Multi-atlas and whole body radiomics approaches for image-guided treatment of gynecologic cancers

用于妇科癌症图像引导治疗的多图谱和全身放射组学方法

• 批准号: 10641665
• 负责人: Loren K. Mell
• 金额: $ 21.81万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10641665
• 关键词: Acute; Adjuvant Chemotherapy; Adjuvant Therapy; Aftercare; Atlases; Biological Markers; Bladder; Bone Marrow; Cancer Etiology; Cancer Patient; Cessation of life; Chemotherapy and/or radiation; Cisplatin; Compensation; Consumption; Data; Dependence; Disease; Distant; Dose; Effectiveness; Femur; Functional Imaging; Goals; Grant; Hematology; Hematopoiesis; Hematopoietic; Inflammatory; Injury; Intensity-Modulated Radiotherapy; Intestines; Malignant Female Reproductive System Neoplasm; Malignant Neoplasms; Methods; Modeling; Morbidity - disease rate; Neck; Normal tissue morphology; Organ; Outcome; Patient Selection; Patient-Focused Outcomes; Patients; Pelvic Cancer; Pelvis; Pharmaceutical Preparations; Phase; Population; Positron-Emission Tomography; Predictive Factor; Process; Radiation; Radiation Dose Unit; Radiation therapy; Randomized; Rectum; Recurrence; Research; Resources; Sampling; Science; Site; Techniques; Technology; Testing; Therapeutic; Time; Toxic effect; Treatment Failure; Treatment outcome; Triapine; Vaccination; Woman; Work; advanced disease; bone; chemoradiation; chemotherapy; comparative effectiveness trial; cost; cost effective; data quality; dosimetry; fluorodeoxyglucose positron emission tomography; gastrointestinal; image guided; image guided radiation therapy; image-guided radiation; imaging approach; imaging biomarker; improved; improved outcome; innovation; machine learning method; mortality; novel; participant enrollment; personalized medicine; phase III trial; predictive marker; quality assurance; radiation-induced injury; radiomics; recruit; rectal; response; screening; socioeconomic disadvantage; standard care; standard of care; tool; treatment effect; trial design; whole body imaging

ABSTRACT Gynecologic cancers are among the leading causes of cancer death in women worldwide. These patients typically are socioeconomically disadvantaged, with poor access to screening and vaccination. Consequently, they often present with locoregionally advanced disease, for which pelvic radiotherapy (RT) with concurrent cisplatin (i.e., chemoradiotherapy) is the standard of care. This treatment is limited, however, by high rates of treatment failure. Intensifying treatment through the delivery of chemotherapy doublets, either concurrently or as adjuvant therapy following chemoradiotherapy, is a promising strategy to improve outcomes. However, the delivery of intensive chemotherapy is complicated by high rates of gastrointestinal and hematologic toxicity. Strategies to reduce toxicity while increasing efficacy of chemoradiotherapy are needed. Standard pelvic RT techniques encompass large volumes of normal tissue including bowel, bone marrow, bone, bladder, and rectum, leading to preventable radiation-induced toxicity. Image-guided radiation therapy (IGRT) can improve target localization and dosimetry, optimizing target dose while minimizing dose to surrounding normal tissues. However, IGRT can be highly resource intensive, and comparative effectiveness trials have been lacking. For this reason, there is considerable controversy as to the utility of IG-IMRT in this disease. Our research group has been at the forefront of developing novel, cost-effective IGRT approaches with wide potential to facilitate better delivery of concurrent and/or adjuvant chemotherapy. Previously we have found that radiation-induced injury to hematopoietically active bone marrow is a critical determinant of tolerance to intensive chemotherapy. Using machine learning methods, we recently developed a multi-atlas-based IGRT method that can predict canonical distributions of active bone marrow, which can obviate the need for positron emission tomography (PET) in settings where this technology is unavailable or unaffordable. The proposed new research will study the ability of multi-atlas-based IGRT to reduce hematologic toxicity and improve chemotherapy delivery compared to standard treatment, using data from 450 patients enrolled to a randomized phase III trial (NRG-GY006). Furthermore, we will use serial whole body PET/CT to study the impact of radiation dose and chemotherapy intensity on the compensatory hematopoietic response, and have developed novel whole body radiomics biomarkers to quantify the inflammatory state, which we hypothesize can influence patients' outcomes and tolerance to chemotherapy. The new research extends our work associated with a current R01 grant (1R01CA197059-01) to conduct correlative science associated with the GY006 trial. The overarching goal of this research line is to augment the therapeutic ratio of chemoradiotherapy for pelvic cancers using advanced image-guided radiation techniques. If successful, this research would significantly alter the approach to the treatment of many pelvic malignancies for which chemoradiotherapy is standard.

