Department of Development and Regeneration, KU Leuven, Herestraat 49 box 7003, 3000 Leuven, Belgium
Department of Electrical Engineering, KU Leuven, Leuven, Belgium iMinds Medical Information Technologies, KU Leuven, Leuven, Belgium.
BMJ. 2014 Oct 15;349:g5920. doi: 10.1136/bmj.g5920.
To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours.
Observational diagnostic study using prospectively collected clinical and ultrasound data.
24 ultrasound centres in 10 countries.
Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients.
Histological classification and surgical staging of the mass.
The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate.
The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.
开发一种术前风险预测模型,以区分良性、交界性、Ⅰ 期浸润性、Ⅱ-Ⅳ期浸润性和继发性转移性卵巢肿瘤。
使用前瞻性收集的临床和超声数据进行观察性诊断研究。
10 个国家的 24 个超声中心。
患有卵巢(包括附件和输卵管)肿块并在手术前接受标准超声检查的女性。该模型在 1999 年至 2007 年间招募的 3506 名患者中进行了开发,在 2009 年至 2012 年间招募的 2403 名患者中进行了时间验证,然后在所有 5909 名患者中进行了更新。
肿块的组织学分类和手术分期。
附件肿瘤的不同评估(ADNEX)模型包含三个临床和六个超声预测指标:年龄、血清 CA-125 水平、中心类型(肿瘤中心与其他医院)、病变最大直径、实性组织比例、超过 10 个囊腔、乳头状突起数量、声影和腹水。在时间验证中,经典区分良性和恶性肿瘤的受试者工作特征曲线下面积(AUC)为 0.94(0.93 至 0.95)。良性与交界性肿瘤的 AUC 为 0.85,良性与Ⅰ期癌症的 AUC 为 0.92,良性与Ⅱ-Ⅳ期癌症的 AUC 为 0.99,良性与继发性转移性肿瘤的 AUC 为 0.95。恶性肿瘤亚型之间的 AUC 范围为 0.71 至 0.95,交界性与Ⅰ期癌症的 AUC 为 0.75,Ⅱ-Ⅳ期与继发性转移性的 AUC 为 0.82。校准曲线表明估计风险准确。
ADNEX 模型很好地区分了良性和恶性肿瘤,并在四种卵巢恶性肿瘤之间提供了良好到极好的区分。使用 ADNEX 有可能改善分诊和管理决策,从而降低与附件病变相关的发病率和死亡率。