Evidence map›Paper›PMID 39651734›Full record

ArticleCancer medicine2024

Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma.

Haitao Sun, Zhaohui Peng, Guoyue Chen, Zhengjun Dai, Jian Yao, Peng Zhou

Abstract read
In one paragraph

Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Haitao SunMedical Imaging Center of Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Zhaohui PengMedical Imaging Center of Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Guoyue ChenMedical Imaging Center of Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Zhengjun DaiScientific Research Department of Huiying Medical Technology Co. Ltd, Beijing, China.
Jian YaoMedical Imaging Center of Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Peng ZhouMedical Imaging Center of Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.ORCID 0009-0004-0071-5687

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe objective is to establish a radiomics nomogram (Rad-nomogram) using dual-phase enhanced computed tomography (DPE-CT) for the prediction of progression-free survival (PFS) in patients diagnosed with stage IV lung adenocarcinoma (ADC).

methodsFrom DPE-CT scans, radiomic characteristics were retrieved from 133 patients diagnosed with stage IV lung ADC. Clinical data were analyzed using univariate and multivariate Cox regression analyses. The radiomics signature was combined with clinical features employing multivariate Cox analysis in order to develop a Rad-nomogram. The predictive efficiency of the nomogram was evaluated using survival studies, such as Kaplan-Meier curves and Harrell's C-index. The benefits and clinical utility of various models were compared using the net reclassification index (NRI), decision curve analysis (DCA), and integrated discrimination improvement (IDI).

resultsIn the test cohort, the C-indexes for the clinical, artery, and vein phase CT models were 0.675, 0.691, and 0.678, respectively. The dual-phase achieved a C-index of 0.731, exceeding the CT model, while the developed nomogram reached a C-index of 0.783. The Kaplan-Meier survival study classified patients into low-risk and high-risk groups related to PFS using the Rad-nomogram (p < 0.05). The Rad-nomogram demonstrated a greater net advantage when compared with clinical and Rad models, as indicated by positive values of the NRI and IDI (ranging from 11.6% to 52.6%, p < 0.05).

conclusionThe Rad-nomogram, employing DPE-CT scans, offers a promising approach to predict PFS in individuals diagnosed with stage IV lung ADC.

Indexed as

Adenocarcinoma of LungLung NeoplasmsNeoplasm StagingNomogramsProgression-Free SurvivalTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansKaplan-Meier EstimateMaleMiddle AgedRadiomicsRetrospective Studiescomputed tomographylung adenocarcinomanomogramprogression‐free survivalradiomics

Identifiers

PMID39651734
PMCPMC11626483

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