Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.

IF 5.8 2区医学 Q1 Medicine

Respiratory Research Pub Date : 2024-10-29 DOI:10.1186/s12931-024-03021-8

Junxiang Chen, Chunxi Zhang, Jun Xie, Xuebin Zheng, Pengchen Gu, Shuaiyang Liu, Yongzheng Zhou, Jie Wu, Ying Chen, Yanli Wang, Chuan He, Jiayuan Sun

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引用次数: 0

Abstract

Background: Rapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.

Methods: The hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS model. The area under the curve (AUC) and accuracy were calculated to assess the performance of the model, and Cohen's kappa (κ) was calculated to measure the agreement between the model and the annotation. The HMLCS model was also compared with professional staff.

Results: The HMLCS model achieved AUC values of 0.9540 (95% confidence interval [CI]: 0.9257-0.9823) in malignant/benign classification, 0.9126 (95% CI: 0.8756-0.9365) in malignancy subtyping (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC], or other malignancies), and 0.9297 (95% CI: 0.9026-0.9603) in NSCLC subtyping (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], or NSCLC not otherwise specified [NSCLC-NOS]), respectively. In total, the model achieved an AUC of 0.8721 (95% CI: 0.7714-0.9258) and an accuracy of 0.7184 in the six-class classification task (benign, LUAD, LUSC, NSCLC-NOS, SCLC, or other malignancies). In addition, the model demonstrated a κ value of 0.6183 with the annotation, which was comparable to cytopathologists and superior to trained bronchoscopists and technicians.

Conclusion: The HMLCS model showed promising performance in the multiclassification of lung lesions or intrathoracic lymphadenopathy, with potential application to provide real-time feedback regarding preliminary diagnoses of specimens during transbronchial sampling procedures.

Clinical trial number: Not applicable.

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利用现场快速评估切片和血清生物标记物自动进行肺癌亚型分析。

背景：快速现场评估（ROSE）在经支气管取样过程中发挥着重要作用，可提供术中细胞病理学评估。然而，细胞病理学家的短缺限制了它的广泛应用。本研究旨在开发一种深度学习模型，用于自动分析 ROSE 细胞学图像：方法：研究人员提出了分层多标签肺癌亚型分析（HMLCS）模型，该模型结合了ROSE切片的全切片图像和血清生物标记物，用于区分良性和恶性病变，并识别肺癌的不同亚型。研究人员于2019年7月至2020年11月期间回顾性收集了811张ROSE切片和配对的血清生物学标志物数据集，并随机划分数据集进行HMLCS模型的训练、验证和测试。计算曲线下面积（AUC）和准确率来评估模型的性能，计算科恩卡帕（κ）来衡量模型与注释之间的一致性。HMLCS 模型还与专业人员进行了比较：结果：HMLCS 模型在恶性/良性分类中的 AUC 值为 0.9540（95% 置信区间 [CI]：0.9257-0.9823），在恶性肿瘤亚型（非小细胞肺癌 [NSCLC]、小细胞肺癌 [SCLC] 或其他恶性肿瘤）中的 AUC 值为 0.9126（95% 置信区间 [CI]：0.8756-0.9365），在恶性肿瘤分类中的 AUC 值为 0.9297（95% CI：0.9026-0.9603）。在六级分类任务（良性、LUAD、LUSC、NSCLC-NOS、SCLC 或其他恶性肿瘤）中，该模型的 AUC 为 0.8721（95% CI：0.7714-0.9258），准确率为 0.7184。此外，该模型的注释κ值为0.6183，与细胞病理学家相当，优于训练有素的支气管镜医师和技术人员：HMLCS模型在肺部病变或胸腔内淋巴结病的多分类中表现出良好的性能，有望在经支气管取样过程中为标本的初步诊断提供实时反馈：临床试验编号：不适用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Respiratory Research RESPIRATORY SYSTEM-

CiteScore

9.70

自引率

1.70%

发文量

314

审稿时长

4-8 weeks

期刊介绍： Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.