Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Computational and structural biotechnology journal Pub Date : 2024-10-05 DOI:10.1016/j.csbj.2024.10.007
Aoling Huang , Yizhi Zhao , Feng Guan , Hongfeng Zhang , Bin Luo , Ting Xie , Shuaijun Chen , Xinyue Chen , Shuying Ai , Xianli Ju , Honglin Yan , Lin Yang , Jingping Yuan
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Abstract

Aims

This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.

Methods and Results

We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443–0.526 vs kappa=0.87; 95 % CI, 0.852–0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance.

Conclusions

This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.
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为尿道膀胱癌量身定制的 HER2 检测算法的性能:双中心研究
目的本研究旨在开发一种人工智能算法,用于对尿路上皮膀胱癌(UBCa)进行自动 HER2 评分,并根据乳腺癌标准使用人工和人工智能辅助方法评估观察者之间的一致性。方法与结果我们利用两家机构的 330 张切片进行了初步的人工智能开发,并选择了 200 张切片进行环形研究,共有 6 名病理学家(3 名高级病理学家,3 名初级病理学家)参与。我们的人工智能算法在两项独立测试中取得了很高的准确率,分别为 0.94 和 0.92。在环形研究中,与人工评分相比,人工智能辅助方法提高了准确性(0.66 vs 0.94)和一致性(kappa=0.48;95 % CI,0.443-0.526 vs kappa=0.87;95 % CI,0.852-0.885),尤其是在 HER2 低的病例中(F1 分数:0.63 vs 0.92)。此外,在 62.3% 的异质性 HER2 阳性病例中,判读准确性也有显著提高(0.49 vs 0.93)。在人工智能的帮助下,病理学家,尤其是初级病理学家,提高了准确性和一致性。结论这是第一项为 UBCa 中的 HER2 评分提供量化算法的研究,可帮助病理学家进行诊断。环形研究表明,基于乳腺癌标准的 HER2 评分可有效应用于 UBCa。此外,即使是在异质性病例中,人工智能的辅助也能大大提高经验水平不同的病理学家解释的准确性和一致性。
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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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