ArticleCancer biomarkers : section A of Disease markers2022
Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers.
Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Diagnostic utility of tumor volume doubling time in ciliated muconodular papillary tumor/bronchiolar adenoma mimicking invasive mucinous adenocarcinoma: A case report.Radiology case reports · 2026Article
- Towards optimized CT lung cancer screening scan protocols.The British journal of radiology · 2026Article
- 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
- Analysis of factors influencing tumor volume doubling time in hepatocellular carcinoma and its predictive value for progression-free survival.Scientific reports · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.Journal of translational medicine · 2024Article
- A solitary pulmonary nodule in a former glass blower mimicking a primary lung adenocarcinoma.International journal of surgery case reports · 2024Article
- Slowly Growing Pulmonary Glandular Papilloma with Air Bronchogram: A Case Report.Journal of the Belgian Society of Radiology · 2024Article
- Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns.Diagnostics (Basel, Switzerland) · 2023Review
- Establishing a predictive model for tumor mutation burden status based on CT radiomics and clinical features of non-small cell lung cancer patients.Translational lung cancer research · 2023Article
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Abstract
backgroundImage-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes.
methodsData and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes.
resultsDecision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS).
conclusionWe utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.
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