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.

摘要 妇科癌症是全世界妇女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，他们往往表现为局部晚期疾病，盆腔放疗（RT）与 同时使用顺铂（即，化疗或放疗）是标准的治疗方法。然而，这种治疗是有限的， 治疗失败率高。通过化疗双重疗法加强治疗， 同时或作为放化疗后的辅助治疗，是一种有前途的策略，以改善结果。 然而，强化化疗的递送因胃肠道和胃肠道的高发生率而复杂化。 血液学毒性需要在提高放化疗疗效的同时降低毒性的策略。 标准盆腔RT技术包括大量正常组织，包括肠、骨 骨髓、骨骼、膀胱和直肠，导致可预防的辐射诱导毒性。图像引导放射 治疗（IGRT）可以改善靶定位和剂量测定，优化靶剂量，同时最小化剂量， 周围的正常组织。然而，IGRT可能是高度资源密集型的，并且相对有效 缺乏审判。由于这个原因，关于IG-IMRT在这种情况下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的，具有成本效益的IGRT方法的最前沿 具有促进更好地递送同时和/或辅助化疗的广泛潜力。 以前我们已经发现，辐射引起的造血活性骨髓损伤是一个重要因素。 对强化化疗耐受性的关键决定因素。利用机器学习方法，我们最近 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， 它可以在使用正电子发射断层扫描（PET）技术的环境中减少对PET的需求。 没有的或负担不起的。拟议的新研究将研究基于多图谱的IGRT的能力， 与标准治疗相比，减少血液学毒性并改善化疗输送，使用数据 从450例患者入组的随机III期试验（NRG-GY 006）。此外，我们将使用串行整体 体部PET/CT研究放疗剂量和化疗强度对代偿性 造血反应，并开发了新的全身放射组学生物标志物，以量化 炎症状态，我们假设它可以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R 01资助（1 R 01 CA 197059 -01）相关的工作扩展到 开展与GY 006试验相关的科学研究。这条研究路线的首要目标是 利用先进的图像引导放射技术提高盆腔癌放化疗的治愈率 技术.如果成功，这项研究将大大改变许多盆腔炎的治疗方法。 标准化放疗的恶性肿瘤。

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