通过小脑结构性磁共振成像应用可解释的放射组学模型识别精神分裂症。

IF 5 3区 医学 Q1 CLINICAL NEUROLOGY
Psychiatry and Clinical Neurosciences Pub Date : 2024-09-01 Epub Date: 2024-07-02 DOI:10.1111/pcn.13707
Minji Bang, Kisung Park, Seoung-Ho Choi, Sung Soo Ahn, Jinna Kim, Seung-Koo Lee, Yae Won Park, Sang-Hyuk Lee
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引用次数: 0

摘要

目的:小脑参与高阶精神处理和感觉运动功能。虽然精神分裂症患者的小脑结构异常已得到证实,但由于缺乏生物标志物,神经影像技术尚不能用于识别这些异常。我们的目标是利用小脑T1加权磁共振成像(T1-MRI)的放射学特征建立一个强大的精神分裂症诊断模型:共有336名参与者(174名精神分裂症患者;162名健康对照组[HCs])被分配到训练组(122名精神分裂症患者;115名健康对照组[HCs])和测试组(52名精神分裂症患者;47名健康对照组[HCs])。我们从小脑亚区的 T1-MRI 中获得了 2568 个放射学特征。在特征选择之后,我们对光梯度提升机分类器进行了训练。对模型的辨别和校准进行了评估。应用SHAPLE Additive exPlanations(SHAP)确定模型的可解释性:结果:我们确定了 17 个放射学特征,用于区分精神分裂症患者和精神疾病患者。在测试队列中,放射组学模型的曲线下面积、准确性、灵敏度和特异性分别为 0.89(95% 置信区间:0.82-0.95)、78.8%、88.5% 和 75.4%。SHAP的模型解释表明,右脑九叶的二阶大小区不均匀性特征和右脑五、六叶的一阶能量特征与精神分裂症的风险高度相关:结论:以小脑为重点的放射组学模型在诊断精神分裂症方面表现出稳健性。我们的研究结果表明,小脑后部微电路的破坏是精神分裂症的一个疾病定义特征,放射组学模型有可能在临床实践中支持基于生物标记的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum.

Aims: 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.

Conclusion: 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.

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来源期刊
CiteScore
7.40
自引率
4.20%
发文量
181
审稿时长
6-12 weeks
期刊介绍: PCN (Psychiatry and Clinical Neurosciences) Publication Frequency: Published 12 online issues a year by JSPN Content Categories: Review Articles Regular Articles Letters to the Editor Peer Review Process: All manuscripts undergo peer review by anonymous reviewers, an Editorial Board Member, and the Editor Publication Criteria: Manuscripts are accepted based on quality, originality, and significance to the readership Authors must confirm that the manuscript has not been published or submitted elsewhere and has been approved by each author
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