ArticleThoracic cancer2020
Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics.
Article in Thoracic cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 26 citations in OpenAlex.
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- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting Tumor Volume Doubling Time and Progression-Free Survival in Untreated Patients from Patient-Derived-Xenograft (PDX) Models: A Translational Model-Based Approach.The AAPS journal · 2024Article
- The correlation between KRAS and TP53 gene mutations and early growth of pulmonary nodules.Journal of cardiothoracic surgery · 2024Article
- Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.Quantitative imaging in medicine and surgery · 2024Article
- Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns.Diagnostics (Basel, Switzerland) · 2023Review
- Nondiagnostic, radial-probe endobronchial ultrasound-guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy.Thoracic cancer · 2023Article
- Correlation and significance of COX-2, Ki67, VEGF and other immune indexes with the growth of malignant pulmonary nodules.Journal of cardiothoracic surgery · 2022Article
- Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume.Quantitative imaging in medicine and surgery · 2022Article
- Prognostic Evaluation Based on Dual-TimeJournal of oncology · 2022Article
- Structural and functional radiomics for lung cancer.European journal of nuclear medicine and molecular imaging · 2021Review
- Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2021Article
- Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.Frontiers in oncology · 2021Article
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6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundBecause shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth.
methodsWe evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected.
resultsAmong the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness.
conclusionsThis study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: We found a relationship between margin-related radiomic features and tumor doubling time. WHAT THIS STUDY ADDS: Margin-related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies.
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