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 虽然改进的早期检测方法和治疗方法降低了乳腺癌死亡率，但 部分患者仍然被过度诊断和过度治疗，需要开发更保守的治疗方法 乳腺癌治疗。磁共振引导聚焦超声（MRgFUS）是最先进的超声引导技术之一。 有吸引力的新兴乳腺癌手术，因为它可以安全有效地治疗局部乳房 肿瘤非侵入性。目前，临床 MRgFUS 消融治疗是通过 MRI 指标进行评估的 主要量化热效应和血管效应。虽然 MR 对引起的组织变化具有明显的敏感性 通过 MRgFUS 热消融，没有任何单一指标或指标组合已证明足够 MRgFUS 消融治疗期间或之后立即预测组织活力的准确性。此外， 如果阳性，使用基于钆造影剂的评估技术可以排除进一步的消融治疗 怀疑肿瘤边缘。这项工作建议通过开发一个解决这一关键的未满足的需求 深度神经网络非对比成像生物标志物，可提供即时、准确的 组织活力评估，并可重复应用于组织活力的迭代评估 在 MRgFUS 消融过程中，确保完全的非侵入性肿瘤治疗。这一目标将是 实现了三个具体目标。 目标 1：开发并验证 3D 多参数 MRI 协议，以有效获取定性和 乳腺 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，用于预测肿瘤中的组织活力 MRgFUS 消融治疗期间的模型。 目标 3：将组织活力生物标志物整合到现有的乳房 MRgFUS 消融临床工作流程中，并 使用非对比深层神经网络生物标记物展示完整的治疗体积消融 治疗评估指标。 我们开发了一种创新的体积组织病理学微分形态配准程序，允许 体内 MR 图像与组织病理学数据的体素比较，提供金标准标记 开发这种成像生物标志物所需的数据集。成像生物标志物的训练和验证将 在临床前模型中进行，旨在允许立即推广并转化为正在进行的临床 试验。该成像生物标志物将在 MRgFUS 消融过程中准确评估组织活力 治疗，彻底改变微创乳腺癌治疗并直接解决关键问题 过度治疗的问题。