Objective:To investigate the distinctive imaging features of epithelial ovarian cancer(EOC)with varying tumor-stroma ratio(TSR)and to develop a nomogram prediction model based on preoperative clinical and imaging characteristics.Methods:Retrospectively collected data from 96 patients diagnosed with EOC through surgery and pathology confirmation,spanning from July 2013 to June 2016 at our hospital.Patients were categorized into high TSR group(TSR≥50%)and low TSR group(TSR＜50%)based on postoperative histopathology.The morphological features of preoperative contrast-enhanced CT of the two groups were retrospectively analyzed,including the location of the primary lesion,degree and morphology of cystic changes,border characteristics,longest and shortest diameters,length-to-shortness ratio,volume,CT values on plain scans,enhancement degree,presence of calcification,ascites,retroperitoneal lymph node enlargement,and peritoneal metastasis.Uni-variate and multivariate Logistic regression analyses were performed to identify independent factors influencing the evaluation of the TSR of EOC.Subsequently,a nomogram prediction model was developed based on these factors.The diagnostic perfor-mance of the nomogram model was evaluated by ROC curve and area under the curve(AUC),and the clinical applicability of the nomogram model was evaluated by decision curve analysis.Results:Preoperative neoadjuvant chemotherapy,length-to-shortness ratio of the tumor,enhancement degree,boundary,morphology of cystic changes,and enlarged lymph nodes were in-dependent influencing factors for TSR of EOC.Based on these results,a nomogram was constructed with an AUC value of 0.966(95%CI 0.936～0.997),sensitivity of 96.1%,and specificity of 88.9%.Conclusion:The high TSR group of EOC has a higher length-to-shortness ratio,higher enhancement degree,obscure boundary,microcystic form and enlarged retroperitoneal lymph nodes.The nomogram,built on the preoperative CT features of EOC patients,offers a non-invasive method for evaluat-ing the TSR and aiding in clinical decision-making for personal treatment.
关键词
卵巢肿瘤/肿瘤,腺和上皮/体层摄影术,X线计算机
Key words
Ovarian Neoplasms/Neoplasms,Glandular and Epithelial/Tomography,X-Ray Computed
万方数据