The value of dynamic enhanced CT combined with multi-slice spiral CT angiography in diagnosis giant tumors of unknown origin in the pelvic and abdominal regions
The value of dynamic enhanced CT combined with multi-slice spiral CT angiography in diagnosis giant tumors of unknown origin in the pelvic and abdominal regions
Objective To study the diagnostic value of dynamic enhanced CT combined with multi-slice spiral CT angiography(MSCTA)for giant tumors of unknown origin in the pelvic and abdominal regions.Methods The clinical data of 96 patients with pelvic and abdominal tumors admitted to Nanhai Hospital of Guangdong provincial People's Hospital from January 2018 to July 2023 were collected and conducted a retrospective comparative study.The clinical value of dynamic enhanced CT and MSCTA in diagnosing the nature and type of giant tumors of unknown origin in the pelvic and abdominal regions was analyzed by using pathological results as the gold standard.Results The surgical pathological results confirmed that there were 66 cases of malignant tumor lesions and 30 cases of benign tumor lesions.The results of dynamic enhanced CT combined with MSCTA in diagnosing the nature and type of unexplained huge tumors in the pelvic and abdominal regions were consistent with the surgical and pathological results(Kappa=0.832,0.911;P＜0.001).The sensitivity,specificity,and accuracy of dynamic enhanced CT and MSCTA in diagnosing the nature of lesions were 93.9％,90.0％,and 92.7％,respectively,the positive predictive value was 95.4％,and the negative predictive value was 87.10％.The surgical pathological results confirmed that there were 23 cases of gastric stromal tumors,17 cases of small intestinal stromal tumors,12 cases of uterine fibroids,22 cases of cystadenomas,14 cases of teratomas,and 8 cases of liver cancer.The accuracy of MSCTA in diagnosing lesion types was 92.7％.Conclusion The dynamic enhanced CT combined with MSCTA has high accuracy in diagnosing the nature and type of giant tumors of unknown origin in the pelvic and abdominal regions.
维普
万方数据