ArticleThe British journal of radiology2022
Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 17 citations in OpenAlex.
- An interpretable machine learning model based on CT imaging for predicting lymphovascular invasion and survival in bladder urothelial carcinoma: a multicenter study.BMC medical imaging · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram.European journal of medical research · 2024Article
- Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer.Annals of medicine and surgery (2012) · 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
- Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review.Annals of medicine and surgery (2012) · 2023Review
- CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study.The British journal of radiology · 2023Article
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022)Frontiers in oncology · 2022Article
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8 authors at 4 institutions in 1 country.
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Abstract
objectiveThe present study explored the value of preoperative CT radiomics in predicting lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC).
methodsA retrospective analysis of 294 pathologically confirmed ESCC patients undergoing surgical resection and their preoperative chest-enhanced CT arterial images were used to delineate the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Radiomics features were extracted from single-slice, three-slice, and full-volume regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) regression method was applied to select valuable radiomics features. Radiomics models were constructed using logistic regression method and were validated using leave group out cross-validation (LGOCV) method. The performance of the three models was evaluated using the receiver characteristic curve (ROC) and decision curve analysis (DCA).
resultsA total of 1218 radiomics features were separately extracted from single-slice ROIs, three-slice ROIs, and full-volume ROIs, and 16, 13 and 18 features, respectively, were retained after optimization and screening to construct a radiomics prediction model. The results showed that the AUC of the full-volume model was higher than that of the single-slice and three-slice models. According to LGOCV, the full-volume model showed the highest mean AUC for the training cohort and the validation cohort.
conclusionThe full-volume radiomics model has the best predictive performance and thus can be used as an auxiliary method for clinical treatment decision making. ADVANCES IN KNOWLEDGE: LVI is considered to be an important initial step for tumor dissemination. CT radiomics features correlate with LVI in ESCC and can be used as potential biomarkers for predicting LVI in ESCC.
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