Evidence map›Paper›PMID 41405691›Full record

ArticleEuropean radiology2026

Development of PET/CT-clinical nomograms for predicting lymph node metastasis in primary lung cancer.

Xiaoyu Han, Wentao Li, Julie Lin, Shuoyang Li, Maliazurina B Saad, Yuliya Kitsel, Simon Heeke, Lingzhi Hong, Mohamed Qayati Mohamed, Xiuning Le and 12 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Xiaoyu Han *Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA.
Wentao Li *Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA.
Julie Lin *Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, USA.
Shuoyang Li *Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA.
Maliazurina B SaadDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA.
Yuliya KitselDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Simon HeekeDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Lingzhi HongDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Mohamed Qayati MohamedDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA.
Xiuning LeDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Natalie VokesDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Myrna Cb GodoyDepartment of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.
Brett W CarterDepartment of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.
Girish S ShroffDepartment of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.
George EapenDepartment of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, USA.
Lauren A ByersDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Ara A VaporciyanDepartment of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, USA.
Don L GibbonsDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
John HeymachDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Carol C WuDepartment of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.
Jianjun ZhangDepartment of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
Jia WuDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA. jwu11@mdanderson.org.ORCID http://orcid.org/0000-0001-8392-8338

Funding

Integrated blood and radiomic subtyping to guide immunotherapy treatment selection and early response assessment in metastatic non-small cell lung cancerR01CA276178 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Natalie Vokes, Jia Wu · 2023 to 2026
$2.6M
MD Anderson Cancer Center Support Grant P30CA016672National Institutes of Health (NIH) grants R01CA276178
6 · The paper itself

Abstract

objectiveAccurate staging of primary lung cancer is crucial for optimizing therapeutic strategies but remains challenging in clinical practice. We aimed to develop a nomogram incorporating clinical characteristics with CT and PET findings to predict lymph node metastasis (LNM) in primary lung cancer. MATERIALS AND

methodsWe retrospectively analyzed patients with primary lung cancer and mediastinal and hilar LNs from a tertiary care cancer center. All patients underwent endobronchial ultrasound-guided transbronchial needle aspiration, with diagnostic chest CT and PET-CT. Cytological confirmation of transbronchial needle aspiration samples served as the gold standard for diagnosing LNM. We employed an LN-level modeling approach and constructed five models for independent prediction of LNM: (1) Clinical-CT-PET model, (2) Clinical-CT model, (3) PET model, (4) Clinical-PET model, and (5) CT-PET model. Their performance was further evaluated in the subgroup of LNs < 1 cm.

resultsThis study included 455 patients (mean age 70 ± 10 years; 55.4% male), predominantly adenocarcinoma (62.0%). Most (68.1%) were stage III-IV. In total, 1391 lymph nodes (1112 training, 279 testing) were analyzed to develop and validate the nomogram. The Clinical-CT-PET model achieved the best diagnostic performance, with AUCs of 0.883 (training cohort) and 0.877 (test cohort), sensitivities of 79.5% and 80.0%, and specificities of 87.1% and 86.9%, respectively. For small LNs, it showed higher AUC (0.797 vs. 0.722, p < 0.001) and sensitivity (71.4% vs. 52%) compared to the PET model.

conclusionWe developed a nomogram that noninvasively estimates the risk of LNM in lung cancer that may inform individualized preoperative assessment and evidence-based decision-making. KEY POINTS: Question Can a nomogram integrating clinical, CT, and PET features improve preoperative prediction of lymph node metastasis in primary lung cancer, particularly in small nodes? Findings We developed a Clinical-CT-PET nomogram that achieved the best diagnostic accuracy (AUCs 0.883 and 0.877) among five models, especially for small lymph nodes (< 1 cm). Clinical relevance This noninvasive Clinical-CT-PET nomogram may improve the accuracy of preoperative lymph node staging and guide individualized treatment planning. It may also help avoid unnecessary invasive procedures in lung cancer patients, pending further multi-center validation.

Indexed as

Lung NeoplasmsLymphatic MetastasisNomogramsPositron Emission Tomography Computed TomographyAgedAged, 80 and overFemaleHumansLymph NodesMaleMiddle AgedNeoplasm StagingRetrospective StudiesSensitivity and SpecificityLung neoplasmsLymphatic metastasisNomogramsPositron emission tomography computed tomography

Identifiers

PMID41405691
PMCPMC13086717

What OpenQuestion holds

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Registered trials

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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.