Astrid Schiulaz, Giovanna Nordio, Alessio Giacomel, Rubaida Easmin, Andrea Bettinelli, Pierluigi Selvaggi, Steven Williams, Federico Turkheimer, Sameer Jauhar, Oliver Howes, Mattia Veronese
{"title":"精神病患者纹状体[18F]FDOPA PET成像的放射组学分析及其对抗精神病药物反应的鉴别。","authors":"Astrid Schiulaz, Giovanna Nordio, Alessio Giacomel, Rubaida Easmin, Andrea Bettinelli, Pierluigi Selvaggi, Steven Williams, Federico Turkheimer, Sameer Jauhar, Oliver Howes, Mattia Veronese","doi":"10.1007/s11307-025-02014-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Schizophrenia (SCZ) is a severe psychiatric disorder marked by abnormal dopamine synthesis, measurable through [<sup>18</sup>F]FDOPA PET imaging. This imaging technique has been proposed as a biomarker for treatment stratification in SCZ, where one-third of patients respond poorly to standard antipsychotics. This study explores the use of radiomics on [<sup>18</sup>F]FDOPA PET data to examine dopamine synthesis in SCZ and predict antipsychotic response.</p><p><strong>Methods: </strong>We analysed 273 [<sup>18</sup>F]FDOPA PET scans from healthy controls (n = 138) and SCZ patients (n = 135) from multiple cohorts, including first-episode psychosis cases. Radiomic features from striatal regions were extracted using the MIRP Python package. Reproducibility was assessed with test-retest scans, selecting features with an intraclass correlation coefficient (ICC) > 0.80. These features were grouped via hierarchical clustering based on Spearman correlation. Regression analysis evaluated sex and age effects on radiomic features. Predictive power for treatment response was tested and compared to standard imaging analysis obtained from the Standardised Uptake Value ratio (SUVr) of striatal over cerebellar tracer activity.</p><p><strong>Results: </strong>Out of 177 features, 15 met the ICC criteria (ICC: 0.81-0.99). Age and sex influenced features in patients but not in controls. The best performance were was by the GLCM joint maximum feature, which effectively differentiated responders from non-responders (AUC: 0.66-0.87), but did not reach statistical significance in classification over SUVr.</p><p><strong>Conclusion: </strong>Radiomic analysis of [<sup>18</sup>F]FDOPA PET supports its use as a biomarker for assessing antipsychotic efficacy in schizophrenia, highlighting differential striatal tracer uptake based on patient response. While it provides modest classification improvements over standard imaging, further validation in larger datasets and integration with multivariate classification algorithms are needed.</p>","PeriodicalId":18760,"journal":{"name":"Molecular Imaging and Biology","volume":" ","pages":"365-378"},"PeriodicalIF":2.5000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162767/pdf/","citationCount":"0","resultStr":"{\"title\":\"Radiomic Analysis of Striatal [<sup>18</sup>F]FDOPA PET Imaging in Patients with Psychosis for the Identification of Antipsychotic Response.\",\"authors\":\"Astrid Schiulaz, Giovanna Nordio, Alessio Giacomel, Rubaida Easmin, Andrea Bettinelli, Pierluigi Selvaggi, Steven Williams, Federico Turkheimer, Sameer Jauhar, Oliver Howes, Mattia Veronese\",\"doi\":\"10.1007/s11307-025-02014-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Schizophrenia (SCZ) is a severe psychiatric disorder marked by abnormal dopamine synthesis, measurable through [<sup>18</sup>F]FDOPA PET imaging. This imaging technique has been proposed as a biomarker for treatment stratification in SCZ, where one-third of patients respond poorly to standard antipsychotics. This study explores the use of radiomics on [<sup>18</sup>F]FDOPA PET data to examine dopamine synthesis in SCZ and predict antipsychotic response.</p><p><strong>Methods: </strong>We analysed 273 [<sup>18</sup>F]FDOPA PET scans from healthy controls (n = 138) and SCZ patients (n = 135) from multiple cohorts, including first-episode psychosis cases. Radiomic features from striatal regions were extracted using the MIRP Python package. Reproducibility was assessed with test-retest scans, selecting features with an intraclass correlation coefficient (ICC) > 0.80. These features were grouped via hierarchical clustering based on Spearman correlation. Regression analysis evaluated sex and age effects on radiomic features. Predictive power for treatment response was tested and compared to standard imaging analysis obtained from the Standardised Uptake Value ratio (SUVr) of striatal over cerebellar tracer activity.</p><p><strong>Results: </strong>Out of 177 features, 15 met the ICC criteria (ICC: 0.81-0.99). Age and sex influenced features in patients but not in controls. The best performance were was by the GLCM joint maximum feature, which effectively differentiated responders from non-responders (AUC: 0.66-0.87), but did not reach statistical significance in classification over SUVr.