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Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review 淋巴结转移检测的计算方法以及无转移淋巴结微结构的特征描述:系统叙事混合综述
Q2 Medicine Pub Date : 2024-02-04 DOI: 10.1016/j.jpi.2024.100367
Elzbieta Budginaite , Derek R. Magee , Maximilian Kloft , Henry C. Woodruff , Heike I. Grabsch

Background

Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.

Objective

To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.

Methods

A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.

Results

A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.

Conclusions

Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.

背景肿瘤引流淋巴结(LN)的组织学检查在癌症分期和预后中起着至关重要的作用。目的系统研究并严格评估已发表研究中描述的数字化组织学淋巴结图像分析方法。方法使用相关检索词对截至 2023 年 12 月的多个公共数据库进行系统检索。纳入了使用苏木精和伊红或免疫组化染色的LN组织切片的明视野光学显微镜图像,旨在使用人工智能(AI)检测和/或分割LN、其分区或转移性肿瘤的研究。结果 共收集到 7201 篇文章,经过文章筛选后,剩下 73 篇文章进行了详细分析。在剩下的文章中,86%的文章以LN转移灶识别为目标,8%的文章以LN分区分割为目标,剩下的文章以LN轮廓划分为目标。此外,78%的文章使用斑块分类,22%的文章使用像素分割模型进行分析。无转移LN的六项研究中有五项(83%)是在未公开的数据集上进行的,因此无法对文章进行定量比较。需要大规模数据集来确定详细分析无转移 LN 的临床相关性。还需要进一步的研究来确定临床上可解释的 LN 区室特征描述指标。
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引用次数: 0
Pathology Visions 2023 Overview 2023 年病理学愿景概述
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100362
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引用次数: 0
External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study 基于深度学习的甲状腺乳头状癌高细胞检测算法的外部验证:一项多中心研究
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100366
Sebastian Stenman, Sylvain Bétrisey, Paula Vainio, Jutta Huvila, M. Lundin, N. Linder, Anja Schmitt, Aurel Perren, Matthias S. Dettmer, Caj Haglund, Johanna Arola, Johan Lundin
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引用次数: 0
Utility of artificial intelligence in a binary classification of soft tissue tumors 人工智能在软组织肿瘤二元分类中的应用
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100368
Jing Di, Caylin Hickey, Cody Bumgardner, Mustafa Yousif, Mauricio Zapata, Therese Bocklage, Bonnie Balzer, Marilyn M. Bui, Jerad M. Gardner, Liron Pantanowitz, Shadi A. Qasem
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引用次数: 0
Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium 基于 AI 的子宫内膜细胞组成分析的动态变化:多囊卵巢综合症和 RIF 子宫内膜分析
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100364
Seungbaek Lee , Riikka K. Arffman , Elina K. Komsi , Outi Lindgren , Janette Kemppainen , Keiu Kask , Merli Saare , Andres Salumets , Terhi T. Piltonen

Background

The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency.

Methods

We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition.

Results

Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS

背景人类子宫内膜每月都会经历一个组织生长和退化的周期。在分泌中期,子宫内膜通过调节细胞组成(如上皮细胞和基质细胞)和分化,为胚胎植入建立一个最佳壁龛。多囊卵巢综合征(PCOS)和复发性着床失败(RIF)等情况下观察到的子宫内膜发育受损会导致不孕。令人惊讶的是,尽管子宫内膜在怀孕前的正常发育非常重要,但在这两种与不孕症相关的疾病中,却完全缺乏对子宫内膜细胞组成的精确测量。此外,目前测量上皮和基质面积的方法也有局限性,包括观察者内部和观察者之间的差异性以及效率。该人工智能模型经过训练,可以分割由上皮细胞和间质内膜细胞填充的区域。在训练步骤中,共标注了 28.36 平方毫米的区域,包括 2.56 平方毫米的上皮和 24.87 平方毫米的基质。两位经验丰富的病理学家验证了人工智能模型的性能。样本集中包含了 73 份健康对照妇女的子宫内膜样本,以确定从增殖期(PE)到分泌期(SE)的子宫内膜上皮与基质比例随周期变化的动态变化。结果我们的人工智能模型在划分上皮和基质区方面表现出可靠和可重复的性能,准确率分别达到 92.40% 和 99.23%。此外,人工智能模型的性能与病理学家的评估结果相当,上皮的 F1 分数超过 82%,基质的 F1 分数超过 96%。接下来,我们比较了多囊卵巢综合症妇女月经周期中子宫内膜上皮与基质的比例,以及 RIF 患者子宫内膜接受状态的相关性。排卵型多囊卵巢综合症子宫内膜在从月经前期到月经后期的每个周期阶段都显示出与对照组和健康妇女样本相似的上皮细胞比例,并与孕酮水平相关(对照组SE,r2 = 0.64,FDR <0.001;多囊卵巢综合症SE,r2 = 0.52,FDR <0.001)。与早期和晚期SE子宫内膜相比,健康女性和多囊卵巢综合症患者的中期SE子宫内膜上皮比例最高。无排卵型多囊卵巢综合征病例的上皮细胞比例与多囊卵巢综合征 PE 病例相当(无排卵型,14.54%;多囊卵巢综合征 PE,15.56%,p = 1.00)。结论人工智能模型通过计算上皮细胞和基质细胞占据的面积,快速准确地识别子宫内膜组织学特征。人工智能模型能根据月经周期阶段显示上皮细胞比例的变化,并能显示多囊卵巢综合症和 RIF 条件下上皮细胞比例的变化。总之,人工智能模型可以加快对组织细胞组成的分析,确保研究和临床目的的最大客观性,从而有可能改进子宫内膜组织学评估。
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引用次数: 0
External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study 基于深度学习的甲状腺乳头状癌高细胞检测算法的外部验证:一项多中心研究
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100366
Sebastian Stenman , Sylvain Bétrisey , Paula Vainio , Jutta Huvila , Mikael Lundin , Nina Linder , Anja Schmitt , Aurel Perren , Matthias S. Dettmer , Caj Haglund , Johanna Arola , Johan Lundin

