Evidence map›Paper›PMID 41299778›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025

Prognostic value of integrating CT morphological features and PET volumetric metabolic parameters in surgically resected stage IA non-small cell lung cancer: a two-center retrospective study.

Jingjie Shang, Xiaobei Duan, Yingjun He, Biao Wu, Qijun Cai, Xiaoling Cao, Yongjin Tang, Jinci Mai, Simin Tan, Huihu Li and 3 more

Abstract readMulticenter Study
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Jingjie Shang *Department of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Xiaobei Duan *Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, 529000, China.
Yingjun He *Department of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Biao WuDepartment of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Qijun CaiDepartment of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Xiaoling CaoDepartment of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Yongjin TangDepartment of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Jinci MaiDepartment of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, 529000, China.
Simin TanDepartment of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, 529000, China.
Huihu LiDepartment of Nuclear Medicine, Maoming People's Hospital, Maoming, 525000, China.
Xueying LingDepartment of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Binhao HuangDepartment of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, 529000, China. 13702288099@163.com.
Hao XuDepartment of Nuclear Medicine, PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China. txh@jnu.edu.cn.

Funding

Beijing Medical Award Foundation YXJL-2023-0866-0324The National Natural Science Foundation of China 82202225
6 · The paper itself

Abstract

backgroundBoth ground-glass opacity (GGO) components on computed tomography (CT) and metabolic information derived from positron emission tomography (PET) are associated with prognosis in early‑stage non-small cell lung cancer (NSCLC) patients. The purpose of this study was to evaluate whether their combined assessment could improve risk stratification in surgically resected stage IA NSCLC.

methodsWe retrospectively analyzed 212 consecutive patients with stage IA NSCLC from two medical centers who underwent 18F-FDG PET/CT before curative surgery between November 2012 and October 2024. The cohort was divided into feasibility evaluation (n = 152) and external validation (n = 60) groups. Primary tumor maximum standardized uptake values (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured. Optimal PET parameter cutoffs for progression-free survival (PFS) were determined using receiver operating characteristic curve analysis in the feasibility cohort. A two-point prognostic scoring system combining GGO features and the multivariable-identified prognostic PET parameters was developed and validated in both cohorts using log-rank tests.

resultsFor the feasibility cohort, the optimal cut-off values for the prediction of PFS were 6.2, 1.9 mL, and 11.7 for SUVmax, MTV, and TLG, respectively. Both patients with primary tumors exceeding these metabolic parameters and those in the GGO-negative subgroup showed significantly shorter PFS (all P < 0.05). Multivariable analysis identified GGO [hazard ratio (HR) = 8.137; 95% CI: 1.035–63.998; P = 0.046] and TLG (HR = 4.993; 95% CI: 1.831–13.612; P = 0.002) as independent prognostic factors, even after adjusting for histopathology and other PET parameters. The two-point prognostic scoring system, which consisted of GGO and TLG, stratified patients into low-, moderate-, and high-risk groups. In the feasibility cohort, PFS demonstrated statistically significant differences between the three groups (all P < 0.05), with similar trends in the external validation cohort.

conclusionsThe two-point prognostic scoring system, combining morphological GGO features with volumetric metabolic parameters TLG, provided a simple yet effective method for enhancing prognostic stratification in patients with surgically resected stage IA NSCLC.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsPositron Emission Tomography Computed TomographyTomography, X-Ray ComputedAgedFeasibility StudiesFemaleFluorodeoxyglucose F18HumansMaleMiddle AgedNeoplasm StagingPrognosisRadiopharmaceuticalsRetrospective StudiesTumor BurdenFluorodeoxyglucose F18RadiopharmaceuticalsGround-glass opacityNon-small cell lung cancerPET metabolic parametersRecurrence

Identifiers

PMID41299778
PMCPMC12659114

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.