ArticleTherapeutic advances in respiratory disease
A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.
Article in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- CT-based deep learning radiomics predicts micropapillary/solid components in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma.Journal of thoracic disease · 2026Article
- GAN-generated target reconstruction CT radiomics for prediction of the novel IASLC grading of pulmonary adenocarcinoma.Journal of thoracic disease · 2026Article
- The Prognostic Significance of the Second Predominant Histological Pattern in Resected Early-Stage Lung Adenocarcinoma: A Retrospective Cohort Analysis.Journal of clinical medicine · 2026Article
- CT radiomics approach to predict response to bronchial arterial chemoembolization in advanced lung cancer by multicenter study.Scientific reports · 2026Article
- Multimodal radiomics for precision management of colorectal cancer.Discover oncology · 2026Review
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- Integrating deep learning and radiomics in the differentiation of major histological subtypes of invasive non-mucinous lung adenocarcinoma using positron emission tomography and computed tomography.Translational lung cancer research · 2025Article
- Proteomics-Empowered Microfluidic-SERS Immunoassay for Identifying and Detecting Biomarkers of Micropapillary Lung Adenocarcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Clinical-Radiomics Nomogram for the Preoperative Prediction of Aggressive Micropapillary and a Solid Pattern in Lung Adenocarcinoma.Current oncology (Toronto, Ont.) · 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
- AI-based prediction of pathological risk factors in lung adenocarcinoma from CT imaging: bridging innovation and clinical practice.Frontiers in oncology · 2025Review
- CYFRA 21-1 predicts efficacy of combined chemoimmunotherapy in patients with advanced non-small cell lung cancer: a prospective observational study.Translational lung cancer research · 2024Article
- Article
- A novel decision tree algorithm model based on chest CT parameters to predict the risk of recurrence and metastasis in surgically resected stage I synchronous multiple primary lung cancer.Therapeutic advances in respiratory diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInvasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment.
objectiveThis study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers.
designA retrospective case control, diagnostic accuracy study.
methodsThis study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.
resultsThe radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively.
conclusionThis study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.
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.