Back

Diagnostic Accuracy of Preoperative CT Staging for Esophageal Cancer for Surgical Patients at Public Hospitals, Addis Ababa, Ethiopia

Tadesse, H. D.; Mesfin, A. A.; Negussie, M. A.; Demessa, M. D.; Teferi, M. G.; Tesfaye, S. Z.

2025-08-03 radiology and imaging
10.1101/2025.08.01.25332671 medRxiv
Show abstract

BackgroundEsophageal cancer is a major health concern in Ethiopia and is often diagnosed at advanced stages because of limited access to diagnostic tools. Computed tomography is the primary imaging modality used for preoperative staging at Tikur Anbessa Specialized Hospital and its affiliated centers. However, its diagnostic accuracy has not been well studied locally. ObjectiveTo evaluate the diagnostic accuracy of preoperative CT TNM staging of esophageal cancer in surgical patients, postoperative histopathological findings were used as the gold standard. MethodThis retrospective cross-sectional study included 121 patients with histologically confirmed esophageal cancer who underwent preoperative CT staging and surgery between January 2021 and December 2024 at TASH and affiliated hospitals. Data were collected from medical records and CT images and analyzed via SPSS version 27. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy were calculated. ResultsAmong the patients, 57.02% were female and 42.98% male, with a median age of 54 years. SCC was the predominant histologic type (84.3%). Preoperative CT staging revealed T3 in 67.8% and T4 in 32.2% of patients. Nodal staging revealed N0 in 77.7% of the patients. The diagnostic accuracies of CT for the T3 and T4 stages were 49.6% and 67.8%, respectively. For the N0 to N3 stages, the accuracy ranged from 61.9% to 95%. The combined sensitivity and specificity for T staging were 82.6% and 20%, respectively; for N staging, they were 88.1% and 25.4%, respectively. ConclusionCT imaging has moderate accuracy in staging esophageal cancer but has limitations, particularly in differentiating tumor depth and nodal involvement. These findings underscore the need for multimodal imaging approaches, including MRI, PET, and EUS, where available, to improve preoperative assessment and patient outcomes.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.