The “digital biopsy” in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT

医学 无线电技术 活检 肺癌 核医学 放射科 肿瘤科
作者
Lavinia Monaco,Elisabetta De Bernardi,Francesca Bono,Diego Cortinovis,Cinzia Crivellaro,Federica Elisei,Vincenzo L’Imperio,Claudio Landoni,Gregory Mathoux,Monica Musarra,Fabio Pagni,E Turolla,Cristina Messa,Luca Guerra
出处
期刊:European Journal of Nuclear Medicine and Molecular Imaging [Springer Science+Business Media]
卷期号:49 (10): 3401-3411 被引量:40
标识
DOI:10.1007/s00259-022-05783-z
摘要

PurposeThe present pilot study investigates the putative role of radiomics from [18F]FDG PET/CT scans to predict PD-L1 expression status in non-small cell lung cancer (NSCLC) patients.MethodsIn a retrospective cohort of 265 patients with biopsy-proven NSCLC, 86 with available PD-L1 immunohistochemical (IHC) assessment and [18F]FDG PET/CT scans have been selected to find putative metabolic markers that predict PD-L1 status (< 1%, 1–49%, and ≥ 50% as per tumor proportion score, clone 22C3). Metabolic parameters have been extracted from three different PET/CT scanners (Discovery 600, Discovery IQ, and Discovery MI) and radiomics features were computed with IBSI compliant algorithms on the original image and on images filtered with LLL and HHH coif1 wavelet, obtaining 527 features per tumor. Univariate and multivariate analysis have been performed to compare PD-L1 expression status and selected radiomic features.ResultsOf the 86 analyzed cases, 46 (53%) were negative for PD-L1 IHC, 13 (15%) showed low PD-L1 expression (1–49%), and 27 (31%) were strong expressors (≥ 50%). Maximum standardized uptake value (SUVmax) demonstrated a significant ability to discriminate strong expressor cases at univariate analysis (p = 0.032), but failed to discriminate PD-L1 positive patients (PD-L1 ≥ 1%). Three radiomics features appeared the ablest to discriminate strong expressors: (1) a feature representing the average high frequency lesion content in a spherical VOI (p = 0.009); (2) a feature assessing the correlation between adjacent voxels on the high frequency lesion content (p = 0.004); (3) a feature that emphasizes the presence of small zones with similar grey levels inside the lesion (p = 0.003). The tri-variate linear discriminant model combining the three features achieved a sensitivity of 81% and a specificity of 82% in the test. The ability of radiomics to predict PD-L1 positive patients was instead scarce.ConclusionsOur data indicate a possible role of the [18F]FDG PET radiomics in predicting strong PD-L1 expression; these preliminary data need to be confirmed on larger or single-scanner series.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
MM发布了新的文献求助10
2秒前
2秒前
4秒前
激情的寄灵完成签到,获得积分20
4秒前
5秒前
xxx发布了新的文献求助10
5秒前
11完成签到,获得积分10
5秒前
小杨完成签到 ,获得积分10
5秒前
6秒前
白白完成签到 ,获得积分10
6秒前
wq发布了新的文献求助10
6秒前
曲初雪发布了新的文献求助10
7秒前
YEGE完成签到 ,获得积分10
7秒前
7秒前
7秒前
Enoelle完成签到,获得积分10
10秒前
Dean应助激情的寄灵采纳,获得30
10秒前
10秒前
受伤雨南发布了新的文献求助10
11秒前
mescal完成签到,获得积分10
11秒前
浏阳河发布了新的文献求助10
12秒前
螃蟹发布了新的文献求助10
12秒前
14秒前
子车一斩完成签到,获得积分10
15秒前
xiaowentu发布了新的文献求助10
16秒前
谢大喵发布了新的文献求助10
17秒前
老迟到的绾绾给老迟到的绾绾的求助进行了留言
18秒前
Sylvia_J完成签到 ,获得积分10
18秒前
子车一斩发布了新的文献求助20
19秒前
赘婿应助hangzi采纳,获得10
19秒前
充电宝应助xdl120318采纳,获得10
20秒前
22秒前
小鱼完成签到,获得积分10
22秒前
若水完成签到,获得积分10
22秒前
popo完成签到,获得积分10
23秒前
什么也难不倒我完成签到 ,获得积分10
23秒前
xpxxj完成签到,获得积分10
23秒前
23秒前
25秒前
前行的灿完成签到 ,获得积分10
26秒前
高分求助中
(应助此贴封号)【重要!!请各位详细阅读】【科研通的精品贴汇总】 10000
Voyage au bout de la révolution: de Pékin à Sochaux 700
First Farmers: The Origins of Agricultural Societies, 2nd Edition 500
Simulation of High-NA EUV Lithography 400
Assessment of adverse effects of Alzheimer's disease medications: Analysis of notifications to Regional Pharmacovigilance Centers in Northwest France 400
The Rise & Fall of Classical Legal Thought 260
Tonal intuitions in "Tristan und Isolde" / by Brian Hyer 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4332959
求助须知:如何正确求助?哪些是违规求助? 3844853
关于积分的说明 12010289
捐赠科研通 3485463
什么是DOI,文献DOI怎么找? 1913011
邀请新用户注册赠送积分活动 956323
科研通“疑难数据库(出版商)”最低求助积分说明 857167