Evidence map›Paper›PMID 39747759›Full record

ReviewInsights into imaging2025

Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines.

Jinbao Feng, Xiaonan Shao, Jianxiong Gao, Xinyu Ge, Yan Sun, Yunmei Shi, Yuetao Wang, Rong Niu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Nonlinear association between standardized uptake value (SUV) index based onQuantitative imaging in medicine and surgery · 2026
    Article
  3. Article
  4. Article
  5. The study of dual-phaseScientific reports · 2025
    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

8 authors.

Jinbao FengDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Xiaonan ShaoDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Jianxiong GaoDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Xinyu GeDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Yan SunDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Yunmei ShiDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Yuetao WangDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China.
Rong NiuDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou Key Laboratory of Molecular Imaging, Changzhou, China. niurongookk@163.com.ORCID http://orcid.org/0000-0001-9482-207X

Funding

Changzhou Clinical Medical Center (Nuclear Medicine) CZZX202204Changzhou Science and Technology Program CJ20220228Outstanding Talent of Changzhou The 14th Five-Year Plan High-Level Health Talents Training Project 2022-260Top Talent of Changzhou The 14th Five-Year Plan High-Level Health Talents Training Project 2022CZBJ037, 2024CZBJ008Young Talent Development Plan of Changzhou Health Commission CZQM2022005
6 · The paper itself

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, with invasive non-mucinous adenocarcinoma (INMA) being the most common type and carrying a poor prognosis. In 2020, the International Association for the Study of Lung Cancer (IASLC) pathology committee proposed a new histological grading system, which offers more precise prognostic assessments by combining the proportions of major and high-grade histological patterns. Accurate identification of lung INMA grading is crucial for clinical diagnosis, treatment planning, and prognosis evaluation. Currently, non-invasive imaging methods (such as CT, PET/CT, and MRI) are increasingly being studied to predict the new grading of lung INMA, showing promising application prospects. This review outlines the establishment and prognostic efficiency of the new IASLC grading system, highlights the application and latest progress of non-invasive imaging techniques in predicting lung INMA grading, and discusses their role in personalized treatment of lung INMA and future research directions. CRITICAL RELEVANCE STATEMENT: The new IASLC grading system has important prognostic implications for patients with lung invasive non-mucinous adenocarcinoma (INMA), and non-invasive imaging methods can be used to predict it, thereby improving patient prognoses. KEY POINTS: The new IASLC grading system more accurately prognosticates for patients with lung INMA. Preoperative prediction of the new grading is challenging because of the complexity of INMA subtypes. It is feasible to apply non-invasive imaging methods to predict the new IASLC grading system.

Indexed as

IASLC gradingImagingInvasive non-mucinous adenocarcinomaLung neoplasm

Identifiers

PMID39747759
PMCPMC11695567

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.