Evaluating cell growth and hypoxic regions of 3D spheroids via a machine learning approach

IF 6.3 2区物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Learning Science and Technology Pub Date : 2024-09-02 DOI:10.1088/2632-2153/ad718e

Jaekak Yoo, Jae Won Choi, Eunha Kim, Eun-Jung Park, Ahruem Baek, Jaeseok Kim, Mun Seok Jeong, Youngwoo Cho, Tae Geol Lee, Min Beom Heo

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引用次数: 0

Abstract

This study investigated the applicability of the area of spheroids and hypoxic regions for efficient evaluation of drug efficacy using machine learning (ML). We initially developed a high-throughput detection method to obtain the area of spheroids and hypoxic regions that can handle over 10 000 images per hour with an error rate of 2%–3%. The ML models were trained using cell growth of six cell lines (i.e. HepG2, A549, Hep3B, BEAS-2B, HT-29, and HCT116) and hypoxic region variations of two cell lines (i.e. HepG2 and BEAS-2B); our model can predict the area of spheroids and hypoxic region of certain growth date with high precision. To demonstrate the applicability, HepG2 spheroids were treated with sorafenib, and the efficacy of the drug was evaluated through a comparison of differences in areas of cell size and hypoxic regions with the predicted results. Furthermore, our ML approach has been shown to be applicable to provide the model-driven evaluative criterion for toxicity and drug efficacy using spheroids.

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通过机器学习方法评估三维球体的细胞生长和缺氧区域

本研究利用机器学习（ML）研究了球形和缺氧区域面积在高效评估药物疗效方面的适用性。我们最初开发了一种高通量检测方法来获取球形区和缺氧区的面积，该方法每小时可处理 10,000 多张图像，误差率为 2%-3%。我们使用六种细胞系（即 HepG2、A549、Hep3B、BEAS-2B、HT-29 和 HCT116）的细胞生长和两种细胞系（即 HepG2 和 BEAS-2B）的缺氧区域变化训练了 ML 模型；我们的模型可以高精度地预测特定生长日期的球形面积和缺氧区域。为了证明其适用性，我们用索拉非尼处理了 HepG2 球形细胞，并通过比较细胞大小和缺氧区域面积与预测结果的差异来评估药物的疗效。此外，我们的 ML 方法已被证明适用于为使用球形细胞的毒性和药物疗效提供模型驱动的评估标准。

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来源期刊

Machine Learning Science and Technology Computer Science-Artificial Intelligence

CiteScore

9.10

自引率

4.40%

发文量

审稿时长

5 weeks

期刊介绍： Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.