ArticleSisli Etfal Hastanesi tip bulteni2023
The Significance of Preoperative Computed Tomography Features in the Prediction of Overall Survival in Gastric Cancer: A Retrospective Analysis.
Article in Sisli Etfal Hastanesi tip bulteni, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors at 1 institution in 1 country.
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Abstract
Objectives: Computed tomography (CT) is a frequently used modality for staging in the preoperative evaluation of gastric cancer (GC). Our aim was to interpret the importance of preoperative CT features in predicting overall survival (OS) in patients operated for GC. Methods: One hundred and one patients with GC (33 women, 68 men; range of age: 29-82 years, median age: 61 years) who had abdominal CT prior to surgical resection were included in the study retrospectively. Two radiologists evaluated CT scans to record the longest dimension of the tumor, the localization of the lesion, the attenuation values of the tumor in the arterial and venous phases (Hounsfield units), invasion depth of the lesion (T stage), and the number of pathological lymph nodes (LNs) (N stage). Postoperative pathological results including resection (R0, R1), T stage, N stage, grade, and histopathological subtype were documented. All CT-provided results and clinicopathological features associated with OS were analyzed by univariate, multivariate, and receiver operator characteristic analysis. Results: Multivariate analysis revealed that none of the CT features were associated with the OS. After resection, the survival ratio was poor for the R1 and high-grade groups than for the R0 and low-grade groups (p=0.001 and p=0.005, respectively). N stage and the longest dimension of the tumor on CT imaging truly estimated R1 resection status (AUC, 0.697; sensitivity, 63%; and specificity, 88%, and AUC, 0.734; sensitivity, 18%; and specificity, 76%, respectively). Conclusion: R1 resection status is associated with poor OS in GC. CT features, including the tumor's longest dimension and the number of pathological LNs, can predict R1 resection status.
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