ArticleJournal of cancer research and clinical oncology2023
Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 13 citations in OpenAlex.
- GAN-generated target reconstruction CT radiomics for prediction of the novel IASLC grading of pulmonary adenocarcinoma.Journal of thoracic disease · 2026Article
- Assessment of high-grade pattern in lung invasive nonmucinous adenocarcinoma based on multiparametric features from dual-layer detector spectral computed tomography combined with extracellular volume fraction.Quantitative imaging in medicine and surgery · 2026Article
- Explainable multimodal fusion model integrating clinical and quantitative CT features for preoperative prediction of high-grade histologic patterns in invasive lung adenocarcinoma.Frontiers in oncology · 2026Article
- [Predicting Invasive Non-mucinous Lung Adenocarcinoma IASLC Grading: A Nomogram Based on Dual-energy CT Imaging and Conventional Features].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma.Scientific reports · 2025Article
- Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.BMC cancer · 2025Article
- Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines.Insights into imaging · 2025Review
- Sublobar resection for lung adenocarcinoma less than 2 cm containing solid or micropapillary components radiologically presented as consolidation-to-tumor ratio (CTR) ≤0.25 [ground-glass opacity (GGO)].Translational lung cancer research · 2024Article
- Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less.Frontiers in oncology · 2024Article
- Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT).
methodsThrough pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram.
resultsThe multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P = 0.029 OR = 4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR = 1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model.
conclusionThe combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.
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.