Artificial Intelligence-based decision support for chemotherapy-response assessment in Brain Tumors

基于人工智能的脑肿瘤化疗反应评估决策支持

• 批准号: 10589512
• 负责人: Pallavi Tiwari
• 金额: --
• 依托单位: WM S. MIDDLETON MEMORIAL VETERANS HOSP
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589512
• 关键词: Adjuvant; Adjuvant Chemotherapy; Adoption; Aftercare; Anatomy; Anxiety; Appearance; Artificial Intelligence; Award; Benign; Biological; Biophysics; Biopsy; Brain; Brain Neoplasms; Cells; Characteristics; Clinical; Clinical Trials; Clinical assessments; Combination Drug Therapy; Combined Modality Therapy; Coupled; Data; Diagnosis; Diagnostic; Discrimination; Disease; Disease Progression; Entropy; Excision; Exposure to; Gadolinium; Genomics; Glioblastoma; Glioma; Gulf War veteran; Healthcare Systems; Heterogeneity; Histologic; Histology; Human; Image; Institution; Intervention; Ionizing radiation; Iraq; Lesion; Link; MGMT gene; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Maps; Measurement; Medical center; Methylation; Molecular; Morbidity - disease rate; Morphology; Neurologic; Ohio; Oncologist; Operative Surgical Procedures; Pathway interactions; Patient Selection; Patients; Pattern; Perfusion; Persons; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Play; Population; Quality of life; Radiation; Radiation therapy; Reader; Reading; Recurrent tumor; Refractory; Resistance; Resort; Risk; Role; Scanning; Shapes; Site; Surface; Survival Rate; Tennessee; Texture; Time Management; Tissues; Treatment-related toxicity; Tumor Biology; Unnecessary Surgery; Validation; Vasodilation; Veterans; Visual; War; Work; brain magnetic resonance imaging; brain parenchyma; chemoradiation; chemotherapy; clinically actionable; cohort; comorbidity; companion diagnostics; conventional therapy; cost; deep learning; diagnostic tool; financial toxicity; follow-up; high risk; imaging biomarker; improved; innovation; mortality; nerve agent; nervous system disorder; neuroimaging; predictive marker; prognostic; promoter; radiation effect; radiologist; radiomics; responders and non-responders; response biomarker; segregation; side effect; standard of care; success; treatment effect; treatment response; tumor; tumor microenvironment

ABSTRACT: In 2020, over 23,000 patients in the US will be diagnosed with Glioblastoma (GBM), a highly aggressive brain tumor, with a dismal median survival of 15-18 months. Studies focusing on Gulf War Veterans especially those exposed to nerve agents in Iraq in 1991 have shown a higher risk of brain tumors among neurological diseases and a distinct neurological brain pattern as compared with the other Veterans. The standard-of-care for GBM consists of surgical resection followed by radiotherapy combined with concomitant and adjuvant chemotherapy. However, ~50% of GBM patients do not respond favorably to chemoradiation following surgery. A priori identification of non-responders could allow for selection of these patients as potential candidates for genomically-driven drug therapies (over 64 ongoing clinical trials in the US) over conventional treatment. Further, chemotherapy costs >$100K/year. There is hence an unmet need to develop and validate predictive biomarkers to identify up front which Veteran patients will not benefit from chemotherapy. Another significant challenge in GBM management is the differentiation of suspicious lesions on post-treatment MRI, as tumor recurrence or treatment-induced radiation effects. In the absence of reliable diagnosis, patients with a benign treatment effect have to undergo an unnecessary surgical confirmation biopsy. The co-morbidities due to unnecessary biopsies disproportionately impact Veteran GBM patients who tend to be older and have increased comorbidity burden. Consequently, developing a companion diagnostic solution using clinical MRI could represent a compelling solution in substantially improve quality-of-life years for Veteran GBM patients by sparing them of the side-effects of surgery, while providing timely management in patients with tumor recurrence. Recently, we have developed a new “Neuro-Image Risk Classifier” (NeuRisC), that uses artificial-intelligence (AI)-driven computational features corresponding to the micro-architectural measurements of disorder in the local intensity gradients (i.e. gradient entropy) on Gadolinium (Gd)-T1w MRI; the initial version of NeuRisC has been shown to (a) be prognostic of GBM survival on n=203 studies (p<0.001), and (2) have an accuracy of 85% (a 37% improvement over expert readers) on n=58 studies in distinguishing radiation effects from tumor recurrence. In this VA project, we propose to further improve, and validate the accuracy of NeuRisC by expanding our initial feature set (using Gd-T1w MRI alone) by including (1) additional features from anatomical (T2w, FLAIR) and functional MR sequences (perfusion), (2) a new class of biophysical deformation attributes from “normal” brain parenchyma, and (3) peritumoral features from outside the lesion. In Aim 1, we will develop (NeuRisC)predict as a predictive image-based marker of benefit to chemotherapy by combining intra- and peri-tumor gradient entropy and biophysical deformation attributes from “normal” brain parenchyma. Similarly, (NeuRisC)diagnose will be developed in Aim 2 by including lesion and peri-lesional features from pre- and multiple post-treatment MRIs, to improve discrimination of radiation effects and tumor recurrence. Overcoming limitations in previous work pertaining to small cohorts & lack of spatially mapped ex-vivo histology, NeuRisC modules will be validated on a large multi-institutional cohort of >1000 studies with co-localized tissue and MRI scans obtained across multiple biopsies/lesion. This cohort will also allow for establishing associations of NeuRisC features with underlying histological/molecular tumor characteristics - a prerequisite for clinical adoption. Lastly, NeuRisC modules will be deployed at Northeast Ohio & Tennessee VA Healthcare Systems to validate their utility as decision support. On an independent cohort of N=200 MRIs from Veteran patients, interpretation results from oncologists and radiologists at these two VA sites will be compared, with and without NeuRisC, to evaluate added benefit of NeuRisC as decision support. Criteria for success will be to demonstrate that NeuRisC is able to (a) predict GBM patients that respond favorably to chemoradiation with >90% accuracy, and (b) is non-inferior to accuracy for invasive biopsies (85-90% accuracy), thereby avoiding biopsies in patients with a benign radiation effect.

