ArticleTranslational oncology2020
Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma.
Article in Translational oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04893200 (Could Radiomics Predict Tumor Spread Through Air Space in Lung Adenocarcinoma in All Computed Tomography Settings?), which is not on this map. Cited by 49 papers, 2 of them syntheses that pooled it.
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.
Could Radiomics Predict Tumor Spread Through Air Space in Lung Adenocarcinoma in All Computed Tomography Settings?
Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it, 72 citations in OpenAlex.
- Diagnostic performance of radiomics analysis for pulmonary cancer airway spread: a systematic review and meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Pooled it
- Research hotspots and trends in lung cancer STAS: a bibliometric and visualization analysis.Frontiers in oncology · 2024Pooled it
- A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma.BMC medical imaging · 2025Trial
- Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma.Scientific reports · 2022Trial
- STAS More than a Prognostic Marker-An Evolving Factor in Operative and Adjuvant Treatment Decisions in Early-Stage NSCLC.Cancers · 2026Review
- Imaging advances of predicting spread through air spaces (STAS) in lung cancer: a narrative review.Journal of thoracic disease · 2026Review
- Factors predicting spread through air spaces (STAS) preoperative for T1a-b non-small cell lung cancer.Discover oncology · 2026Review
- Machine learning-based quantitative prediction of spread through air spaces in primary lung adenocarcinoma using intratumoural heterogeneity scores.International journal of surgery (London, England) · 2025Article
- Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI.Journal of imaging informatics in medicine · 2025Article
- A radiomics-based machine learning model and SHAP for predicting spread through air spaces and its prognostic implications in stage I lung adenocarcinoma: a multicenter cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models.Journal of cardiothoracic surgery · 2025Article
- Utilizing intratumoral and peritumoral ultrasound radiomics for predicting KRAS gene mutation status in rectal cancer patients.Journal of applied clinical medical physics · 2025Article
- Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer.NPJ precision oncology · 2025Article
- Article
- CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study.European journal of radiology open · 2025Article
- A narrative review of preoperative CT for predicting spread through air spaces of lung cancer.Translational lung cancer research · 2025Review
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- To predict the spread through air spaces in lung adenocarcinoma using radiomic features from different regions of part-solid nodules: a multicenter study.Frontiers in oncology · 2025Article
- EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Frontiers in oncology · 2025Article
- Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.Gland surgery · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97-1.00] and test set (AUC, 0.99; 95% CI, 0.97-1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.
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.