ArticleJournal of thoracic disease2017
Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness.
Article in Journal of thoracic disease, 2017. 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, 36 citations in OpenAlex.
- Integration of CT radiomics and machine learning for preoperative T staging of esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer.World journal of radiology · 2025Article
- Bi-Regional Machine Learning Radiomics Based on CT Noninvasively Predicts LOX Expression Level and Overall Survival in Hepatocellular Carcinoma.Cancer medicine · 2025Article
- CT-based radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinoma.Frontiers in oncology · 2025Article
- Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis.Journal of clinical medicine · 2024Article
- Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.Japanese journal of radiology · 2024Article
- Novel Advances in Qualitative Diagnostic Imaging for Decision Making in Multidisciplinary Treatment for Advanced Esophageal Cancer.Journal of clinical medicine · 2024Review
- Computed tomography radiomics identification of T1-2 and T3-4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?Abdominal radiology (New York) · 2024Article
- Could tumour volume and major and minor axis based on CTA statistical anatomy improve the pre-operative T-stage in oesophageal cancer?Cancer medicine · 2023Article
- A radiomics nomogram based on contrast-enhanced CT for preoperative prediction of Lymphovascular invasion in esophageal squamous cell carcinoma.Frontiers in oncology · 2023Article
- Review
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography.Nagoya journal of medical science · 2022Article
- Cervical Cancer Imaging Features Associated WithFrontiers in neurology · 2022Article
- Radiomics Analysis of Multiparametric MRI for Prediction of Synchronous Lung Metastases in Osteosarcoma.Frontiers in oncology · 2022Article
- Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study.World journal of gastroenterology · 2021Article
- A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning.The British journal of radiology · 2021Article
- Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI.Cancers · 2021Review
- Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging.Computational and mathematical methods in medicine · 2021Article
- Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo explore the role of texture analysis of computed tomography (CT) images in preoperative assessment of esophageal squamous cell carcinoma (ESCC) aggressiveness.
methodsSeventy-three patients with pathologically confirmed ESCC underwent unenhanced and contrast enhanced CT imaging preoperatively. Texture analysis was performed on unenhanced and contrast enhanced CT images, respectively. Six CT texture parameters were obtained. One-way analysis of variance or independent-samples t-test (normality), independent-samples Kruskal-Wallis test or Mann-Whitney U test (non-normality), binary Logistic regression analysis (multivariable), Spearman correlation test, receiver operating characteristic (ROC) curve analysis and intraclass correlation coefficient (ICC) were used for statistical analyses.
resultsKurtosis was an independent predictor for T stages (T1-2
conclusionsTexture analysis of CT images held great potential in differentiating different T, N and overall stages of ESCC preoperatively, while failed to assess the differentiation degrees.
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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.