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前瞻性验证 IOTA 方法对附件包块的分类,以及使用良性描述符和 ADNEX 模型对两步策略的回顾性评估:葡萄牙多中心研究。

Prospective external validation of IOTA methods for classifying adnexal masses and retrospective assessment of two-step strategy using benign descriptors and ADNEX model: Portuguese multicenter study.

机构信息

Ginecologia e Obstetrícia, Hospital de São Francisco Xavier, Lisbon, Portugal.

Faculdade de Ciências da Saúde, Universidade da Beira Interior, Covilhã, Portugal.

出版信息

Ultrasound Obstet Gynecol. 2024 Oct;64(4):538-549. doi: 10.1002/uog.27641. Epub 2024 Sep 4.

Abstract

OBJECTIVES

To externally and prospectively validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SRs), Logistic Regression model 2 (LR2) and Assessment of Different NEoplasias in the adneXa (ADNEX) model in a Portuguese population, comparing these approaches with subjective assessment and the risk-of-malignancy index (RMI), as well as with each other. This study also aimed to retrospectively validate the IOTA two-step strategy, using modified benign simple descriptors (MBDs) followed by the ADNEX model in cases in which MBDs were not applicable.

METHODS

This was a prospective multicenter diagnostic accuracy study conducted between January 2016 and December 2021 of consecutive patients with an ultrasound diagnosis of at least one adnexal tumor, who underwent surgery at one of three tertiary referral centers in Lisbon, Portugal. All ultrasound assessments were performed by Level-II or -III sonologists with IOTA certification. Patient clinical data and serum CA 125 levels were collected from hospital databases. Each adnexal mass was classified as benign or malignant using subjective assessment, RMI, IOTA SRs, LR2 and the ADNEX model (with and without CA 125). The reference standard was histopathological diagnosis. In the second phase, all adnexal tumors were classified retrospectively using the two-step strategy (MBDs + ADNEX). Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios and overall accuracy were determined for all methods. Receiver-operating-characteristics curves were constructed and corresponding areas under the curve (AUC) were determined for RMI, LR2, the ADNEX model and the two-step strategy. The ADNEX model calibration plots were constructed using locally estimated scatterplot smoothing (LOESS).

RESULTS

Of the 571 patients included in the study, 428 had benign disease and 143 had malignant disease (prevalence of malignancy, 25.0%), of which 42 had borderline ovarian tumor, 93 had primary invasive adnexal cancer and eight had metastatic tumors in the adnexa. Subjective assessment had an overall sensitivity of 97.9% and a specificity of 83.6% for distinguishing between benign and malignant lesions. RMI showed high specificity (95.6%) but very low sensitivity (58.7%), with an AUC of 0.913. The IOTA SRs were applicable in 80.0% of patients, with a sensitivity of 94.8% and specificity of 98.6%. The IOTA LR2 had a sensitivity of 84.6%, specificity of 86.9% and an AUC of 0.939, at a malignancy risk cut-off of 10%. At the same cut-off, the sensitivity, specificity and AUC for the ADNEX model with vs without CA 125 were 95.8% vs 98.6%, 82.5% vs 79.7% and 0.962 vs 0.960, respectively. The ADNEX model gave heterogeneous results for distinguishing between benign masses and different subtypes of malignancy, with the highest AUC (0.991) for discriminating benign masses from primary invasive adnexal cancer Stages II-IV, and the lowest AUC (0.696) for discriminating primary invasive adnexal cancer Stage I from metastatic lesion in the adnexa. The calibration plot suggested underestimation of the risk by the ADNEX model compared with the observed proportion of malignancy. The MBDs were applicable in 26.3% (150/571) of cases, of which none was malignant. The two-step strategy using the ADNEX model in the second step only, with and without CA 125, had AUCs of 0.964 and 0.961, respectively, which was similar to applying the ADNEX model in all patients.

