ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
Growth dynamics of lung nodules: implications for classification in lung cancer screening.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Has computer-aided diagnosis now outperformed radiologists in the accurate localization of lung nodules?-a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Radiologists' diagnostic confidence in surgically referred pulmonary nodules: a routine computed tomography report retrospective analysis.BMC pulmonary medicine · 2026Article
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- Biopsy-Proven Pulmonary Tumors Detected by LDCT: A 10-Year Single-Center Study of Growth Patterns and Diagnostic Pitfalls.International journal of medical sciences · 2026Article
- Improvement of machine learning models for predicting high-grade subtypes of lung adenocarcinoma based on delta radiomics: A multicenter cohort study.European journal of radiology open · 2025Article
- A stochastic modelling framework for cancer patient trajectories: combining tumour growth, metastasis, and survival.Journal of mathematical biology · 2025Article
- Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation.Quantitative imaging in medicine and surgery · 2025Article
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5 authors.
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Abstract
backgroundLung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.
methodsData were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.
resultsMost lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).
conclusionsIn our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.
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