Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Eur J Radiol. 2024 Feb;171:111314. doi: 10.1016/j.ejrad.2024.111314. Epub 2024 Jan 12.
To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies.
This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies.
Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall.
The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.
总结肺癌患者预后放射组学和深度学习特征的潜在生物学相关性，并评估现有研究的质量。
本研究检索了 PubMed、Embase、Web of Science Core Collection 和 Cochrane Library 等数据库，纳入了基于 CT 或 PET/CT 探讨与预后放射组学和深度学习特征的潜在生物学相关性、以预测肺癌患者预后的研究。提取纳入研究的患者和放射基因组学分析信息。使用放射组学质量评分（RQS）和预测模型风险偏倚评估工具评估这些研究的质量。
纳入了 12 项研究，共纳入了 2014 年至 2022 年的 7338 例患者。除了一项研究外，所有研究均为回顾性研究。6 项研究采用了有监督机器学习，其余研究采用了无监督机器学习方法。83.33%的研究分析了基因测序数据，16.67%的研究分析了组织病理学数据。最常用的探索预后特征生物学意义的方法是基因集富集分析和相关性分析。有监督学习文章的中位数 RQS 为 13.5（范围为 12-19），无监督学习文章的中位数 RQS 为 7.0（范围为 5-14）。总体而言，纳入的研究被评估为具有较高的偏倚风险。
数据驱动模型的可解释性的生物学基础主要集中在基因组学和组织病理学因素上，未来可能会通过更恰当的生物学解释来改善肺癌的预后。
