ArticleJournal of cancer research and clinical oncology2023
Radiomics nomogram for the prediction of Ki-67 index in advanced non-small cell lung cancer based on dual-phase enhanced computed tomography.
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, 1 of them a synthesis 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.
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, 1 synthesis or guideline pooled it.
- CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Nomogram combining visual imaging and radiomics analysis for distinguishing diffuse hemispheric glioma, H3 G34-mutant from adult-type diffuse high-grade gliomas.Neuroradiology · 2026Article
- Radiomics features combined with clinical and CT features for predicting the Ki-67 index in stage T1 non-small cell lung cancer patients: a multicenter study.Journal of thoracic disease · 2026Article
- Prediction of the Ki-67 proliferation index in lung adenocarcinoma using an interpretable CT-based deep learning radiomics model: a two-center study.BMC pulmonary medicine · 2025Article
- Review
- Predicting Ki-67 expression levels in non-small cell lung cancer using an explainable CT-based deep learning radiomics model.Frontiers in oncology · 2025Article
- Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma.Cancer medicine · 2024Article
- CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis.Experimental and therapeutic medicine · 2024Article
- Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.Biomedical engineering online · 2023Article
- Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purposeTo develop a radiomics nomogram based on dual-phase enhanced computed tomography (CT) for predicting the Ki-67 index status in patients with advanced non-small cell lung cancer (NSCLC).
methods137 patients with NSCLC who had undergone dual-phase enhanced CT scans and Ki-67 examination within 2 weeks were retrospectively enrolled between January 2020 and December 2022. Clinical and laboratory data were collected, and the patients were categorized based on low or high expression of Ki-67 index, with a cut-off value of 40%. The cohort was randomly divided into a training group (n = 95) and a testing group (n = 42) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select the most valuable radiomics features from the dual-phase enhanced CT images. Subsequently, a nomogram that incorporated the radiomics score and clinical factors associated with Ki-67 index status was established through univariate and multivariate logistic regression analyses. The predictive performance of the nomogram was evaluated using the area under the curve (AUC).
resultsThe AUC values of the radiomics features of artery phase and vein phase CT in the testing group were 0.748 and 0.758, respectively. The AUC of the dual-phase enhanced CT was 0.785, and the AUC of the developed nomogram was 0.859, which was higher than those of the radiomics (AUC, 0.785) and clinical models (AUC, 0.736).
conclusionsThe radiomics nomogram based on dual-phase enhanced CT images provides a promising method for predicting the Ki-67 index status in patients with advanced NSCLC.
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.