A tissue viability imaging biomarker for use in non-invasive breast cancer therapy

用于非侵入性乳腺癌治疗的组织活力成像生物标志物

• 批准号: 10365605
• 负责人: SARANG JOSHI
• 金额: $ 54.47万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10365605
• 关键词: 3-Dimensional; Ablation; Address; Advanced Development; Anatomy; Applications Grants; Benchmarking; Biological Markers; Biomedical Engineering; Blood Vessels; Breast; Breast Cancer Patient; Breast Cancer Treatment; Breast Cancer therapy; Breast Magnetic Resonance Imaging; Cancer Center; Clinical; Clinical Trials; Clinical assessments; Collaborations; Computer Vision Systems; Conflict (Psychology); Contrast Media; Cosmetics; Data; Data Set; Development; Dose; Early Diagnosis; Ensure; Environment; Female Breast Carcinoma; Focused Ultrasound; Focused Ultrasound Therapy; Future; Gadolinium; Goat; Gold; Histopathology; Image; Image Analysis; Individual; International; Label; Literature; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Neoplasms; Methods; Modeling; Monitor; Oryctolagus cuniculus; Pathology; Patients; Pre-Clinical Model; Procedures; Protocols documentation; Radiology Specialty; Research Project Grants; Research Proposals; Safety; Supervision; System; Techniques; Therapeutic; Thermal Ablation Therapy; Thermometry; Time; Tissue Viability; Tissues; Training; Translations; Treatment Efficacy; Treatment outcome; Udders; Validation; Work; base; contrast imaging; convolutional neural network; deep learning; deep neural network; detection method; digital imaging; efficacious treatment; image guided; image registration; imaging biomarker; improved; in vivo; innovation; learning strategy; malignant breast neoplasm; minimally invasive; model design; mortality; multidisciplinary; novel; overtreatment; patient population; predictive marker; professional atmosphere; radio frequency; reconstruction; treatment planning; treatment response; tumor; tumor ablation

Summary/Abstract While improved early detection methods and treatments have reduced breast cancer mortality, a sizable portion of patients remains overdiagnosed and overtreated, warranting the development of more conservative breast cancer treatments. Magnetic resonance guided focused ultrasound (MRgFUS) is one of the most attractive, emerging procedures for breast cancer as it can safely and efficaciously treat localized breast tumors non-invasively. Currently, clinical MRgFUS ablation treatments are assessed with MRI metrics that primarily quantify thermal and vascular effects. While there is evident MR sensitivity for tissue changes induced by MRgFUS thermal ablation, no single metric or combination of metrics have demonstrated adequate accuracy in predicting tissue viability during or immediately post-MRgFUS ablation treatment. In addition, the use of gadolinium contrast agent-based assessment techniques precludes further ablation treatment if positive tumor margins are suspected. This work proposes to address this critical unmet need through developing a deep neural network non-contrast imaging biomarker that would provide an immediate and accurate assessment of tissue viability and could be applied repeatedly for an iterative assessment of tissue viability during the MRgFUS ablation procedure, assuring complete non-invasive tumor treatment. This objective will be accomplished with three specific aims. Aim 1: Develop and validate a 3D multiparametric MRI protocol for efficient acquisition of qualitative and quantitative MR images in the breast MRgFUS therapeutic environment. Aim 2: Develop, train and validate a deep neural network biomarker for predicting tissue viability in a tumor model during MRgFUS ablation treatments. Aim 3: Integrate the tissue viability biomarker in an existing breast MRgFUS ablation clinical workflow and demonstrate complete treatment volume ablation using the non-contrast, deep neural network biomarker as the treatment assessment metric. We have developed an innovative, volumetric histopathology diffeomorphic registration procedure that allows the voxel-wise comparison of in vivo MR images to histopathological data, providing the gold-standard labeled data set needed to develop this imaging biomarker. Training and validation of the imaging biomarker will be performed in preclinical models designed to allow immediate generalizability and translation to ongoing clinical trials. This imaging biomarker will provide accurate assessment of tissue viability during MRgFUS ablation treatments, revolutionizing minimally invasive breast cancer treatments and directly addressing the critical issue of overtreatment.

摘要/摘要 虽然改善的早期检测方法和治疗方法降低了乳腺癌的死亡率，但相当大 部分患者仍然过度诊断和过度治疗，保证更保守的发展 乳腺癌治疗。磁共振引导聚焦超声（MRGFU）是最多的之一 有吸引力的乳腺癌的新兴程序，因为它可以安全有效地治疗局部乳房 肿瘤无创。目前，使用MRI指标评估临床MRGFUS消融治疗 主要量化热和血管效应。虽然对组织变化有明显的MR敏感性诱导 通过MRGFU热消融，没有单一的度量或指标组合表现出足够的表现 预测在MRGFUS消融治疗期间或立即预测组织生存能力的准确性。另外， 如果阳性 怀疑肿瘤边缘。这项工作建议通过开发一个关键的未满足需求 深度神经网络非对比度成像生物标志物，它将提供直接准确的 评估组织生存能力，可以重复应用于组织生存能力的迭代评估 在MRGFUS消融过程中，确保完全非侵入性肿瘤治疗。这个目标将是 以三个具体目标完成。 目标1：开发和验证3D多参数MRI协议，以有效地获取定性和 乳房MRGFUS治疗环境中的定量MR图像。 目标2：开发，训练和验证深度神经网络生物标志物，以预测肿瘤的组织生存能力 MRGFUS消融治疗过程中的模型。 AIM 3：将组织生存能力生物标志物整合在现有的乳房MRGFUS消融临床工作流程中 使用非对比度，深神经网络生物标志物作为完整的治疗量消融 治疗评估指标。 我们已经开发了一种创新的，体积的组织病理学差异登记程序，该程序允许 体内MR图像与组织病理学数据的体素比较，提供标记的金标准 开发此成像生物标志物所需的数据集。成像生物标志物的培训和验证将是 在旨在立即推广和翻译为正在进行的临床的临床前模型中进行 试验。这种成像生物标志物将在MRGFUS消融过程中准确评估组织生存能力 治疗，革新微创乳腺癌的治疗以及直接解决关键 过度治疗的问题。