ArticleFrontiers in oncology2022
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 28 citations in OpenAlex.
- The prognostic value of CT-quantified visceral and subcutaneous fat areas in sepsis.World journal of emergency medicine · 2026Article
- Prognostic and Diagnostic Value of Node-RADS for Non-Small Cell Lung Cancer Following Neoadjuvant Therapy: A Multicenter Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Based 18F-FDG PET/CT Radiomics for Mediastinal Lymph Node Staging in Non-Small Cell Lung Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Predictive value of primary tumorCancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Development and external validation of a FDG PET-based radiomics model predicting occult lymph node metastasis in non-small cell lung cancer patients.European journal of nuclear medicine and molecular imaging · 2026Article
- AptoDetect™-Lung Assay as a Blood-Based Predictor of Advanced-Stage Lung Cancer in Patients with Lung-RADS 3-4 Pulmonary Nodules: A Multicenter Prospective Cohort Study.Biomedicines · 2026Article
- Article
- Development of a Simplified Protocol for Respiratory Muscle Segmentation in Unenhanced Chest CT and Identification of New Radiomic Biomarkers of Sarcopenia in Lung Diseases: A Retrospective Study.Journal of clinical medicine · 2025Article
- Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis.World journal of clinical oncology · 2025Review
- CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis.BMC pediatrics · 2025Article
- Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Development and validation of a nomogram model for predicting lymph node metastasis in early non-small-cell lung cancer.American journal of cancer research · 2025Article
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- Predictive value of radiomics modeling based on dynamic and staticFrontiers in oncology · 2025Article
- Machine learning-based radiomics for guiding lymph node dissection in clinical stage I lung adenocarcinoma: a multicenter retrospective study.Translational lung cancer research · 2024Article
- Preoperative prediction of occult lymph node metastasis in patients with non-small cell lung cancer: a simple and widely applicable model.BMC pulmonary medicine · 2024Article
- Prognostic factors of resectable anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) patients: a retrospective analysis based on a single center.Translational lung cancer research · 2024Article
- Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer.Frontiers in oncology · 2024Article
- Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To investigate the ability of a PET/CT-based radiomics nomogram to predict occult lymph node metastasis in patients with clinical stage N0 non-small cell lung cancer (NSCLC). Materials and methods: This retrospective study included 228 patients with surgically confirmed NSCLC (training set, 159 patients; testing set, 69 patients). ITKsnap3.8.0 was used for image(CT and PET images) segmentation, AK version 3.2.0 was used for radiomics feature extraction, and Python3.7.0 was used for radiomics feature screening. A radiomics model for predicting occult lymph node metastasis was established using a logistic regression algorithm. A nomogram was constructed by combining radiomics scores with selected clinical predictors. Receiver operating characteristic (ROC) curves were used to verify the performance of the radiomics model and nomogram in the training and testing sets. Results: The radiomics nomogram comprising six selected features achieved good prediction efficiency, including radiomics characteristics and tumor location information (central or peripheral), which demonstrated good calibration and discrimination ability in the training (area under the ROC curve [AUC] = 0.884, 95% confidence interval [CI]: 0.826-0.941) and testing (AUC = 0.881, 95% CI: 0.8031-0.959) sets. Clinical decision curves demonstrated that the nomogram was clinically useful. Conclusion: The PET/CT-based radiomics nomogram is a noninvasive tool for predicting occult lymph node metastasis in NSCLC.
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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.