Abstract：
OBJECTIVE: The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum.
METHODS: A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability.
RESULTS: We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia.
CONCLUSIONS: The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.
METHODS: A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability.
RESULTS: We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia.
CONCLUSIONS: The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.
摘要：
目的：小脑参与高级心理处理以及感觉运动功能。尽管在精神分裂症中已经证明了小脑的结构异常，由于缺乏生物标志物,神经影像学技术尚不适用于鉴定它们.我们旨在使用小脑的T1加权磁共振成像（T1-MRI）的影像学特征来开发精神分裂症的稳健诊断模型。
方法：总共336名参与者（174名精神分裂症；162名健康对照[HCs]）被分配到训练（122名精神分裂症；115名HCs）和测试（52名精神分裂症；47名HCs）队列中。我们从小脑亚区的T1-MRI获得了2568个影像学特征。选择功能后，训练了光梯度增强机器分类器。对模型的判别和校准进行了评价。Shapley加法扩张（SHAP）用于确定模型的可解释性。
结果：我们确定了17个放射学特征来区分精神分裂症患者和HCs。在测试队列中，影像组学模型在曲线下有一个面积，准确度,灵敏度,特异性为0.89(95%置信区间：0.82-0.95)，78.8%,88.5%,和75.4%，分别。SHAP的模型解释表明，右小叶IX的二阶大小区非均匀性特征和右小叶V和VI的一阶能量特征与精神分裂症的风险高度相关。
结论：以小脑为中心的影像组学模型在精神分裂症诊断中显示出稳健性。我们的结果表明，小脑后部的微电路中断是精神分裂症的疾病定义特征，和影像组学建模有可能在临床实践中支持基于生物标志物的决策。
方法：总共336名参与者（174名精神分裂症；162名健康对照[HCs]）被分配到训练（122名精神分裂症；115名HCs）和测试（52名精神分裂症；47名HCs）队列中。我们从小脑亚区的T1-MRI获得了2568个影像学特征。选择功能后，训练了光梯度增强机器分类器。对模型的判别和校准进行了评价。Shapley加法扩张（SHAP）用于确定模型的可解释性。
结果：我们确定了17个放射学特征来区分精神分裂症患者和HCs。在测试队列中，影像组学模型在曲线下有一个面积，准确度,灵敏度,特异性为0.89(95%置信区间：0.82-0.95)，78.8%,88.5%,和75.4%，分别。SHAP的模型解释表明，右小叶IX的二阶大小区非均匀性特征和右小叶V和VI的一阶能量特征与精神分裂症的风险高度相关。
结论：以小脑为中心的影像组学模型在精神分裂症诊断中显示出稳健性。我们的结果表明，小脑后部的微电路中断是精神分裂症的疾病定义特征，和影像组学建模有可能在临床实践中支持基于生物标志物的决策。
