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，难以获得筛查和疫苗接种。 因此，他们经常出现局部晚期疾病，为此需要进行盆腔放射治疗（RT） 同步顺铂（即放化疗）是标准治疗。然而，这种治疗方法受到以下限制： 治疗失败率高。通过给予化疗双药来强化治疗，或者 同时或作为放化疗后的辅助治疗，是一种有前景的改善预后的策略。 然而，由于胃肠道和胃肠道损伤发生率较高，强化化疗的实施变得复杂。 血液学毒性。需要减少毒性同时提高放化疗疗效的策略。 标准盆腔放疗技术涵盖大量正常组织，包括肠、骨 骨髓、骨骼、膀胱和直肠，导致可预防的辐射引起的毒性。图像引导辐射 治疗（IGRT）可以改善目标定位和剂量测定，优化目标剂量，同时最大限度地减少剂量 周围正常组织。然而，IGRT 可能是高度资源密集型的，并且相对有效性 缺乏试验。因此，对于 IG-IMRT 在此方面的效用存在相当大的争议。 疾病。我们的研究小组一直处于开发新颖、经济高效的 IGRT 方法的前沿 具有促进更好地实施同步和/或辅助化疗的广泛潜力。 此前我们发现辐射对造血活跃的骨髓造成的损伤是一种 对强化化疗耐受的关键决定因素。最近，我们利用机器学习方法 开发了一种基于多图谱的 IGRT 方法，可以预测活性骨髓的典型分布， 在使用该技术的环境中，这可以消除对正电子发射断层扫描 (PET) 的需要 无法获得或负担不起。拟议的新研究将研究基于多图集的 IGRT 的能力 使用数据，与标准治疗相比，减少血液毒性并改善化疗递送 来自 450 名参加随机 III 期试验 (NRG-GY006) 的患者。此外，我们将使用串行整体 全身PET/CT研究放射剂量和化疗强度对代偿性的影响 造血反应，并开发了新型全身放射组学生物标志物来量化 炎症状态，我们假设它会影响患者的结果和对化疗的耐受性。 这项新研究将我们与当前 R01 资助 (1R01CA197059-01) 相关的工作扩展到 进行与 GY006 试验相关的相关科学。该研究方向的总体目标是 使用先进的图像引导放射提高盆腔癌放化疗的治疗率 技术。如果成功，这项研究将显着改变许多盆腔疾病的治疗方法 标准放化疗的恶性肿瘤。

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