抽象的 妇科癌症是全世界女性癌症死亡的主要原因之一。这些 患者通常在社会经济上处于不利地位，接受筛查和疫苗接种不良。 因此，它们经常出现局部晚期疾病，骨盆放射疗法（RT）具有 并发顺铂（即化学放疗）是护理标准。但是，这种治疗受到限制 高治疗失败率。通过化学疗法双重疗法加强治疗，要么 化学放疗后同时或作为辅助治疗是改善预后的有前途的策略。 然而，高胃肠道和 血液学毒性。需要降低毒性的策略，同时增加化学放疗的功效。 标准骨盆RT技术包括大量正常组织，包括肠子，骨头 骨髓，骨，膀胱和直肠，导致可预防的辐射诱导的毒性。图像引导的辐射 治疗（IGRT）可以改善目标定位和剂量法，优化靶剂量，同时最大程度地减少剂量 周围正常组织。但是，IGRT可能是资源密集的高度，并且比较有效性 试验缺乏。因此，关于IG-IMRT的实用性存在很大的争议 疾病。我们的研究小组一直处于发展新颖的，具有成本效益的IGRT方法的最前沿 具有广泛的潜力，可以促进更好地提供并发和/或辅助化学疗法。 以前，我们发现辐射引起的造血活性骨髓损伤是 对强化化疗的耐受性的关键决定因素。使用机器学习方法，我们最近 开发了一种基于多ATLA的IGRT方法，可以预测活性骨髓的规范分布， 这可以消除在该技术所在的设置中对正电子发射断层扫描（PET）的需求 无法获得或负担不起。拟议的新研究将研究基于多ATLA的IGRT的能力 与标准治疗相比，减少血液学毒性并改善化学疗法的递送，使用数据 从450名招募的患者到一项随机III期试验（NRG-GY006）。此外，我们将使用串行整体 身体PET/CT研究辐射剂量和化学疗法强度对代偿性的影响 造血反应，并开发了新型的全身放射组生物标志物来量化 我们假设的炎症状态可以影响患者的结局和对化学疗法的耐受性。 这项新的研究将我们的工作扩展到当前的R01赠款（1R01CA1CA197059-01）相关的工作 进行与GY006试验相关的相关科学。该研究线的总体目标是 使用先进的图像引导的辐射增强骨盆癌的化学放疗的治疗比率 技术。如果成功，这项研究将显着改变许多骨盆的治疗方法 化学放疗是标准疗法的恶性肿瘤。

项目成果

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases.

用于改进全身扩散加权成像数据评估和前列腺癌骨转移评估的多室扩散模型。

• DOI: 10.1148/rycan.210115
• 发表时间: 2023
• 期刊: Radiology. Imaging cancer
• 作者: Conlin,ChristopherC;Feng,ChristineH;Digma,LeonardinoA;Rodríguez-Soto,AnaE;Kuperman,JoshuaM;Rakow-Penner,Rebecca;Karow,DavidS;White,NathanS;Seibert,TylerM;Hahn,MichaelE;Dale,AndersM
• 通讯作者: Dale,AndersM