Evidence map›Paper›PMID 39687006›Full record

ArticleiScience2024

Multimodal integration to identify the invasion status of lung adenocarcinoma intraoperatively.

Xueyun Tan, Feng Pan, Na Zhan, Sufei Wang, Zegang Dong, Yan Li, Guanghai Yang, Bo Huang, Yanran Duan, Hui Xia and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

19 authors.

Xueyun TanDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Feng PanDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Na ZhanDepartment of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Sufei WangDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Zegang DongSino-US Telemed (Wuhan) Co., Ltd, Wuhan 430064, China.
Yan LiDepartment of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Guanghai YangDepartment of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Bo HuangDepartment of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Yanran DuanDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Hui XiaDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Yaqi CaoDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Min ZhouDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Zhilei LvDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Qi HuangDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Shan TianDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Liang ZhangDepartment of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Mengmeng ZhouDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Lian YangDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Yang JinDepartment of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Disease, NHC Key Laboratory of Pulmonary Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating the invasiveness of lung adenocarcinoma is crucial for determining the appropriate surgical strategy, impacting postoperative outcomes. This study developed a multimodality model combining radiomics, intraoperative frozen section (FS) pathology, and clinical indicators to predict invasion status. The study enrolled 1,424 patients from two hospitals, divided into multimodal training, radiology testing, and pathology testing cohorts. A prospective validation cohort of 114 patients was selected between March and May 2023. The radiomics + pathology + clinical indicators multimodality model (multi-RPC model) achieved an area under the curve (AUC) of 0.921 (95% confidence interval [CI] 0.899-0.939) in the multimodal training cohort and 0.939 (95% CI 0.878-0.975) in the validation cohort, outperforming single- and dual-modality models. The multi-RPC model's predictive accuracy of 0.860 (95% CI 0.782-0.918) suggests that it could significantly reduce inappropriate surgical procedures, enhancing precision oncology by integrating multimodal information to guide surgical decisions.

Indexed as

Artificial intelligence applicationsMedical imagingOncology

Identifiers

PMID39687006
PMCPMC11647133

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.