摘要：2020年，美国将被诊断出23,000多名胶质母细胞瘤（GBM），这是一种高度 侵略性脑肿瘤，中位生存期为15-18个月。关注海湾退伍军人的研究 特别是那些1991年暴露于伊拉克神经毒剂的人显示出脑肿瘤的风险更高 与其他退伍军人相比，神经系统疾病和独特的神经系统脑模式。这 GBM的标准护理包括手术切除，然后放疗与随之而来 并调整化疗。但是，约有50％的GBM患者对化学放射反应不佳 手术后。对非反应者的先验鉴定可以使这些患者选择作为潜力 常规 治疗。此外，化学疗法的费用>每年$ 10万美元。因此，有未满足的需要开发和验证 预测性生物标志物以确定哪些退伍军人患者不会从化学疗法中受益。其他 GBM管理中的重大挑战是对治疗后MRI的可疑病变的差异，如 肿瘤复发或治疗引起的辐射效应。在没有可靠诊断的情况下，患有A 良性治疗效果必须进行不必要的手术确认活检。合并症应得 不必要的活检对经验丰富的GBM患者的影响不成比例 合并症伯恩的合并症增加。因此，使用临床MRI开发伴侣诊断解决方案 可以代表令人信服的解决方案，从而大大改善了资深GBM患者的生活质量年份 保留他们的手术副作用，同时为肿瘤复发的患者提供及时的管理。 最近，我们开发了一种新的“神经形象风险分类器”（Neurisc），它使用了人工智能 （AI）驱动的计算特征，对应于局部疾病的微构造测量值 gadolinium（GD）-T1W MRI上的强度梯度（即梯度熵）； Neurisc的初始版本已经 （a）在n = 203研究（p <0.001）上显示为（a）的预后，（2）的精度为85％（a n = 58个研究辐射效应与肿瘤复发的研究，比专家读者提高了37％。 在这个VA项目中，我们建议进一步改善，并通过扩展我们的初始 功能集（单独使用GD-T1W MRI）包括（1）解剖学（T2W，FLAIR）和 功能性MR序列（灌注），（2）来自“正常”大脑的新型生物物理变形属性 实质和（3）病变外部的周围特征。在AIM 1中，我们将开发（神经主）预测 通过结合肿瘤内和肿瘤周期熵的基于图像的基于图像的益处标记 来自“正常”脑实质的生物物理变形属性。同样，（神经轴）诊断将是 在AIM 2中开发，包括病变和遗传学特征，从前和多个处理后MRI到 改善辐射效应和肿瘤复发的歧视。克服以前工作的限制 与小型队列和缺乏空间映射的前病学组织学有关的神经盘模块将在 通过共定位的组织和MRI扫描进行的大量> 1000个研究的大型多机构队列 活检/病变。该队列还将允许建立与基础的神经轴特征的关联 组织学/分子肿瘤特征 - 临床采用的先决条件。最后，神经轴模块将 在俄亥俄州东北部和田纳西州VA Healthcare Systems部署，以验证其效用作为决策支持。 在来自资深患者的n = 200 MRI的独立队列中，肿瘤学家和 在这两个VA站点的放射科医生将在有和没有神经轴的情况下进行比较，以评估 神经席作为决策支持。成功的标准将证明神经轴能够（a）预测 对化学放疗的反应率> 90％的GBM患者，并且（b）与准确性无关。 对于侵入性活检（精度为85-90％），从而避免了具有良性辐射效果的患者的活检。