A radiomics nomogram based on multiparametric MRI for diagnosing focal cortical dysplasia and initially identifying laterality.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-08-15 DOI:10.1186/s12880-024-01374-6
Shi-Qi Chen, Liang Wei, Keng He, Ya-Wen Xiao, Zhao-Tao Zhang, Jian-Kun Dai, Ting Shu, Xiao-Yu Sun, Di Wu, Yi Luo, Yi-Fei Gui, Xin-Lan Xiao
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Abstract

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.

Conclusion: The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

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基于多参数磁共振成像的放射组学提名图,用于诊断局灶性皮质发育不良并初步确定侧位。
背景:局灶性皮质发育不良(FCD)是最常见的致痫性发育畸形。FCD 的诊断具有挑战性。我们根据多参数磁共振成像(MRI)生成了一个放射组学提名图,用于诊断FCD和早期识别侧位:回顾性入选了 2017 年 7 月至 2022 年 5 月间接受治疗的 43 例经组织病理学证实的 FCD 患者。对侧未受影响的半球作为对照组。因此,最终纳入了86个ROI。以2021年1月为时间分界线,2021年1月后入院的患者被纳入暂不入院组(n = 20)。其余患者随机(8:2)分为训练组(55 人)和验证组(11 人)。所有术前和术后磁共振图像,包括 T1 加权(T1w)、T2 加权(T2w)、体液增强反转恢复(FLAIR)和组合(T1w + T2w + FLAIR)图像,均被纳入其中。采用最小绝对收缩和选择算子(LASSO)来选择特征。多变量逻辑回归分析用于建立诊断模型。用曲线下面积(AUC)、净再分类改进(NRI)、综合辨别改进(IDI)、校准和临床实用性评估了放射组学提名图的性能:从联合序列(T1w + T2w + FLAIR)中选取的基于模型的放射组学特征在所有模型中表现最佳,在训练集(AUCs:0.847 VS. 0.664,p = 0.008)、验证集(AUCs:0.857 VS. 0.521,p = 0.155)和保留集(AUCs:0.828 VS. 0.571,p = 0.080)中的诊断性能均优于无经验的放射科医生。三组数据的 NRI(0.402,0.607,0.424)和 IDI(0.158,0.264,0.264)均为正值,表明模型组合的诊断性能显著提高。放射组学提名图与校准曲线拟合良好(p > 0.05),决策曲线分析进一步证实了提名图的临床实用性。此外,FCD 病变的对比度(放射组学特征)不仅在分类器中发挥了关键作用,而且还具有显著的相关性(r = -0.319,p 结论:FCD 病变的对比度(放射组学特征)不仅在分类器中发挥了关键作用,而且还具有显著的相关性(r = -0.319,p 结论):基于逻辑回归模型的多参数磁共振成像生成的放射组学提名图是 FCD 诊断和治疗的重要进步。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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