Table_2_Ovarian Cancer surgical consideration is markedly improved by the neural network powered-MIA3G multivariate index assay.DOCX

BackgroundSurgery remains the main treatment option for an adnexal mass suspicious of ovarian cancer. The malignancy rate is, however, only 10–15% in women undergoing surgery. This results in a high number of unnecessary surgeries. A surveillance-based approach is recommended to form the basis for surgical referrals. We have previously reported the clinical performance of MIA3G, a deep neural network-based algorithm, for assessing ovarian cancer risk. In this study, we show that MIA3G markedly improves the surgical selection for women presenting with adnexal masses.
MethodsMIA3G employs seven serum biomarkers, patient age, and menopausal status. Serum samples were collected from 785 women (IQR: 39–55 years) across 12 centers that presented with adnexal masses. MIA3G risk scores were calculated for all subjects in this cohort. Physicians had no access to the MIA3G risk score when deciding upon a surgical referral. The performance of MIA3G for surgery referral was compared to clinical and surgical outcomes. MIA3G was also tested in an independent cohort comprising 29 women across 14 study sites, in which the physicians had access to and utilized MIA3G prior to surgical consideration.
ResultsWhen compared to the actual number of surgeries (n = 207), referrals based on the MIA3G score would have reduced surgeries by 62% (n = 79). The reduction was higher in premenopausal patients (77%) and in patients ≤55 years old (70%). In addition, a 431% improvement in malignancy prediction would have been observed if physicians had utilized MIA3G scores for surgery selection. The accuracy of MIA3G referral was 90.00% (CI 87.89–92.11), while only 9.18% accuracy was observed when the MIA3G score was not used. These results were corroborated in an independent multi-site study of 29 patients in which the physicians utilized MIA3G in surgical consideration. The surgery reduction was 87% in this cohort. Moreover, the accuracy and concordance of MIA3G in this independent cohort were each 96.55%.
ConclusionThese findings demonstrate that MIA3G markedly augments the physician’s decisions for surgical intervention and improves malignancy prediction in women presenting with adnexal masses. MIA3G utilization as a clinical diagnostic tool might help reduce unnecessary surgeries.

【背景】对于疑似卵巢癌（ovarian cancer）的附件肿块（adnexal mass）患者，手术仍是主要治疗手段。但接受手术的女性中，恶性病变率仅为10%~15%，由此造成大量不必要的手术操作。目前推荐以监测为基础的方案作为手术转诊的依据。本团队此前已报道了基于深度神经网络（deep neural network）的算法MIA3G在评估卵巢癌风险方面的临床性能。本研究证实，MIA3G可显著优化附件肿块患者的手术遴选流程。
【方法】MIA3G整合了7项血清生物标志物（serum biomarkers）、患者年龄与绝经状态（menopausal status）。本研究从12个中心招募了785名存在附件肿块的女性，其年龄四分位数间距（IQR）为39~55岁，采集所有受试者的血清样本，为该队列（cohort）中的所有对象计算MIA3G风险评分。在进行手术转诊决策时，临床医师无法获知MIA3G风险评分，由此将MIA3G用于手术转诊的性能与临床及手术结局进行对比。此外，本研究还在一项独立队列中验证了MIA3G：该队列纳入14个研究中心的29名女性，临床医师在考虑手术前可获取并使用MIA3G评分。
【结果】与实际手术量（n=207）相比，基于MIA3G评分的转诊方案可使手术量减少62%（n=79）。其中绝经前患者的手术量减少幅度更高（77%），≤55岁患者的手术量减少幅度也达70%。此外，若临床医师采用MIA3G评分进行手术遴选，恶性病变预测性能可提升431%。MIA3G转诊的准确率为90.00%（置信区间CI：87.89~92.11），而未使用MIA3G评分时的准确率仅为9.18%。上述结果在另一项独立多中心队列研究中得到验证：该队列纳入29名患者，临床医师在手术考量中使用了MIA3G，此队列的手术量减少幅度达87%。此外，该独立队列中MIA3G的准确率与一致性（concordance）均为96.55%。
【结论】本研究结果证实，MIA3G可显著优化附件肿块患者的手术干预决策，并提升恶性病变预测效能。将MIA3G作为临床诊断工具应用，或可减少不必要的手术操作。