*Gynaecological Diagnostic Outpatient Treatment Unit, University College Hospital; †Department of Statistical Science, University College London; and ‡Department of Women's Cancer, University College London, Elizabeth Garrett Anderson, Institute for Women's Health, London, United Kingdom.
Int J Gynecol Cancer. 2013 Nov;23(9):1583-9. doi: 10.1097/IGC.0b013e3182a6171a.
This study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.
This was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.
The AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92-0.97] and 0.93 (95% CI, 0.90-0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84-24.6) and a LR-ve of 0.049 (95% CI, 0.022-0.107). The corresponding LR+ve and LR-ve for LR1 were 3.33 (95% CI, 2.85-3.55) and 0.03 (95% CI, 0.01-0.10), and for LR2 were 3.58 (95% CI, 2.77-4.63) and 0.052 (95% CI, 0.022-0.123). The accuracy of PR was 0.942 (95% CI, 0.908-0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781-0.870) for LR1 and 0.836 (95% CI, 0.788-0.872) for LR2 (P < 0.001).
The AUC of the IOTA LR1 and LR2 were similar in nonexpert's hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.
本研究旨在评估国际卵巢肿瘤分析（IOTA）逻辑回归模型（LR1 和 LR2）以及主观模式识别（PR）在卵巢癌诊断中的准确性。
这是一项在 33 个月内于一家三级医院普通妇科病房进行的前瞻性单中心研究。共有 292 名连续接受超声诊断附件肿瘤后行手术的女性入组。所有检查均由一名 2 级超声操作员按照 IOTA 指南进行。使用 IOTA LR1 和 LR2 计算恶性肿瘤可能性。然后，由一名专家操作员单独使用主观 PR 对这些女性进行检查。这些结果与手术发现和组织学进行比较。计算并比较了这 3 种方法的灵敏度、特异性、曲线下面积（AUC）和准确性。
LR1 和 LR2 的 AUC 分别为 0.94（95%CI，0.92-0.97）和 0.93（95%CI，0.90-0.96）。主观 PR 的阳性似然比（LR+ve）为 13.9（95%CI，7.84-24.6），LR-ve 为 0.049（95%CI，0.022-0.107）。LR1 的 LR+ve 和 LR-ve 分别为 3.33（95%CI，2.85-3.55）和 0.03（95%CI，0.01-0.10），LR2 的 LR+ve 和 LR-ve 分别为 3.58（95%CI，2.77-4.63）和 0.052（95%CI，0.022-0.123）。PR 的准确性为 0.942（95%CI，0.908-0.966），与专家的 0.829（95%CI，0.781-0.870）相比，LR1 的 0.836（95%CI，0.788-0.872）相比，差异有统计学意义（P<0.001）。
在非专家手中，IOTA LR1 和 LR2 的 AUC 与原始和验证的 IOTA 研究相似。PR 方法比任何 IOTA 模型更准确地诊断卵巢癌。
