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：到2020年，美国将有超过23，000名患者被诊断患有胶质母细胞瘤（GBM），这是一个高度危险的疾病。 侵袭性脑肿瘤，中位生存期为15-18个月。研究重点是海湾战争老兵 特别是1991年在伊拉克暴露于神经毒剂的人， 与其他退伍军人相比，神经系统疾病和独特的神经系统大脑模式。的 GBM的标准治疗包括手术切除，随后进行放疗， 和辅助化疗。然而，~50%的GBM患者对放化疗反应不佳， 手术后。无应答者的先验识别可以允许选择这些患者作为潜在的 基因组驱动的药物治疗的候选人（在美国超过64个正在进行的临床试验）超过传统的 治疗此外，化疗费用> 10万美元/年。因此，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疗中受益。另一 GBM管理的一个重大挑战是在治疗后MRI上区分可疑病变， 肿瘤复发或治疗引起的辐射效应。在缺乏可靠诊断的情况下， 良性的治疗效果必须经过一个不必要的手术确认活检。由于合并症 不必要的活检不成比例地影响退伍军人GBM患者，他们往往年龄较大， 增加了共同负担。因此，开发使用临床MRI的伴随诊断解决方案 可以代表一个引人注目的解决方案，大大提高退伍军人GBM患者的生活质量年， 避免手术的副作用，同时为肿瘤复发患者提供及时的治疗。 最近，我们开发了一种新的“神经图像风险分类器”（NeuRisC），它使用人工智能 （AI）驱动的计算特征对应于局部无序的微观结构测量 钆（Gd）-T1 w MRI上的强度梯度（即梯度熵）; NeuRisC的初始版本已 显示（a）在n=203项研究中预测GBM存活（p<0.001），和（2）具有85%的准确性（a 与专家阅片者相比提高37%），在n=58项研究中区分放射效应与肿瘤复发。 在这个VA项目中，我们建议通过扩展我们的初始模型来进一步改进和验证NeuRisC的准确性。 特征集（仅使用Gd-T1 w MRI），包括（1）解剖学（T2 w，FLAIR）的附加特征， 功能性MR序列（灌注），（2）来自“正常”大脑的一类新的生物物理变形属性 实质;（3）病变外的瘤周特征。在目标1中，我们将开发（NeuRisC）预测作为一个 通过结合肿瘤内和肿瘤周围梯度熵的化疗益处的基于图像的预测性标记 和来自“正常”脑实质的生物物理变形属性。同样，（NeuRisC）诊断将是 在目标2中开发，包括治疗前和多次治疗后MRI的病变和病变周围特征， 提高了对放射效应和肿瘤复发辨别力。克服以往工作的局限性 关于小队列和缺乏空间映射的离体组织学，NeuRisC模块将在 一个大型的多机构队列，包含>1000项研究，在多个研究中获得了共定位组织和MRI扫描， 活检/病变。该队列还将允许建立NeuRisC特征与潜在的 组织学/分子肿瘤特征-临床采用的先决条件。最后，NeuRisC模块将 部署在东北俄亥俄州和田纳西州VA医疗保健系统，以验证其作为决策支持的实用性。 在N=200例来自退伍军人患者的MRI的独立队列中， 将比较这两个VA研究中心的放射科医生，使用和不使用NeuRisC，以评价 NeuRisC作为决策支持。成功的标准是证明NeuRisC能够（a）预测 GBM患者对放化疗反应良好，准确性>90%，且（B）不劣于准确性 用于侵入性活检（准确率85-90%），从而避免了对具有良性辐射效应的患者进行活检。