肺癌亚型的识别对于患者（尤其是晚期患者）的成功治疗至关重要。许多先进的个人治疗需要了解特定突变以及基因的上调和下调，才能有效靶向癌细胞。虽然许多研究侧重于单个细胞结构并深入研究基因测序，但本研究提出了一种基于 2D 共培养环境中低放大倍数癌症生长模式的肺癌分类机器学习方法。使用磁力孔板支架，在成纤维细胞中生成圆形肺癌细胞簇，并每天捕获图像以监测 9 天内的癌症生长情况。然后对这些生长图像进行增强，并用于训练基于轻量级 TinyVGG 架构的卷积神经网络 (CNN) 模型。该模型使用代表 NSCLC 三种亚型的成对类别进行训练：A549（腺癌）、H520（鳞状细胞癌）和 H460（大细胞癌）。目的是评估这种轻量级机器学习模型是否能够准确地对处于癌症生长不同阶段的三种肺癌细胞系进行分类。此外，还使用 ​​CNN 模型捕获并分类了两种源自患者的肺癌细胞的癌症生长图像，其中一种带有 KRAS 癌基因，另一种带有 EGFR 癌基因。该演示旨在研究机器学习支持的肺癌分类的转化潜力。轻量级 CNN 模型在 A549、H460 和 H520 生长 1 天时实现了超过 93% 的分类准确率，并在生长 7 天时达到 100% 的分类准确率。生长出来的。此外，该模型在 4 天后对患者来源的肺癌细胞实现了 100% 的分类准确率。尽管这些细胞被归类为腺癌，但它们的生长模式根据其癌基因表达（KRAS 或 EGFR）而有所不同。这些结果表明，在没有网络或云连接的笔记本电脑上本地运行的轻量级 CNN 架构可以有效地创建机器学习-该模型能够根据肺癌细胞亚型（包括来自患者的肺癌细胞亚型）在周围成纤维细胞存在的情况下的生长模式进行准确分类。这一进展强调了机器学习增强早期肺癌亚型分型的潜力，为改善晚期患者的治疗结果提供了有希望的途径。© 2024。作者。

Identification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.Using a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.The lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).These results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.© 2024. The Author(s).