公共算法相当或更胜一筹。
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来源期刊
Modern Pathology
Modern Pathology 医学-病理学
CiteScore
14.30
自引率
2.70%
发文量
174
审稿时长
18 days
期刊介绍: Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology. Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.
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