Body Composition Factors in Myeloma Spectrum Patients that Predict Morbidity, Mortality, and Progression: Opportunistic Screening Using Whole Body Low Dose CT

预测发病率、死亡率和进展的骨髓瘤谱系患者的身体成分因素：使用全身低剂量 CT 进行机会性筛查

• 批准号: 538209205
• 负责人: Fabian Stefan Bauer
• 金额: --
• 依托单位: Abteilung Radiologie
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/538209205?language=en
• 关键词: Body; Composition; Factors; Myeloma; Spectrum

The proposed research project aims to establish predictive models for morbidity, mortality, and disease progression in the myeloma spectrum, ranging from monoclonal gammopathy of undetermined significance to (smoldering) multiple myeloma. For this study, body composition measurements will be obtained from whole body low dose CT scans, utilizing approximately 430 CT scans performed at the host institution between July 2017 and the present. The whole body low dose CT scans will be retrospectively analyzed and 2D body composition analysis will be performed on selected thoracic (T3, T5, T7) and lumbar (L4) levels. The body composition measurements comprise the quantification and assessment of the cross sectional area and density of total adipose tissue, subcutaneous and visceral adipose tissue, intermuscular adipose tissue, muscle tissue and vertebral bone. These measurements will be performed using validated segmentation algorithms. Subsequently, measurements will be averaged and indexed as body composition markers based on patient weight, patient height, age, and gender. The patient outcomes, including time to progression, days of hospitalization, chemotherapy response, adverse effects, fractures, progression-free survival, and overall survival, will be documented and correlated with patient and disease characteristics and body composition markers. Multivariate analyses will be conducted to establish predictive models. This proposed study aligns with routine clinical practice as whole body low dose CT is indicated and regularly performed when suspecting myeloma spectrum disease, allowing for opportunistic screening without additional risk to the patients. By utilizing body composition measurements from these scans, the study aims to identify additional body composition markers with predictive value, potentially improving the prediction of disease progression as well as patient outcomes, which may lead to an enhancement of patient management and treatment strategies.

拟议的研究项目旨在建立骨髓瘤谱系的发病率、死亡率和疾病进展的预测模型，范围从意义未定的单克隆丙种球蛋白病到（闷烧）多发性骨髓瘤。在本研究中，身体成分测量值将从全身低剂量 CT 扫描中获得，利用 2017 年 7 月至今在主办机构进行的约 430 次 CT 扫描。将回顾性分析全身低剂量 CT 扫描，并对选定的胸椎（T3、T5、T7）和腰椎（L4）水平进行 2D 身体成分分析。身体成分测量包括总脂肪组织、皮下和内脏脂肪组织、肌间脂肪组织、肌肉组织和椎骨的横截面积和密度的量化和评估。这些测量将使用经过验证的分割算法进行。随后，将根据患者体重、患者身高、年龄和性别对测量结果进行平均并索引为身体成分标记。患者的结果，包括疾病进展时间、住院天数、化疗反应、不良反应、骨折、无进展生存期和总生存期，将被记录下来，并与患者和疾病特征以及身体成分标志物相关联。将进行多变量分析以建立预测模型。这项拟议的研究符合常规临床实践，因为当怀疑骨髓瘤谱系疾病时，需要进行全身低剂量 CT 扫描并定期进行，从而可以进行机会性筛查，而不会给患者带来额外的风险。通过利用这些扫描的身体成分测量结果，该研究旨在识别具有预测价值的其他身体成分标记物，从而有可能改善疾病进展和患者结果的预测，从而可能导致患者管理和治疗策略的增强。