Generalizable quantitative imaging and machine learning signatures in glioblastoma, for precision diagnostics and personalized treatment: the ReSPOND consortium

摘要 磁共振成像(MRI)方法在神经肿瘤学中的现状提供了很大的 提供结构、生理和代谢特征的丰富特征的潜力- 脑肿瘤，尤其是神经胶质瘤，是一种复杂且高度异质性的癌症。 尤其是胶质母细胞瘤(GBM)预后很差，中位总存活率(OS)低于 15个月，在过去15年中，Stupp协议的改善相对较小 介绍。许多实验性治疗正在进行中；然而，OS在很大程度上仍然存在 停滞不前。改善这一结果的一些障碍是1)疾病的异质性， 这使得在第一阶段甚至第二阶段试验中很难检测到治疗效果，并且调用 个人化，而不是一刀切的治疗策略；2)用于肿瘤的方法 基于大小、增强、灌注和扩散特性的表征相对 粗糙，没有充分利用成像数据的丰富性或其空间异构性。量子- 过去十年发展起来的定量成像和机器学习(QIML)方法显示 极大的潜力剖析基底膜的空间、时间和患者间的异质性； 发现成像和分子特征之间的关系；提供个性化 对临床结果的预测；以及利用数据中微妙的多参数关系 检测肿瘤周围的浸润或辨别与治疗相关的变化，即假性进展 (PSP)，来自真正的肿瘤复发。我们小组一直走在QIML的前列，重点是 A)依靠多参数信号、纹理参数、形状获得丰富的成像表型 属性、从图谱注册获得的空间模式、以及肿瘤生长的生物物理模型， 以及b)使用机器学习将这种成像特征集成到临床预测中 预后、肿瘤周围浸润的早期复发、PSP和GBM的放射学亚型。 尽管QIML方法前景看好，但它有一个臭名昭著的局限性：它们可能会过度匹配特定的 从其派生的数据集，在现实生活中可能表现出较差的重复性 不同扫描仪类型和成像协议的条件。在这项提案中，我们的目标是利用 最近成立的RESPONSE(用于精密诊断的放射组学签名)联盟，以 整合、协调和分析来自全球14个中心的4578个数据集，因此 更合适的是，培训和交叉验证QIML工具以获得更广泛的通用性。这个财团 将产生一个史无前例的数据库，其中包含各种精心协调的MRI和 临床措施，旨在为社区提供健壮和可重现的QIML模型 为这一可怕的脑癌的精确诊断和个性化治疗做出贡献。

Abstract The current state of magnetic resonance imaging (MRI) methods in neurooncology offers great potential for providing rich characterizations of structural, physiological, and metabolic character- istics of brain tumors, especially gliomas, which are complex and highly heterogeneous cancers. Glioblastoma (GBM), in particular, has a grim prognosis, with median overall survival (OS) less than 15 months with relatively little improvement in the past 15 years since the Stupp protocol was introduced. Many experimental treatments are being pursued; however, OS has largely remained stagnant. Some of the obstacles in improving this outcome have been 1) disease heterogeneity, which both renders it difficult to detect treatment effects in Phase 1 or even Phase 2 trials, and calls for personalized, rather than one-size fits-all, treatment strategies; 2) methods used for tumor characterization based on size, enhancement, perfusion and diffusion properties are relatively crude and don't fully leverage the richness of imaging data or their spatial heterogeneity. Quanti- tative imaging and machine learning (QIML) methods developed in the past decade have shown great potential for dissecting the spatial, temporal and inter-patient heterogeneity of GBM; for discoveringrelationships between imaging and molecular characteristics ; foroffering personalized predictions of clinical outcome; and for leveraging subtle multi-parametric relationships in the data to detect peri-tumoral infiltration or distinguish treatment related changes, i.e., pseudo-progression (PsP), from true tumor recurrence. Our group has been at the forefront of QIML, with emphasis on a) obtaining rich imaging phenotypes relying on multi-parametric signals, texture parameters, shape properties, spatial patterns derived from atlas registration, and biophysical models of tumor growth, and b) integrating such imaging signatures using machine learning into predictors of clinical outcome, early recurrence from peri-tumoral infiltration, PsP, and radiologic subtypes of GBM. Despite their promise, QIML methods have a notorious limitation: they might overfit specific datasets from which they are derived, and might display poor reproducibility under real-life conditions of variable scanner types and imaging protocols. In this proposal we aim to leverage the recently formed ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium, to integrate, harmonize, and analyze 4,578 datasets from 14 centers around the world, and hence more appropriately train and cross-validate QIML tools for a wider generalizability. This consortium will generate an unprecedented database of diverse and carefully harmonized sets of MRI and clinical measures, and aims to provide the community with robust and reproducible QIML models contributing to precision diagnostics and personalize treatment for this dreaded brain cancer.