非小细胞肺癌立体定向体放射治疗中的放射组学:系统综述和放射组学质量评分研究。

Radiation oncology journal Pub Date : 2024-03-01 Epub Date: 2024-02-21 DOI:10.3857/roj.2023.00612
Ben Man Fei Cheung
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引用次数: 0

摘要

目的:立体定向体放射治疗(SBRT)已被广泛用于早期非小细胞肺癌(NSCLC)的根治性治疗。它取得了与手术相当的良好局部控制率。目前,SBRT 的疗效或并发症预测还没有标准的风险模型。放射组学有望改善临床结果预后。在此,我们回顾了通过使用放射质量评分(RQS)对胸部 SBRT 进行放射学分析的现有文献:在 PubMed 和 Embase 上进行文献检索,检索有关早期 NSCLC SBRT 的放射组学研究。文献检索包括截至 2021 年 6 月的研究。仅包括发表在同行评审期刊上的论文全文。排除了包括转移性肺癌或非肺癌的研究。两名独立调查人员使用RQS对每项研究进行评估,并通过讨论解决差异:共分析了 25 项研究。平均 RQS 为 7.76,最高分为 36 分。这相当于最高分的 21.56%。缺乏特征缩减策略、外部验证和开放数据共享被认为是所审查研究的主要局限性。同时,在不同的研究中发现了各种常见的放射体征,如灰度共现矩阵同质性和能量。此外,还综述了多种稳健的放射线组学模型,这些模型可改善预后或并发症预测:结论:放射组学作为一种预后预测工具,在胸部 SBRT 中的应用前景非常广阔。然而,还需要更大规模的多中心前瞻性研究来确认放射组学特征。未来研究方法的改进也能促进其更广泛的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study.

Purpose: Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS).

Materials and methods: Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion.

Results: A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction.

Conclusion: Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.

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