卵巢肿瘤恶性肿瘤的术前预测及临床应用

E. Joyeux , T. Miras , I. Masquin , P.-E. Duglet , K. Astruc , S. Douvier
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引用次数: 10

摘要

目的本研究的主要目的是恶性卵巢肿瘤的可预测性,并确定该评分的临界值,以表明恶性肿瘤的风险,这将易于在临床实践中使用。方法回顾性计算所有在勃艮第两家医院(第戎大学医院和Chalon-sur-Saône医院)接受卵巢肿瘤手术的患者的ADNEX评分。我们使用了ADNEX模型的九个标准。纳入标准是所有ADNEX标准的存在和组织学结果。我们按年龄组分析了整个人群的敏感性、特异性、PPV和PNV的四个临界值(3%、5%、10%和15%);结果2013年1月1日至2015年12月31日共纳入卵巢肿瘤患者284例。鉴别卵巢良恶性肿瘤的AUC为0.94 (95% CI[0.903-0.977])。截止值为10%时,敏感性为90%,特异性为81.1%,PPV为34.6%,PNV为98.5%。年轻女性的结果低于第二组。对于10%的临界值,1组的敏感性为77.7%,特异性为89.6%,PPV为46.6%,PNV为97.5%。2组敏感性95.2%,特异性76.6%,PPV为33.8%,PNV为99.2%。整个池子最合理的临界值是10%。对于第1组,由于在年轻患者中更常见的“边缘”肿瘤的检测不太令人满意,因此保留了5%的截止值。对于第二组,10%的分界点给出了最好的结果。结论:在我们的研究中,年轻女性较低的临界值似乎更适合于鉴别交界性肿瘤。在实践中,ADNEX评分与术中腹腔镜检查相结合似乎是使用ADNEX模型的最佳方法。我们的研究表明,ADNEX模型可以很好地预测恶性卵巢肿瘤。对于最年轻的患者来说,这种可预测性就不那么令人满意了。两个年龄组达到了允许在专门机构对患者进行手术治疗的恶性肿瘤临界值:42岁以下妇女的临界值为5%,43岁以上妇女的临界值为10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prédictibilité préopératoire de la malignité des tumeurs ovariennes à partir du score ADNEX et utilisation en pratique clinique

Objective

The principal aim of this study was the predictability of malignant ovarian tumors and to determine a cut-off value for this score to indicate the risk of malignancy that would be easy to use in clinical practice.

Methods

We retrospectively calculated the ADNEX score for all patients who underwent surgery for ovarian tumours in two Burgundy hospitals (Dijon University Hospital and Chalon-sur-Saône Hospital). We used the nine criteria of the ADNEX model. The inclusion criteria were the presence of all of the ADNEX criteria and a histology result. We analysed the sensitivity, specificity, PPV and PNV of four cut-offs (3%, 5%, 10% and 15%) for the entire pool then by age groups; from 14 to 42 (group 1) and 43 and more (group 2)

Results

Two hundred and eighty-four patients managed for an ovarian tumour were included between the 1st January 2013 and the 31st December 2015. Our AUC was of 0.94 (95% CI [0.903–0.977]) for discrimination between benign and malignant ovarian tumors. For a cut-off of 10%, sensitivity was 90%, specificity was 81.1%, PPV was 34.6% and PNV 98.5%. Results were lower for young women than for the second group. For a cut-off of 10%, group 1 had a sensitivity of 77.7% and specificity of 89.6%, PPV of 46.6% and PNV 97.5%. For the group 2, sensitivity was 95.2%, specificity was 76.6%, PPV was 33.8% and PNV was 99.2%. The most reasonable cut-off for the whole pool was 10%. For group 1 a cut-off of 5% was retained due to the less satisfying detection of “borderline” tumours more frequent in younger patients. For group 2 the cut-off of 10% gave the best results.

Conclusion

In our study, a lower cut-off for younger women seemed better suited to discriminate borderline tumours. In practice, the ADNEX score associated with the peroperative laparoscopic examination seems to be the best way to use the ADNEX model. Our study showed that the ADNEX model allows a good predictability of malignant ovarian tumours. The predictability becomes less satisfying for the youngest patients. A cut-off malignity value allowing surgical treatment of patients in a specialised facility was reached for two age groups: a cut-off of 5% for women under 42 years old and a cut-off of 10% for women over 43 years old.

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