</p><p><strong>Conclusion: </strong>Radiomic analysis of [<sup>18</sup>F]FDOPA PET supports its use as a biomarker for assessing antipsychotic efficacy in schizophrenia, highlighting differential striatal tracer uptake based on patient response. While it provides modest classification improvements over standard imaging, further validation in larger datasets and integration with multivariate classification algorithms are needed.</p>\",\"PeriodicalId\":18760,\"journal\":{\"name\":\"Molecular Imaging and Biology\",\"volume\":\" \",\"pages\":\"365-378\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162767/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Imaging and Biology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11307-025-02014-3\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/5 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Imaging and Biology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11307-025-02014-3","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/5 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Radiomic Analysis of Striatal [18F]FDOPA PET Imaging in Patients with Psychosis for the Identification of Antipsychotic Response.
Purpose: Schizophrenia (SCZ) is a severe psychiatric disorder marked by abnormal dopamine synthesis, measurable through [18F]FDOPA PET imaging. This imaging technique has been proposed as a biomarker for treatment stratification in SCZ, where one-third of patients respond poorly to standard antipsychotics. This study explores the use of radiomics on [18F]FDOPA PET data to examine dopamine synthesis in SCZ and predict antipsychotic response.
Methods: We analysed 273 [18F]FDOPA PET scans from healthy controls (n = 138) and SCZ patients (n = 135) from multiple cohorts, including first-episode psychosis cases. Radiomic features from striatal regions were extracted using the MIRP Python package. Reproducibility was assessed with test-retest scans, selecting features with an intraclass correlation coefficient (ICC) > 0.80. These features were grouped via hierarchical clustering based on Spearman correlation. Regression analysis evaluated sex and age effects on radiomic features. Predictive power for treatment response was tested and compared to standard imaging analysis obtained from the Standardised Uptake Value ratio (SUVr) of striatal over cerebellar tracer activity.
Results: Out of 177 features, 15 met the ICC criteria (ICC: 0.81-0.99). Age and sex influenced features in patients but not in controls. The best performance were was by the GLCM joint maximum feature, which effectively differentiated responders from non-responders (AUC: 0.66-0.87), but did not reach statistical significance in classification over SUVr.
Conclusion: Radiomic analysis of [18F]FDOPA PET supports its use as a biomarker for assessing antipsychotic efficacy in schizophrenia, highlighting differential striatal tracer uptake based on patient response. While it provides modest classification improvements over standard imaging, further validation in larger datasets and integration with multivariate classification algorithms are needed.
期刊介绍：
Molecular Imaging and Biology (MIB) invites original contributions (research articles, review articles, commentaries, etc.) on the utilization of molecular imaging (i.e., nuclear imaging, optical imaging, autoradiography and pathology, MRI, MPI, ultrasound imaging, radiomics/genomics etc.) to investigate questions related to biology and health. The objective of MIB is to provide a forum to the discovery of molecular mechanisms of disease through the use of imaging techniques. We aim to investigate the biological nature of disease in patients and establish new molecular imaging diagnostic and therapy procedures.
Some areas that are covered are:
Preclinical and clinical imaging of macromolecular targets (e.g., genes, receptors, enzymes) involved in significant biological processes.
The design, characterization, and study of new molecular imaging probes and contrast agents for the functional interrogation of macromolecular targets.
Development and evaluation of imaging systems including instrumentation, image reconstruction algorithms, image analysis, and display.
Development of molecular assay approaches leading to quantification of the biological information obtained in molecular imaging.
Study of in vivo animal models of disease for the development of new molecular diagnostics and therapeutics.
Extension of in vitro and in vivo discoveries using disease models, into well designed clinical research investigations.
Clinical molecular imaging involving clinical investigations, clinical trials and medical management or cost-effectiveness studies.
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