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RadxTools for assessing tumor treatment response on imaging

用于评估影像学肿瘤治疗反应的 RadxTools

基本信息

批准号：
10593646
负责人：
Pallavi Tiwari
金额：
$ 23.4万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10593646
关键词：
3-Dimensional Adjuvant Adult Affect Award Biophysics Brain Neoplasms Cells Characteristics Child Childhood Brain Neoplasm Childhood Medulloblastomas Clinical Clinical stratification Communities Data Descriptor Development Dose Entropy Etiology Excision Foundations Future Goals Heterogeneity Histology Image Informatics MRI Scans Magnetic Resonance Imaging Malignant - descriptor Malignant Childhood Neoplasm Metastatic Neoplasm to the Leptomeninges Modeling Molecular Morbidity - disease rate Operative Surgical Procedures Outcome Patients Pediatric Hospitals Pediatric cohort Prognosis Quality of life RadxTools Residual Tumors Risk Shapes Stratification Subgroup Survival Rate Technology Texture Treatment Protocols United States National Institutes of Health Vasodilation Work anticancer research base brain parenchyma chemotherapy clinical care clinical risk high risk improved indexing irradiation long-term sequelae medulloblastoma prognostication radiomics risk stratification tool treatment response treatment strategy tumor

项目摘要

ABSTRACT: Medulloblastoma (MB) is a malignant, fast-growing pediatric brain tumor with heterogenous outcomes and a 5-year survival rate of 70-80%. Current treatment strategies for MB patients include surgical resection, chemotherapy, and craniospinal irradiation (CSI), with dose-intensification in high-risk patients (defined as residual tumor >1.5 cm2, evidence of leptomeningeal metastases, or large-cell/anaplastic histology) to improve clinical outcomes, while de-escalation of therapy to reduce long-term sequelae in standard-risk MB patients. Unfortunately, this treatment protocol has only proven useful as a rough guide for predicting prognosis with the existing clinical stratification; particularly, the 5-year survival rate for the high-risk patients are currently at about 60%. Additionally, the existing clinical risk stratification fails to identify about 20–30% of standard-risk patients who might be overtreated and eventually suffer from long-term morbidities that significantly affect their quality of life. Consequently, there is a critical need for reliable tools to risk-stratify MB patients based on their survival, with the goal of identifying high-risk MB cases who are most likely to receive added benefit from adjuvant and concomitant therapy, while de-escalating therapy in low/standard-risk cases. Through an ongoing NCI U01 award (1U01CA248226-01) from the Informatics Technology for Cancer Research (ITCR), our group has been leading the development of peri-tumoral (Eur. Rad 20172, AJNR 20183) and intra-tumoral spatial heterogeneity radiomics, that go beyond texture, shape-based approaches, for characterization of adult tumors. As an extension to our U01 efforts, in this supplemental project, we propose to develop two informatics modules for (1) radiomic analysis for tumor characterization on clinical MRI scans (Gd-T1w, T2w, FLAIR), and (2) a risk-stratification module, for survival risk-stratification of pediatric MB patients. In our preliminary work, we demonstrated that our biophysical deformation descriptor that characterizes subtle changes in vasodilation from brain parenchyma on Gd-T1w MRI scans, had higher concordance-index (C- index) in predicting overall survival in MB patients compared to employing the molecular subgroup-based stratification [n=89, p<0.05 vs. p=0.6, C-index=0.831 vs. 0.80]. The 2 modules developed in our supplement project will be leveraged to improve on our initial model (using deformations alone), to (a) include features relating to (1) 3D topology, (2) localized entropy, and (3) peri-tumoral features from the vicinity of the tumor and (b) perform MRI-based risk-stratification of MB patients based on their survival characteristics, independent of molecular stratification. Our collaborative efforts with Children's Hospital Cinncinati, Nationawide Childrens Columbus, and Children's Brain Tumor Network, will led to creation of a rich, one-of-the-largest MB cohorts for the pediatric cancer community and will serve as the foundation of ongoing and future studies focused on resolving MB aetiology. Following successful completion, we will make the multi-institutional studies and the associated mRRisc features publicly available by leveraging ongoing efforts through ITCR.

摘要：髓母细胞瘤（MB）是一种恶性、快速生长的小儿脑肿瘤， 5年生存率为70- 80%。MB患者的当前治疗策略包括手术切除、化疗和颅脊髓照射（CSI），在高危患者中进行剂量强化（定义为残留肿瘤>1.5 cm 2、软脑膜转移证据或大细胞/间变性组织学）改善临床结局，同时降低治疗水平以减少标准风险MB的长期后遗症患者不幸的是，这种治疗方案只被证明是一个粗略的指导，预后与现有的临床分层;特别是，5年生存率的高风险患者目前约为60%。此外，现有的临床风险分层未能识别约20-30%的标准风险患者可能会接受过度治疗并最终患上长期疾病，严重影响他们的生活质量。因此，迫切需要可靠的工具对甲基溴进行风险分层患者的生存率，目的是确定最有可能接受辅助治疗和伴随治疗的额外获益，而在低/标准风险病例中的递减治疗。通过癌症信息技术正在进行的NCI U 01奖（1U 01 CA 248226 -01）研究（ITCR），我们的小组一直领导着肿瘤周围（Eur. Rad 20172，AJNR 20183）和肿瘤内空间异质性放射组学，超越了纹理，基于形状的方法，成人肿瘤的特征。作为我们U 01工作的延伸，在这个补充项目中，我们建议开发两个信息学模块，用于（1）临床MRI扫描肿瘤表征的放射组学分析（Gd-T1 w，T2 w，FLAIR），和（2）风险分层模块，用于儿童MB患者的生存风险分层。在我们的初步工作中，我们证明了我们的生物物理变形描述符，在Gd-T1 w MRI扫描上，脑实质血管舒张的变化具有较高的一致性指数（C- 指数）预测MB患者的总生存率，与采用基于分子亚组的分层[n=89，p<0.05 vs. p=0.6，C指数=0.831 vs. 0.80]。在我们的补充中开发的2个模块项目将利用我们的初始模型（仅使用变形）进行改进，以（a）包括功能涉及（1）3D拓扑，（2）局部熵，和（3）来自肿瘤附近的肿瘤周围特征， (b)根据MB患者的生存特征，对MB患者进行基于MRI的风险分层，独立于分子分层我们与Cinncinati儿童医院、全国儿童医院、哥伦布和儿童脑肿瘤网络，将导致创建一个丰富的，最大的MB队列之一，儿童癌症社区，并将作为正在进行和未来研究的基础，重点是解决MB病因。在成功完成后，我们将进行多机构研究和相关mRRisc功能通过ITCR利用正在进行的工作公开提供。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges.

DOI：
10.1093/noajnl/vdaa148
发表时间：
2020-12
期刊：
Neuro-oncology advances
影响因子：
0
作者：
Beig N;Bera K;Tiwari P
通讯作者：
Tiwari P

Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?-A Feasibility Study.

DOI：
10.3389/fncom.2020.563439
发表时间：
2020
期刊：
Frontiers in computational neuroscience
影响因子：
3.2
作者：
Ismail M;Hill V;Statsevych V;Mason E;Correa R;Prasanna P;Singh G;Bera K;Thawani R;Ahluwalia M;Madabhushi A;Tiwari P
通讯作者：
Tiwari P

Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results.