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Scand J Gastroenterol ; 59(7): 859-867, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38578144

RESUMO

OBJECTIVE: To study and compare the value of the Kyoto classification risk scoring system and the modified Kyoto classification risk scoring system based on linked color imaging (LCI) in predicting the risk of early gastric cancer. METHODS: One hundred and fifty patients with pathologically confirmed non-cardia early gastric cancer by endoscopic LCI and 150 non-gastric cancer patients matched for age and gender were included. Basic patient data and whole gastric endoscopic images under LCI were collected, and the images were scored according to the LCI-based Kyoto classification risk scoring system and the LCI-based modified Kyoto classification risk scoring system. RESULTS: Compared with the LCI-based Kyoto classification risk scoring system, the LCI-based modified Kyoto classification risk scoring system had a higher AUC for predicting the risk of early gastric cancer (0.723 vs. 0.784, p = 0.023), with a score of ≥3 being the best cutoff value for predicting the risk of early gastric cancer (sensitivity 61.33%, specificity 86.00%), and scores of 3 to 5 were significantly associated with early gastric carcinogenesis significantly (OR = 9.032, 95% CI: 4.995-16.330, p < 0.001). CONCLUSIONS: Compared with the LCI-based Kyoto classification risk scoring system, the LCI-based Kyoto modified classification risk scoring system has a better value for predicting the risk of early gastric cancer, and the score of 3 to 5 is a high-risk factor for the risk of early gastric cancer development, which is more strongly correlated with the risk of early gastric cancer.


Assuntos
Detecção Precoce de Câncer , Neoplasias Gástricas , Humanos , Neoplasias Gástricas/patologia , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Medição de Risco/métodos , Detecção Precoce de Câncer/métodos , Gastroscopia , Fatores de Risco , Adulto , Sensibilidade e Especificidade , Curva ROC , Modelos Logísticos , Estudos Retrospectivos , Área Sob a Curva
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