Background and Hypothesis Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis. Study Design A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure. Study Results The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P > 0.82) while outperformed the single-site models (Delong test, P < 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients’ positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable). Conclusions In summary, the proposed MICI biomarker may provide a simple and explainable way to support clinicians for objectively diagnosing schizophrenia. Finally, we developed an online model share platform to promote biomarker generalization and provide free individual prediction services (http://micc.tmu.edu.cn/mici/index.html).

中文翻译：

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形态计量综合分类指数：基于多位点模型、可解释、可共享和可进化的精神分裂症生物标志物

背景和假设 多站点大规模精神分裂症神经影像数据共享对于理解精神分裂症的病理生理机制和做出客观诊断变得至关重要；获得一种可概括、可解释、可共享和可进化的神经影像生物标志物用于精神分裂症诊断仍然具有挑战性。研究设计 基于来自 10 个地点的 1270 名受试者（588 名精神分裂症患者和 682 名正常对照）的结构磁共振成像数据，提出形态计量综合分类指数 (MICI) 作为精神分裂症诊断的潜在生物标志物。使用最佳 XGBoost 分类器加上样本加权 Shapley Additive 解释算法来构建 MICI 度量。研究结果 MICI 测量与样本加权集成模型和基于原始数据的合并模型（Delong 检验，P > 0.82）取得了可比的性能，同时在以下任一方面优于单站点模型（Delong 检验，P < 0.05）。来自 9 个站点的独立样本测试数据集或独立站点数据集（可概括）。此外，当嵌入新站点时，该措施的性能逐渐提高（可进化）。最后，MICI 与精神分裂症大脑结构异常的严重程度、患者的阳性和阴性症状以及精神分裂症风险基因的大脑表达谱（可解释）密切相关。结论 总之，所提出的 MICI 生物标志物可能提供一种简单且可解释的方法来支持临床医生客观诊断精神分裂症。最后，我们开发了一个在线模型共享平台，以促进生物标志物泛化并提供免费的个体预测服务（http://micc.tmu.edu.cn/mici/index.html）。