Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou City, Jiangsu Province, 215002, China.
Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou City, Jiangsu Province, 215002, China.
Eur Radiol. 2024 Nov;34(11):7115-7124. doi: 10.1007/s00330-024-10785-6. Epub 2024 May 10.
To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs.
Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).
Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA.
Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs.
It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations.
Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.
评估正常胎儿（NFs）和脑室扩大胎儿（FVs）侧脑室枕角周围的颅内结构和脑实质放射组学特征，并预测 FVs 出生后侧脑室扩大的改变。
2014 年 1 月至 2023 年 8 月，141 例 NFs 和 101 例 FVs 行 1.5T 平衡稳态自由进动（BSSFP）检查，其中 68 例 FVs 侧脑室扩大已缓解（FVM-resolved），33 例 FVs 侧脑室稳定（FVM-stable）。分析其人口统计学数据和颅内结构。为了预测 FVs 出生后侧脑室扩大的改变，基于侧脑室形态、皮质混合或/和皮质下放射组学特征，建立了 5 折交叉验证的逻辑回归模型。使用受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）对模型的性能进行验证。
NFs 和 FVs 之间观察到脑结构有显著差异（p<0.05），但最大额角直径除外。然而，FVM-resolved 组和 FVM-stable 组之间没有明显差异（均 p>0.05）。基于 FVs 异常侧的皮质下放射组学，该方法在训练/验证集中能很好地区分 NFs 和 FVs，AUC 值高达 1/0.992。在训练/验证集中，皮质下放射组学模型的 AUC 值分别为 0.822/0.743，能够预测 FVs 侧脑室的改变，DCA 表明其具有最大的预测优势。
侧脑室扩大伴发的皮质下实质的微观结构改变可作为预测 FVs 出生后侧脑室变化的指标。
从单一胎儿 MRI 中获取相关信息对于预测脑室扩大胎儿出生后的侧脑室变化至关重要。这种方法有可能减少对反复产前超声或 MRI 检查的需求。
胎儿脑室扩大是一种影响出生后神经发育的动态情况。机器学习和皮质下放射组学可预测侧脑室的出生后变化。基于单胎 MRI 的机器学习可能会减少所需的产前检查。
