ArticleMolecular imaging and biology2019
Texture Analysis on [
Article in Molecular imaging and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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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.
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Who cites it
40 citing papers in PubMed.
- UniPET: a unified approach for whole-body CT to PET translation.Frontiers in artificial intelligence · 2026Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 2025Article
- 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
- Image analysis:Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Clinicopathologic and metabolic variables fromAnnals of nuclear medicine · 2025Article
- Role of Textural Analysis Parameters Derived from FDG PET/CT in Diagnosing Cardiac Sarcoidosis.World journal of nuclear medicine · 2024Article
- Article
- Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.Frontiers in medicine · 2024Article
- CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [Abdominal radiology (New York) · 2023Article
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Diagnostic value of dynamicQuantitative imaging in medicine and surgery · 2023Article
- Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes.International journal of molecular sciences · 2023Article
- Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside.Frontiers in oncology · 2023Article
- Detection of Changes in CEA and ProGRP Levels in BALF of Patients with Peripheral Lung Cancer and the Relationship with CT Signs.Contrast media & molecular imaging · 2023Article
- Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [Journal of clinical medicine · 2022Article
- Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning.European radiology experimental · 2022Article
- Article
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe study aims to investigate the correlations between positron emission tomography (PET) texture features, X-ray computed tomography (CT) texture features, and histological subtypes in non-small-cell lung cancer evaluated with 2-deoxy-2-[ PROCEDURES: We retrospectively evaluated the baseline PET/CT scans of 81 patients with histologically proven non-small-cell lung cancer. Feature extraction and statistical analysis were carried out on the Matlab platform (MathWorks, Natick, USA).
resultsIntra-CT correlation analysis revealed a strong positive correlation between volume of the lesion (CT
conclusionsSignificant associations emerged between PET features, CT features, and histological type in NSCLC. Texture analysis on PET/CT shows potential to differentiate between histological types in patients with non-small-cell lung cancer.
Indexed as
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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.