Abstract：
OBJECTIVE: To investigate the value of magnetic resonance imaging (MRI) radiomics for distinguishing solitary fibrous tumor (SFT) from schwannoma in the orbit.
METHODS: A total of 140 patients from two institutions were retrospectively included. All patients from institution 1 were randomized into a training cohort (n = 69) and a validation cohort (n = 35), and patients from institution 2 were used as an external testing cohort (n = 36). One hundred and six features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CET1WI). A radiomics model was built for each sequence using least absolute shrinkage and selection operator logistic regression, and radiomics scores were calculated. A combined model was constructed and displayed as a radiomics nomogram. Two radiologists jointly assessed tumor category based on MRI findings. The performances of the radiomics models and visual assessment were compared via area under the curve (AUC).
RESULTS: The performances of the radiomics nomogram combining T2WI and CET1WI radiomics scores were superior to those of the pooled readers in the training (AUC 0.986 vs. 0.807, p < 0.001), validation (AUC 0.989 vs. 0.788, p = 0.009), and the testing (AUC 0.903 vs. 0.792, p = 0.093), although significant difference was not found in the testing cohort. Decision curve analysis demonstrated that the radiomics nomogram had better clinical utility than visual assessment.
CONCLUSIONS: MRI radiomics nomogram can be used for distinguishing between orbital SFT and schwannoma, which may help tumor management by clinicians.
CONCLUSIONS: It is of great importance and challenging for distinguishing solitary fibrous tumor from schwannoma in the orbit. In the present study, an MRI-based radiomics nomogram were developed and independently validated, which could help the discrimination of the two entities.
CONCLUSIONS: • It is challenging to differentiate solitary fibrous tumor from schwannoma in the orbit due to similar clinical and image features. • A radiomics nomogram based on T2-weighted imaging and contrast-enhanced T1-weighted imaging has advantages over radiologists. • Radiomics can provide a non-invasive diagnostic tool for differentiating between the two entities.
METHODS: A total of 140 patients from two institutions were retrospectively included. All patients from institution 1 were randomized into a training cohort (n = 69) and a validation cohort (n = 35), and patients from institution 2 were used as an external testing cohort (n = 36). One hundred and six features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CET1WI). A radiomics model was built for each sequence using least absolute shrinkage and selection operator logistic regression, and radiomics scores were calculated. A combined model was constructed and displayed as a radiomics nomogram. Two radiologists jointly assessed tumor category based on MRI findings. The performances of the radiomics models and visual assessment were compared via area under the curve (AUC).
RESULTS: The performances of the radiomics nomogram combining T2WI and CET1WI radiomics scores were superior to those of the pooled readers in the training (AUC 0.986 vs. 0.807, p < 0.001), validation (AUC 0.989 vs. 0.788, p = 0.009), and the testing (AUC 0.903 vs. 0.792, p = 0.093), although significant difference was not found in the testing cohort. Decision curve analysis demonstrated that the radiomics nomogram had better clinical utility than visual assessment.
CONCLUSIONS: MRI radiomics nomogram can be used for distinguishing between orbital SFT and schwannoma, which may help tumor management by clinicians.
CONCLUSIONS: It is of great importance and challenging for distinguishing solitary fibrous tumor from schwannoma in the orbit. In the present study, an MRI-based radiomics nomogram were developed and independently validated, which could help the discrimination of the two entities.
CONCLUSIONS: • It is challenging to differentiate solitary fibrous tumor from schwannoma in the orbit due to similar clinical and image features. • A radiomics nomogram based on T2-weighted imaging and contrast-enhanced T1-weighted imaging has advantages over radiologists. • Radiomics can provide a non-invasive diagnostic tool for differentiating between the two entities.
摘要：
目的：探讨磁共振成像（MRI）影像组学在鉴别眼眶孤立性纤维瘤（SFT）与神经鞘瘤中的应用价值。
方法：回顾性纳入来自两个机构的140例患者。来自机构1的所有患者被随机分为训练队列（n=69）和验证队列（n=35），来自机构2的患者被用作外部检测队列(n=36).从T2加权成像（T2WI）和对比增强T1加权成像（CET1WI）中提取了106个特征。使用最小绝对收缩和选择算子逻辑回归为每个序列建立放射组学模型，并计算了影像组学评分。构建了组合模型并将其显示为放射组学列线图。两名放射科医生根据MRI检查结果共同评估肿瘤类别。通过曲线下面积（AUC）比较了影像组学模型和视觉评估的性能。
结果：结合T2WI和CET1WI影像组学评分的影像组学列线图的表现优于培训中的汇总读者（AUC0.986vs.0.807，p<0.001)，验证(AUC0.989vs.0.788,p=0.009),和测试(AUC0.903vs.0.792,p=0.093),尽管在测试队列中没有发现显著差异。决策曲线分析表明，影像组学列线图比视觉评估具有更好的临床实用性。
结论：MRI影像组学列线图可用于区分眼眶SFT和神经鞘瘤，这可能有助于临床医生的肿瘤管理。
结论：对于鉴别眼眶孤立性纤维性肿瘤和神经鞘瘤具有重要意义和挑战性。在本研究中,开发并独立验证了基于MRI的放射组学列线图，这可以帮助歧视这两个实体。
结论：•由于相似的临床和影像特征，将孤立性纤维性肿瘤与眼眶神经鞘瘤区分开来具有挑战性。•基于T2加权成像和对比增强T1加权成像的放射组学列线图优于放射科医师。•影像组学可以提供用于区分两个实体的非侵入性诊断工具。
方法：回顾性纳入来自两个机构的140例患者。来自机构1的所有患者被随机分为训练队列（n=69）和验证队列（n=35），来自机构2的患者被用作外部检测队列(n=36).从T2加权成像（T2WI）和对比增强T1加权成像（CET1WI）中提取了106个特征。使用最小绝对收缩和选择算子逻辑回归为每个序列建立放射组学模型，并计算了影像组学评分。构建了组合模型并将其显示为放射组学列线图。两名放射科医生根据MRI检查结果共同评估肿瘤类别。通过曲线下面积（AUC）比较了影像组学模型和视觉评估的性能。
结果：结合T2WI和CET1WI影像组学评分的影像组学列线图的表现优于培训中的汇总读者（AUC0.986vs.0.807，p<0.001)，验证(AUC0.989vs.0.788,p=0.009),和测试(AUC0.903vs.0.792,p=0.093),尽管在测试队列中没有发现显著差异。决策曲线分析表明，影像组学列线图比视觉评估具有更好的临床实用性。
结论：MRI影像组学列线图可用于区分眼眶SFT和神经鞘瘤，这可能有助于临床医生的肿瘤管理。
结论：对于鉴别眼眶孤立性纤维性肿瘤和神经鞘瘤具有重要意义和挑战性。在本研究中,开发并独立验证了基于MRI的放射组学列线图，这可以帮助歧视这两个实体。
结论：•由于相似的临床和影像特征，将孤立性纤维性肿瘤与眼眶神经鞘瘤区分开来具有挑战性。•基于T2加权成像和对比增强T1加权成像的放射组学列线图优于放射科医师。•影像组学可以提供用于区分两个实体的非侵入性诊断工具。
