Department of UCD Obstetrics and Gynaecology, School of Medicine, University College Dublin, National Maternity Hospital, Dublin 2, Ireland.
School of Mathematical Sciences, University College Cork, Cork T12 XF62, Ireland.
Br J Radiol. 2024 Nov 1;97(1163):1833-1842. doi: 10.1093/bjr/tqae164.
We previously demonstrated the potential of radiomics for the prediction of severe histological placenta accreta spectrum (PAS) subtypes using T2-weighted MRI. We aim to validate our model using an additional dataset. Secondly, we explore whether the performance is improved using a new approach to develop a new multivariate radiomics model.
Multi-centre retrospective analysis was conducted between 2018 and 2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomized feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, and specificity.
We present 100 women [mean age 34.6 (±3.9) with PAS], 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found that a support vector machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922), and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset was selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990), and specificity of 0.447 (95% CI 0.167; 0.667).
We validated our previous model and present a new multivariate radiomic model for the prediction of severe placenta increta from a well-defined, cohort of PAS cases.
Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.
我们之前已经证明了放射组学在使用 T2 加权 MRI 预测严重的组织学胎盘植入谱（PAS）亚型方面的潜力。我们旨在使用额外的数据集验证我们的模型。其次，我们探索是否通过采用新方法开发新的多变量放射组学模型来提高性能。
进行了 2018 年至 2023 年期间的多中心回顾性分析。纳入标准：MRI 怀疑 PAS 来自超声，剖腹术时 PAS 的临床发现和/或组织病理学证实。从 T2 加权 MRI 中提取放射组学特征。验证了以前的多变量模型。其次，从随机特征选择方案中选择了 5 个放射组学特征随机森林分类器来预测侵袭性胎盘植入 PAS 病例。基于多个指标评估预测性能，包括接收器操作特征曲线（ROC）的曲线下面积（AUC）、灵敏度和特异性。
我们呈现了 100 名女性[平均年龄 34.6（±3.9）岁，伴有 PAS]，其中 64 名患有胎盘植入症。首先，我们验证了以前的多变量模型，发现支持向量机分类器的灵敏度为 0.620（95%CI：0.068；1.0），特异性为 0.619（95%CI：0.059；1.0），AUC 为 0.671（95%CI：0.440；0.922），准确率为 0.602（95%CI：0.353；0.817），用于预测胎盘植入症。从新的多变量模型中，通过随机子集特征选择方案选择了最佳的 5 个特征子集，该方案由 4 个放射组学特征和 1 个临床变量（剖宫产次数）组成。该临床放射组学模型的 AUC 为 0.713（95%CI：0.551；0.854），准确率为 0.695（95%CI 0.563；0.793），灵敏度为 0.843（95%CI：0.682；0.990），特异性为 0.447（95%CI：0.167；0.667）。
我们验证了以前的模型，并为 PAS 病例的严重胎盘植入症提出了一种新的多变量放射组学模型。
放射组学特征对识别胎盘植入具有良好的预测潜力。这表明放射组学可能是一种有用的辅助工具，可用于治疗患有这种高危妊娠的妇女。