CONCLUSIONS

The IOTA methods showed good-to-excellent performance in the Portuguese population, outperforming RMI. The ADNEX model was superior to other methods in terms of accuracy, but interpretation of its ability to distinguish between malignant subtypes was limited by sample size and large differences in the prevalence of tumor subtypes. The IOTA MBDs are reliable in identifying benign disease. The two-step strategy comprising application of MBDs followed by the ADNEX model if MBDs are not applicable, is suitable for daily clinical practice, circumventing the need to calculate the risk of malignancy in all patients. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

摘要

目的

在葡萄牙人群中,对国际卵巢肿瘤分析(IOTA)简单规则(SRs)、逻辑回归模型 2(LR2)和附件中不同肿瘤的评估(ADNEX)模型进行外部前瞻性验证,并与主观评估和风险恶性指数(RMI)进行比较,以及彼此之间进行比较。本研究还旨在使用改良的良性简单描述符(MBDs),然后在 MBDs 不适用的情况下使用 ADNEX 模型,对两步策略进行回顾性验证。

方法

这是一项前瞻性多中心诊断准确性研究,于 2016 年 1 月至 2021 年 12 月在里斯本的三家三级转诊中心进行,连续纳入至少有一个附件肿瘤超声诊断的患者。所有超声评估均由具有 IOTA 认证的二级或三级超声医师进行。从医院数据库中收集患者的临床数据和血清 CA125 水平。使用主观评估、RMI、IOTA SRs、LR2 和 ADNEX 模型(有和没有 CA125)对每个附件肿块进行分类为良性或恶性。参考标准为组织病理学诊断。在第二阶段,使用两步策略(MBDs+ADNEX)对所有附件肿瘤进行回顾性分类。确定了所有方法的敏感性、特异性、阳性和阴性预测值、阳性和阴性似然比以及总准确性。构建了接受者操作特征曲线,并为 RMI、LR2、ADNEX 模型和两步策略确定了相应的曲线下面积(AUC)。使用局部估计散点平滑(LOESS)构建了 ADNEX 模型校准图。

结果

在纳入的 571 例患者中,428 例为良性疾病,143 例为恶性疾病(恶性肿瘤患病率为 25.0%),其中 42 例为交界性卵巢肿瘤,93 例为原发性侵袭性附件癌,8 例为转移性肿瘤附件。主观评估在区分良性和恶性病变方面具有 97.9%的总体敏感性和 83.6%的特异性。RMI 显示出高特异性(95.6%)但非常低的敏感性(58.7%),AUC 为 0.913。IOTA SRs 适用于 80.0%的患者,敏感性为 94.8%,特异性为 98.6%。IOTA LR2 的敏感性为 84.6%,特异性为 86.9%,AUC 为 0.939,恶性风险截断值为 10%。在相同的截断值下,ADNEX 模型(有和没有 CA125)的敏感性、特异性和 AUC 分别为 95.8%vs98.6%、82.5%vs79.7%和 0.962 vs 0.960。ADNEX 模型在区分良性肿块和不同类型的恶性肿瘤方面给出了不均匀的结果,对于区分良性肿块和原发性侵袭性附件癌 II-IV 期,AUC 最高(0.991),而对于区分原发性侵袭性附件癌 I 期和转移性肿瘤在附件,AUC 最低(0.696)。校准图表明 ADNEX 模型相对于观察到的恶性比例低估了风险。MBDs 适用于 26.3%(150/571)的病例,其中无一例为恶性。仅在第二步中使用 ADNEX 模型的两步策略(有和没有 CA125),AUC 分别为 0.964 和 0.961,与在所有患者中使用 ADNEX 模型相似。

结论

IOTA 方法在葡萄牙人群中表现出良好至优秀的性能,优于 RMI。ADNEX 模型在准确性方面优于其他方法,但由于样本量小且肿瘤亚型的患病率差异较大,其区分恶性亚型的能力受到限制。IOTA MBDs 可可靠地识别良性疾病。两步策略包括应用 MBDs,如果 MBDs 不适用,则应用 ADNEX 模型,如果 MBDs 不适用,则适用于日常临床实践,避免了在所有患者中计算恶性风险的需要。

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