RadxTools for assessing tumor treatment response on imaging

用于评估影像学肿瘤治疗反应的 RadxTools

• 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
• 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

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：髓母细胞瘤是一种恶性、生长迅速、异质性的儿童脑肿瘤。 结果，5年生存率为70%-80%。目前对MB患者的治疗策略包括手术治疗 高危患者的切除、化疗和头颅脊髓照射(CSI)，并增加剂量 (定义为残留肿瘤，1.5平方厘米，软脑膜转移的证据，或大细胞/间变性组织学) 改善临床结果，同时降低治疗以减少标准风险MB的长期后遗症 病人。不幸的是，这种治疗方案仅被证明是有用的，作为预测的粗略指南。 与现有临床分层的预后；特别是高危患者的5年生存率 目前约为60%。此外，现有的临床风险分层未能识别出约20%-30%的 标准风险患者，他们可能被过度治疗，并最终患有长期的 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。 根据患者的生存情况，目标是确定最有可能接受 从辅助和伴随治疗中获得额外的好处，同时在低/标准风险病例中降低治疗的难度。 通过癌症信息学技术正在进行的NCI U01奖(1U01CA248226-01) 研究(ITCR)，我们的团队一直在领导肿瘤周围(EUR。RAD 20172，AJNR 20183) 和肿瘤内空间异质性放射组学，这超越了纹理，基于形状的方法， 成人肿瘤的特征。作为我们U01努力的延伸，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扫描肿瘤定性的放射组学分析 (2)风险分层模块，用于儿科MB患者的生存风险分层。 在我们的初步工作中，我们演示了我们的生物物理变形描述符，它描述了微妙的 在Gd-T1w磁共振扫描上，脑实质血管扩张的改变具有较高的一致性指数(C- 指数)预测MB患者的总生存期与采用基于分子亚组的 分层[n=，p&lt；0.05vs.p=0.6，C指数=0.831 vs.0.80]。在我们的附录中开发的两个模块 将利用项目来改进我们的初始模型(仅使用变形)，以(A)包括功能 与(1)3D拓扑、(2)局部熵和(3)来自肿瘤附近的肿瘤周围特征和 (B)根据MB患者的生存特征，对其进行基于MRI的风险分层，独立于 分子分层。我们与儿童医院Cinncinati，全国儿童医院的合作 哥伦布，和儿童脑瘤网络，将导致创建一个丰富的，最大的MB队列之一 儿科癌症社区，并将作为正在进行的和未来重点研究的基础 解决甲基溴病原学。在成功完成后，我们将进行多机构研究和 通过ITCR利用正在进行的努力，公开提供相关的mRRisc功能。

项目成果

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
• 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.

• DOI: 10.3390/cancers12123663
• 发表时间: 2020-12-07
• 期刊: Cancers
• 影响因子: 5.2
• 作者: Alvarez-Jimenez C;Sandino AA;Prasanna P;Gupta A;Viswanath SE;Romero E
• 通讯作者: Romero E

Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.

小儿髓母细胞瘤肿瘤的放射组学和放射基因组学的机会和进步。

• DOI: 10.3390/diagnostics13172727
• 发表时间: 2023-08-22
• 期刊: DIAGNOSTICS
• 影响因子: 3.6
• 作者: Ismail, Marwa;Craig, Stephen;Ahmed, Raheel;de Blank, Peter;Tiwari, Pallavi
• 通讯作者: Tiwari, Pallavi

Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.

• DOI: 10.3389/fmed.2023.1149056
• 发表时间: 2023
• 期刊: Frontiers in medicine
• 影响因子: 3.9