背景:局灶性皮质发育不良(FCD)是最常见的癫痫性发育畸形。FCD的诊断具有挑战性。我们基于多参数磁共振成像(MRI)生成了放射组学列线图,以诊断FCD并早期识别侧向性。
方法:回顾性纳入了在2017年7月至2022年5月期间接受治疗的43例经组织病理学证实的FCD患者。将未受影响的对侧半球作为对照组。因此,86个ROI最终被包括在内。以2021年1月为截止时间,2021年1月后被录取的人被列入延期名单(n=20)。其余患者随机(8:2比率)分成训练(n=55)和验证(n=11)组。所有术前和术后MR图像,包括T1加权(T1w),T2加权(T2w),流体衰减反转恢复(FLAIR),和组合(T1w+T2w+FLAIR)图像,包括在内。使用最小绝对收缩和选择运算符(LASSO)来选择特征。采用多因素logistic回归分析建立诊断模型。用曲线下面积(AUC)评估放射学列线图的性能,净重新分类改进(NRI),综合歧视改进(IDI),校准和临床效用。
结果:从组合序列(T1w+T2w+FLAIR)中选择的基于模型的放射组学特征在所有模型中具有最高的性能,并且在训练中显示出比没有经验的放射科医师更好的诊断性能(AUC:0.847VS。0.664,p=0.008),验证(AUC:0.857VS。0.521,p=0.155),和坚持集(AUC:0.828VS。0.571,p=0.080)。三组中NRI(0.402,0.607,0.424)和IDI(0.158,0.264,0.264)的正值表明Model-Combined的诊断性能显着提高。放射组学列线图与校准曲线拟合良好(p>0.05),和决策曲线分析进一步证实了列线图的临床有用性。此外,FCD病变的对比(影像组学特征)不仅在分类器中起着至关重要的作用,而且与FCD的持续时间有显著的相关性(r=-0.319,p<0.05)。
结论:基于逻辑回归模型的多参数MRI生成的影像组学列线图代表了FCD诊断和治疗的重要进展。
BACKGROUND: Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.
METHODS: Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.
RESULTS: The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.
CONCLUSIONS: The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.