人工智能推动了对肺癌微环境组成的评估。

Q2 Medicine
Enzo Gallo , Davide Guardiani , Martina Betti , Brindusa Ana Maria Arteni , Simona Di Martino , Sara Baldinelli , Theodora Daralioti , Elisabetta Merenda , Andrea Ascione , Paolo Visca , Edoardo Pescarmona , Marialuisa Lavitrano , Paola Nisticò , Gennaro Ciliberto , Matteo Pallocca
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引用次数: 0

摘要

目的:肿瘤浸润淋巴细胞(TILs)以及肿瘤微环境中其他成分的丰度和分布对于预测肺癌(LC)免疫疗法的反应尤为重要。我们在此介绍一项采用人工智能(AI)评估TILs和其他细胞群的试验性研究,旨在减少观察者之间或观察者内部的差异性,这种差异性通常是这种评估的特点:设计:我们利用开源框架 QuPath 开发了一种基于机器学习的分类器,用于检测苏木精和伊红染色切片上的肿瘤细胞、免疫细胞和基质细胞。我们评估了 37 个 LC 整张切片图像感兴趣区中上述三种细胞群的数量,比较了五位病理学家在使用人工智能图形预测之前和之后所做的评估,共进行了 1110 次定量测量:我们的研究结果表明,病理学家之间以及病理学家与人工智能之间的评分分布存在显著差异。在人工智能指导下,病理学家的评估结果减少了病理学家之间的显著差异:三项比较显示显著性降低(P > 0.05),而其他四项比较显示显著性降低(P > 0.01):我们的研究表明,在细胞群定量中采用机器学习方法可减少观察者之间和观察者内部的差异,从而提高可重复性,便于在进一步的验证研究中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI drives the assessment of lung cancer microenvironment composition

Purpose

The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation.

Design

We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework QuPath. We evaluated the quantity of the aforementioned three cell populations among 37 LC whole slide images regions of interest, comparing the assessments made by five pathologists, both before and after using graphical predictions made by AI, for a total of 1110 quantitative measurements.

Results

Our findings indicate noteworthy variations in score distribution among pathologists and between individual pathologists and AI. The AI-guided pathologist's evaluations resulted in reduction of significant discrepancies across pathologists: three comparisons showed a loss of significance (p > 0.05), whereas other four showed a reduction in significance (p > 0.01).

Conclusions

We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies.
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来源期刊
Journal of Pathology Informatics
Journal of Pathology Informatics Medicine-Pathology and Forensic Medicine
CiteScore
3.70
自引率
0.00%
发文量
2
审稿时长
18 weeks
期刊介绍: The Journal of Pathology Informatics (JPI) is an open access peer-reviewed journal dedicated to the advancement of pathology informatics. This is the official journal of the Association for Pathology Informatics (API). The journal aims to publish broadly about pathology informatics and freely disseminate all articles worldwide. This journal is of interest to pathologists, informaticians, academics, researchers, health IT specialists, information officers, IT staff, vendors, and anyone with an interest in informatics. We encourage submissions from anyone with an interest in the field of pathology informatics. We publish all types of papers related to pathology informatics including original research articles, technical notes, reviews, viewpoints, commentaries, editorials, symposia, meeting abstracts, book reviews, and correspondence to the editors. All submissions are subject to rigorous peer review by the well-regarded editorial board and by expert referees in appropriate specialties.
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