Gynecology and Obstetrics, Oslo University Hospital, Oslo, Norway.
National Advisory Center for Late Effects after Cancer Treatment, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway.
Int J Gynecol Cancer. 2020 Nov;30(11):1775-1779. doi: 10.1136/ijgc-2020-001416. Epub 2020 Jul 21.
Algorithms have been developed to identify ovarian cancer in women with a pelvic mass. The aim of this study was to determine how the base rates of ovarian cancer influence the case finding abilities of recently developed algorithms applicable to pelvic tumors. We used three ovarian cancer algorithms and the principle of Bayes' theorem for risk estimation.
First, we evaluated the case finding abilities of the Risk of Malignancy Algorithm, the Rajavithi-Ovarian Predictive Score, and the Copenhagen Index in a prospectively collected sample at Oslo University Hospital of 227 postmenopausal women with a 74% base rate of ovarian cancer. Second, we examined the case finding abilities of the Risk of Malignancy Algorithm in three published studies with different base rates of ovarian cancer. We applied Bayes' theorem in these examinations.
In the Oslo sample, all three algorithms functioned poorly as case finders for ovarian cancer. When the base rate changed from 8.2% to 43.8% in the three studies using the Risk of Malignancy Algorithm, the proportion of false negative ovarian cancer diagnoses increased from 1.2% to 3.4%, and the number of false positive diagnosis increased from 4.6% to 14.2%.
This study demonstrated that the base rate of ovarian cancer in the samples tested was important for the case finding abilities of algorithms.
已经开发出算法来识别盆腔肿块女性中的卵巢癌。本研究的目的是确定卵巢癌的基础发生率如何影响适用于盆腔肿瘤的最近开发的算法的病例发现能力。我们使用了三种卵巢癌算法和贝叶斯定理的风险估计原理。
首先，我们在奥斯陆大学医院前瞻性收集的 227 例绝经后妇女（卵巢癌的基础发生率为 74%）中评估了恶性肿瘤风险算法、Rajavithi-卵巢预测评分和哥本哈根指数的病例发现能力。其次，我们在三个具有不同卵巢癌基础发生率的已发表研究中检查了恶性肿瘤风险算法的病例发现能力。我们在这些检查中应用了贝叶斯定理。
在奥斯陆样本中，所有三种算法在卵巢癌病例发现方面表现不佳。当使用恶性肿瘤风险算法的三项研究中的基础发生率从 8.2%变为 43.8%时，卵巢癌假阴性诊断的比例从 1.2%增加到 3.4%，假阳性诊断的数量从 4.6%增加到 14.2%。
本研究表明，所测试样本中卵巢癌的基础发生率对算法的病例发现能力很重要。
