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使用列线图预测非恶性内窥镜组织诊断患者胆道狭窄的恶性程度:一项回顾性研究
Authors Zhang Y , Jiang Q, Wang Q, Feng Y, Wu D, Guo T, Zhang S, Chen X , Yang Y, Shi W, Wu X, Yang A
Received 12 August 2021
Accepted for publication 20 September 2021
Published 11 October 2021 Volume 2021:13 Pages 7735—7745
DOI https://doi.org/10.2147/CMAR.S333333
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Chien-Feng Li
Purpose: The accurate differentiation between benign and malignant biliary stricture is significant but challenging. Tissue diagnosis of biliary stricture by endoscopy sampling can provide excellent specificity but insufficient sensitivity. For patients with suspected malignant biliary stricture (MBS) but non-malignant was reported in endoscopy tissue samples, we constructed a nomogram to predict malignancy and improve the overall diagnostic performance.
Patients and Methods: 232 patients with suspected MBS and underwent endoscopy tissue sampling from January 2017 to December 2019 were included, among which 123 patients’ endoscopy tissue samples were classified as non-malignant (including atypical, negative for malignancy, and nondiagnostic). Demographics, serum markers, radiological and sampling results of these 123 patients were collected to construct a nomogram using multivariate analysis.
Results: The nomogram was developed based on bilirubin, CA19-9, radiological result, and atypical sampling results and provided an AUC of 0.863 (95% CI 0.795– 0.930) for predicting MBS. The specificity, sensitivity, and accuracy of endoscopy tissue diagnosis were 100.00%, 59.90%, and 68.53%, respectively. With the nomogram added, the overall diagnosis specificity, sensitivity, and accuracy were 95.24%, 89.20%, and 90.23%, respectively.
Conclusion: The nomogram can predict malignancy in patients whose endoscopy tissue diagnoses were non-malignant. The overall diagnostic performance was improved with the nomogram added.
Keywords: malignant biliary stricture, endoscopic tissue sampling, bilirubin, CA19-9, radiology