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.

高细胞亚型(TC-PTC)是甲状腺乳头状癌(PTC)的一种侵袭性亚型。TC-PTC的定义是:PTC中至少有30%的上皮细胞的高度是宽度的三倍。在实践中,这一定义很难遵守,导致观察者之间的差异很大。在这项多中心研究中,我们在 160 张外部收集的苏木精和伊红(HE)染色的 PTC 全切片图像上验证了之前训练的基于深度学习(DL)的高大细胞检测算法。在由外部数据集中 18 个独立组织切片的 360 个人工注释感兴趣区组成的测试集中,基于 DL 的算法检测到高细胞的灵敏度为 90.6%,特异度为 88.5%。DL 算法检测到非 TC 区域的灵敏度为 81.6%,特异度为 92.9%。在验证数据集中,20% 和 30% 的 TC 临界值与明显较短的无复发生存期相关。总之,DL 算法无需任何再训练就能在未见过的外部扫描 HE 组织切片中检测出 TC,灵敏度和特异性都很高。
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引用次数: 0
Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review 可公开获取的乳腺组织病理学 H&E 全切片图像数据集:范围审查
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100363
M. Tafavvoghi, L. A. Bongo, N. Shvetsov, Lill-ToveRasmussen Busund, Kajsa Møllersen
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引用次数: 0
Pathology Visions 2023 Overview 2023 年病理学愿景概述
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100362
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引用次数: 0
Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium 基于 AI 的子宫内膜细胞组成分析的动态变化:多囊卵巢综合症和 RIF 子宫内膜分析
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100364
Seungbaek Lee, R. Arffman, E. Komsi, Outi Lindgren, J. Kemppainen, K. Kask, M. Saare, Andres Salumets, T. Piltonen
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引用次数: 0
Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review 可公开获取的乳腺组织病理学 H&E 全切片图像数据集:范围审查
Q2 Medicine Pub Date : 2024-02-01 DOI: 10.1016/j.jpi.2024.100363
Masoud Tafavvoghi , Lars Ailo Bongo , Nikita Shvetsov , Lill-Tove Rasmussen Busund , Kajsa Møllersen

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.

数字病理学和计算资源的进步对用于乳腺癌诊断和治疗的计算病理学领域产生了重大影响。然而,获取高质量的乳腺癌标记组织病理学图像是一个巨大的挑战,限制了准确、稳健的深度学习模型的开发。在这篇范围综述中,我们确定了可用于开发深度学习算法的公开可用的乳腺H&E染色全切片图像(WSI)数据集。我们系统地搜索了 9 个科学文献数据库和 9 个研究数据存储库,发现了 17 个公开可用的数据集,包含 10 385 张乳腺癌 H&E WSIs。此外,我们还报告了每个数据集的图像元数据和特征,以帮助研究人员为乳腺癌计算病理学的特定任务选择合适的数据集。此外,我们还编制了两份乳腺 H&E 补丁和私人数据集列表,作为研究人员的补充资源。值得注意的是,只有28%的收录文章使用了多个数据集,只有14%的文章使用了外部验证集,这表明其他已开发模型的性能可能容易被高估。52%的入选研究使用了 TCGA-BRCA。该数据集存在相当大的选择偏差,可能会影响训练算法的稳健性和普适性。此外,乳腺 WSI 数据集缺乏一致的元数据报告,这可能会成为开发精确深度学习模型的一个问题,这表明有必要制定明确的指南来记录乳腺 WSI 数据集的特征和元数据。
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引用次数: 0
期刊
Journal of Pathology Informatics
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