ArticleEuropean journal of nuclear medicine and molecular imaging2021
Histologic subtype classification of non-small cell lung cancer using PET/CT images.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
97 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- PET radiomics for histologic subtype classification of non-small cell lung cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images.Journal of digital imaging · 2023Trial
- Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.European radiology · 2023Trial
- 3D multi-omics tumour atlases: from technology to biology and clinical translation.Nature reviews. Cancer · 2026Review
- Quantitative CT analysis in lung cancer research: a bibliometric analysis based on Web of Science [2005-2025].Journal of thoracic disease · 2026Article
- Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer.Translational lung cancer research · 2026Article
- A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.Journal of imaging informatics in medicine · 2026Article
- Machine-learning with multiomics for radiotherapy-induced acute rectal toxicities and survival outcome in patients with cervical cancer.Radiation oncology (London, England) · 2026Article
- Dysregulated TMPO-AS1/let-7b-5p/PLK1/E2F1 Axis Associated with Poor Prognosis in Lung Adenocarcinoma.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- MetaPaCS: A Novel Meta-Learning Framework for Pancreatic Cancer Subtype Identification.bioRxiv : the preprint server for biology · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion.Quantitative imaging in medicine and surgery · 2026Article
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- PET-based radiomic analysis in multicentre lung cancer study and impact of feature domain harmonization.Physical and engineering sciences in medicine · 2025Article
- A computed tomography-based deep learning radiomics for predicting the response to neoadjuvant chemotherapy combined with immunotherapy in patients with locally advanced esophageal cancer: a multicenter cohort study.Journal of thoracic disease · 2025Article
- NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.Scientific reports · 2025Article
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
37 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
purposesTo evaluate the capability of PET/CT images for differentiating the histologic subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from radiomics-based machine learning/deep learning algorithms.
methodsIn this study, 867 patients with adenocarcinoma (ADC) and 552 patients with squamous cell carcinoma (SCC) were retrospectively analysed. A stratified random sample of 283 patients (20%) was used as the testing set (173 ADC and 110 SCC); the remaining data were used as the training set. A total of 688 features were extracted from each outlined tumour region. Ten feature selection techniques, ten machine learning (ML) models and the VGG16 deep learning (DL) algorithm were evaluated to construct an optimal classification model for the differential diagnosis of ADC and SCC. Tenfold cross-validation and grid search technique were employed to evaluate and optimize the model hyperparameters on the training dataset. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity and specificity was used to evaluate the performance of the models on the test dataset.
resultsFifty top-ranked subset features were selected by each feature selection technique for classification. The linear discriminant analysis (LDA) (AUROC, 0.863; accuracy, 0.794) and support vector machine (SVM) (AUROC, 0.863; accuracy, 0.792) classifiers, both of which coupled with the ℓ
conclusionEmploying radiomic machine learning/deep learning algorithms could help radiologists to differentiate the histologic subtypes of NSCLC via PET/CT images.
Indexed as
Identifiers
32776232What 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.