ArticleThoracic cancer2023
The T stage of esophageal cancer can be effectively predicted by muscularis propria thickness and muscularis propria + mucosa thickness under ultrasonic gastroscopy.
Article in Thoracic cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Integration of CT radiomics and machine learning for preoperative T staging of esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- RegGAN-based contrast-free CT enhances esophageal cancer assessment: multicenter validation of automated tumor segmentation and T-staging.La Radiologia medica · 2025Article
- Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.BMC medical informatics and decision making · 2024Article
- Collaborative multidisciplinary management and expertise of cT2-3 locally advanced operable esophageal squamous cell carcinoma: a report of two cases.Journal of thoracic disease · 2023Article
- The T stage of esophageal cancer can be effectively predicted by muscularis propria thickness and muscularis propria + mucosa thickness under ultrasonic gastroscopy.Thoracic cancer · 2023Article
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10 authors.
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Abstract
objectivesThe latest version of the National Comprehensive Cancer Network recommends neoadjuvant therapy followed by surgical treatment or radical chemoradiotherapy for patients with cT3N0M0. Neoadjuvant therapy can improve the prognosis of patients with locally advanced esophageal cancer. Therefore, the evaluation or prediction of T stage is particularly important because the treatment could differently affect the prognosis. Here, we establish a model to predict the T stage of patients with T2-3N0M0 to help choose the best treatment strategy.
methodsFrom 1637 patents with esophageal cancer, we enrolled 48 patients and performed least absolute shrinkage and selection operator regression to screen for independent factors influencing pathological T stage. We, then, trained the decision tree to obtain the decision tree diagram and divided the T stages obtained by different methods into two categories, T2 and T3, for survival analysis.
resultsA total of 21 and 27 cases were predicted to be T2 and T3, respectively, under ultrasonic gastroscopy, 19 and 29 under magnetic resonance imaging, and 22 and 26 under pathological examination. Multivariate logistic regression analysis revealed that the muscularis propria thickness (MPT) (p = 0.0097) and the muscularis propria + mucosa thickness (MPMT) in the largest tumor cross-section (p = 0.0239) were independent influencing factors. We plotted a decision tree diagram with these two factors. MPT in the largest tumor cross-section >1.3 mm could be judged as pT3; if ≤1.3 mm, MPMT should be considered a thickness ≥1.7 mm could be judged as pT2 (otherwise pT3). Corresponding survival analysis was performed according to the T stage under different examination modalities.
conclusionMPT in the largest tumor cross-section and MPMT in the largest tumor cross-section are independent predicting factors of pathological T stage.
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