Evidence map›Paper›PMID 38846271›Full record

ArticleQuantitative imaging in medicine and surgery2024

Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.

En-Kuei Tang, Yun-Ju Wu, Chi-Shen Chen, Fu-Zong Wu

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Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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12citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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12 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

En-Kuei TangDepartment of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung.
Yun-Ju WuDepartment of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung.
Chi-Shen ChenPhysical Examination Center, Kaohsiung Veterans General Hospital, Kaohsiung.
Fu-Zong WuDepartment of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Prediction of subsolid nodule (SSN) interval growth is crucial for clinical management and decision making in lung cancer screening program. To the best of our knowledge, no study has investigated whether volume doubling time (VDT) is an independent factor for predicting SSN interval growth, or whether its predictive power is better than that of traditional semantic methods, such as nodular diameter or type. This study aimed to investigate whether VDT could provide added value in predicting the long-term natural course of SSNs (<3 cm) regarding stage shift. Methods: This retrospective study enrolled 132 patients with spectrum lesions of lung adenocarcinoma who underwent two consecutive computed tomography (CT) examinations before surgical tissue proofing between 2012 and 2021 in Kaohsiung Veterans General Hospital. The VDTs were manually calculated from the volumetric segmentation using Schwartz's approximation formula. We utilized logistic regression to identify predictors associated with stage shift progression based on the VDT parameter. Results: The average duration of follow-up period was 3.629 years. A VDT-based nomogram model (model 2) based on CT semantic features, clinical characteristics, and the VDT parameter yielded an area under the curve (AUC) of 0.877 [95% confidence interval (CI): 0.807-0.928]. Compared with model 1 (CT semantic features and clinical characteristics), model 2 exhibited the better predictive performance for stage shift (AUC model 1: 0.833 versus AUC model 2: 0.877, P=0.047). In model 2, significant predictors of stage shift growth included initial nodule size [odds ratio (OR) =4.074, 95% CI: 1.368-12.135; P=0.012], SSN classification (OR =0.042; 95% CI: 0.006-0.288; P=0.001), follow-up period (OR =1.692, 95% CI: 1.337-2.140; P<0.001), and VDT classification (OR =2.327, 95% CI: 1.368-3.958; P=0.002). For the stage shift, the mean progression time for the VDT (>400 d) group was 7.595 years, and median progression time was 7.430 years. Additionally, a VDT ≤400 d is an important prognostic factor associated with aggressive growth behavior with a stage shift. Conclusions: VDT is crucial for predicting SSN stage shift growth irrespective of clinical and CT semantic features. This highlights its significance in informing follow-up protocols and surgical planning, emphasizing its prognostic value in predicting SSN growth.

Indexed as

adenocarcinomadisease progressionLung neoplasmsnatural history

Identifiers

PMID38846271
PMCPMC11151246

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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.