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

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的死亡率，但相当大的 部分患者仍然被过度诊断和过度治疗，因此有理由发展为更保守的 乳腺癌治疗。磁共振引导聚焦超声(MRgFUS)是其中最重要的一种 极具吸引力的新兴乳腺癌治疗方法，因为它可以安全有效地治疗局限性乳腺癌 肿瘤是非侵袭性的。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。虽然磁共振对引起的组织变化有明显的敏感性 通过MRgFUS热消融，没有一个单一的指标或指标的组合被证明是足够的 在MRgFUS消融治疗期间或治疗后立即预测组织活性的准确性。此外， 如果阳性，使用基于造影剂的评估技术可以排除进一步的消融治疗 肿瘤边缘可疑。这项工作建议通过开发一种 深度神经网络非对比成像生物标记物，将提供即时和准确的 组织活性的评估，并可重复应用于组织活性的迭代评估 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：开发和验证3D多参数磁共振成像协议，以有效获取定性和 乳腺MRgFUS治疗环境中的定量磁共振图像。 目的2：开发、训练和验证用于预测肿瘤组织活力的深度神经网络生物标记物 模型在MRgFUS消融治疗期间。 目的3：将组织活性生物标记物整合到现有的乳腺MRgFUS消融临床工作流程中 使用无对比深部神经网络生物标记物演示完全治疗体积消融 治疗评估指标。 我们已经开发了一种创新的体积组织病理学差同胚层配准程序，它允许 活体磁共振图像与组织病理学数据的体素比较，提供了金标准标记 开发这种成像生物标记物所需的数据集。将对成像生物标记物进行培训和验证 在临床前模型中执行，旨在立即推广并转换为正在进行的临床 审判。该成像生物标记物将在MRgFUS消融术中提供组织存活的准确评估。 治疗，革命性的微创乳腺癌治疗，并直接解决关键 过度治疗的问题。

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.