Wang Zhiwei, Jiao Xinyao, Liu Weiwu, Song Han, Li Jiapeng, Hu Jing, Huang Yuanbo, Liu Yang, Huang Sa
Department of Radiology, The Second Hospital of Jilin University, Changchun 130041, PR China (Z.W., X.J., W.L., H.S., J.L., Y.H., S.H.).
Changchun University of Science and Technology, Changchun 130022, Jilin, China (J.H.).
Acad Radiol. 2025 Apr;32(4):2041-2052. doi: 10.1016/j.acra.2024.10.021. Epub 2024 Nov 24.
The escalating incidence of placental accreta spectrum (PAS), a pregnancy complication, underscores the need for accurate prenatal diagnosis to guide optimal management strategies. This study aims to develop, validate, and compare various prenatal PAS prediction models integrating clinical data, MRI signs, and radiomics signatures.
A cohort comprising 111 patients (72 with PAS and 39 without, denoted as N-PAS) served as the training set, while another 47 patients (33 PAS and 14 N-PAS) constituted the validation set. Clinical features and MRI signs were subjected to univariate and multivariate analyses to construct the Clinical-MRI model. Radiomic features were extracted from MRI images and refined through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, thereby establishing the Radiomics model. An optimal set of radiomic features was utilized to derive the Radscore, which was then integrated with clinical features and MRI signs to formulate the Nomogram model. The performance of these models was comprehensively evaluated and compared.
In the validation set evaluation, the Nomogram model, which integrated Radscore, a pivotal clinical indicator, and two MRI signs, demonstrated superior performance. With an area under the curve (AUC) of 0.861 (95% CI: 0.745, 0.978), this model significantly outperformed both the clinical-MRI model (AUC = 0.796, 95% CI: 0.649, 0.943) and the radiomics model (AUC = 0.704, 95% CI: 0.531, 0.877). Specifically, the Nomogram model achieved a high sensitivity of 81.8% and a specificity of 78.6% in the prenatal diagnosis of placenta accreta spectrum (PAS), thereby offering clinicians a precise and efficient diagnostic aid.
The radiomics-derived Radscore serves as an independent predictor for prenatal PAS. Combining Radscore with clinical features and MRI signs into a Nomogram model provides a non-invasive tool with high sensitivity or specificity for PAS diagnosis, enhancing prenatal assessment and management.
胎盘植入谱系疾病（PAS）作为一种妊娠并发症，其发病率不断攀升，凸显了准确产前诊断以指导最佳管理策略的必要性。本研究旨在开发、验证并比较整合临床数据、MRI征象和放射组学特征的各种产前PAS预测模型。
一个由111例患者组成的队列（72例患有PAS，39例未患，记为N - PAS）作为训练集，另一个由47例患者（33例PAS和14例N - PAS）构成验证集。对临床特征和MRI征象进行单因素和多因素分析以构建临床 - MRI模型。从MRI图像中提取放射组学特征，并通过最小绝对收缩和选择算子（LASSO）算法进行优化，从而建立放射组学模型。利用一组最佳的放射组学特征得出Radscore，然后将其与临床特征和MRI征象相结合以制定列线图模型。对这些模型的性能进行全面评估和比较。
在验证集评估中，整合了Radscore、一个关键临床指标和两个MRI征象的列线图模型表现出色。该模型的曲线下面积（AUC）为0.861（95%CI：0.745，0.978），显著优于临床 - MRI模型（AUC = 0.796，95%CI：0.649，0.943）和放射组学模型（AUC = 0.704，95%CI：0.531，0.877）。具体而言，列线图模型在胎盘植入谱系疾病（PAS）的产前诊断中实现了81.8%的高灵敏度和78.6%的特异性，从而为临床医生提供了一种精确且高效的诊断辅助工具。
基于放射组学的Radscore可作为产前PAS的独立预测指标。将Radscore与临床特征和MRI征象结合到列线图模型中，为PAS诊断提供了一种具有高灵敏度或特异性的非侵入性工具，增强了产前评估和管理。
