ArticleQuantitative imaging in medicine and surgery2024
Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.
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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Who cites it
12 citing papers in PubMed.
- Assessment of risk factors in persistent subsolid pulmonary nodules based on CT dynamic follow-up.BMC medical imaging · 2026Article
- Quantitative Tomographic Analysis as a Prognostic Tool in Connective Tissue Disease-Associated Interstitial Lung Disease.Diagnostics (Basel, Switzerland) · 2026Article
- The role of growth heterogeneity in solid nodular non-small cell lung cancer in clinical practice: a narrative review.Journal of thoracic disease · 2026Review
- Incidental pulmonary findings on CT in daily practice: the nodule and the interstitial lung abnormalities - what's old, what's new.La Radiologia medica · 2026Review
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- Management of suspicious nodules in lung cancer screening - a narrative review of monitoring strategies based on nodule features and further needs.Frontiers in oncology · 2026Review
- The value of relative CT attenuation in predicting invasiveness in patients with T1-stage lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Impact of Spread Through Air Spaces (STAS) on Recurrence and Surgical Trends in Stage I Non-Small Cell Lung Cancer: A Real-World Cohort Study.The Kaohsiung journal of medical sciences · 2025Article
- Consensus clustering based on CT radiomics has potential for risk stratification of patients with clinical T1 stage lung adenocarcinoma.BMC medical imaging · 2025Article
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- Multi-type classification of lung nodules based on CT radiomics and ensemble learning for diversity weighting.Quantitative imaging in medicine and surgery · 2024Article
- Development and external validation of a nomogram for predicting lymph node metastasis in 1-3 cm lung adenocarcinoma.Future oncology (London, England) · 2024Article
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4 authors.
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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.
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