ArticleTranslational lung cancer research2024
A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on follow-up CT imaging.
Article in Translational lung cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Development and validation of a nomogram integrating multi-dimensional clinical factors for predicting lung cancer-related mediastinal/hilar lymph node metastasis before endobronchial ultrasound-guided transbronchial needle aspiration.Journal of thoracic disease · 2026Article
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.Journal of thoracic disease · 2026Review
- A radiomics-based nomogram for preoperatively predicting the invasiveness of nodular lung adenocarcinoma: a multicenter study.Translational lung cancer research · 2026Article
- Ground-Glass Enhancement on Contrast-Enhanced Mammography: A CT-Inspired Qualitative Descriptor for Breast Lesion Characterization.Journal of clinical medicine · 2026Article
- Correlation between CT growth patterns and invasiveness progression in neoplastic subcentimeter sub-solid nodules.Annals of medicine · 2025Article
- Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant subcentimeter solid nodules.Journal of thoracic disease · 2025Article
- The value of relative CT attenuation in predicting invasiveness in patients with T1-stage lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.Thoracic cancer · 2025Article
- Natural course of lung adenocarcinoma manifesting as ground-glass nodules: invasiveness assessment based on growth evaluation.Translational lung cancer research · 2025Article
- Development and validation of CT radiomics diagnostic models: differentiating benign from malignant pulmonary nodules and evaluating malignancy degree.Journal of thoracic disease · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Different pathological stages of lung adenocarcinoma require different surgical strategies and have varying prognoses. Predicting their invasiveness is clinically important. This study aims to develop a nomogram to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) based on follow-up computed tomography (CT) imaging. Methods: We retrospectively collected data of 623 GGNs from 601 patients who underwent two follow-up chest CT scans and were confirmed as lung adenocarcinoma by postoperative pathology between June 2017 and August 2023. These patients were randomly divided into training and testing sets in a 7:3 ratio. Eighty-seven GGNs from 86 patients who underwent surgery between September 2023 and April 2024 were prospectively collected as a validation set. The volume, mean density, solid component volume (SV), percentage of solid component (PSC), and mass of GGNs were evaluated using the InferRead CT Lung software. Patients were classified into Group A (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and Group B (invasive adenocarcinoma). Three predictive models were established: model 1 utilized clinical characteristics and morphological features on pre-surgical CT, model 2 incorporated clinical characteristics, morphological features and quantitative parameters on pre-surgical CT, and model 3 utilized all selected features on baseline and pre-surgical CT. Results: Model 3 achieved a satisfying area under the curves values of 0.911, 0.893, and 0.932 in the training, testing, and validation sets, respectively, demonstrating superior predictive performance than model1 (0.855, 0.858, and 0.816) and model2 (0.895, 0.891, and 0.903). A nomogram was constructed based on model 3. Calibration curves showed a good fit, and decision curve analysis showed that the nomogram was clinically useful. Conclusions: The nomogram based on morphological features and quantitative parameters from follow-up CT images showed good discrimination and calibration abilities in predicting the invasiveness of lung adenocarcinoma manifesting as GGNs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.