Evidence map›Paper›PMID 37568768›Full record

ArticleCancers2023

Prediction of Surgical Outcome by Tumor Volume Doubling Time via Stereo Imaging Software in Early Non-Small Cell Lung Cancer.

Chia-Chi Liu, Ya-Fu Cheng, Pei-Cing Ke, Yi-Ling Chen, Ching-Min Lin, Bing-Yen Wang

Abstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Chia-Chi LiuDivision of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua 50006, Taiwan.ORCID 0000-0003-1662-1218
Ya-Fu ChengDivision of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua 50006, Taiwan.
Pei-Cing KeDivision of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua 50006, Taiwan.
Yi-Ling ChenSurgery Clinical Research Center, Changhua Christian Hospital, Changhua 50006, Taiwan.
Ching-Min LinDivision of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua 50006, Taiwan.ORCID 0000-0002-0139-9397
Bing-Yen WangDivision of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua 50006, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVolume doubling time (VDT) has been proven to be a powerful predictor of lung cancer progression. In non-small cell lung cancer patients receiving sublobar resection, the discussion of correlation between VDT and surgery was absent. We proposed to investigate the surgical outcomes according to VDT.

methodsWe retrospectively studied 96 cases post sublobar resection from 2012 to 2018, collecting two chest CT scans preoperatively of each case and calculating the VDT. The receiver operating characteristic curve was constructed to identify the optimal cut-off point of VDTs as 133 days. We divided patients into two groups: VDT < 133 days and VDT ≥ 133 days. Univariable and multivariable analyses were performed for comparative purposes.

resultsUnivariable and multivariable analyses revealed that the consolidation and tumor diameter ratio was the factor of overall survival (OS), and VDT was the only factor of disease-free survival (DFS). The five year OS rates of patients with VDTs ≥ 133 days and VDTs < 133 days, respectively, were 89.9% and 71.9%, and the five year DFS rates were 95.9% and 61.5%.

conclusionAs VDT serves as a powerful prognostic predictor and provides an essential role in planning surgical procedures, the evaluation of VDT preoperatively is highly suggested.

Indexed as

non-small cell lung cancersegmentectomysurvival ratevolume doubling timewedge resection

Identifiers

PMID37568768
PMCPMC10417538

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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.