Quantitative Multimodal Imaging Biomarkers for Combined Locoregional and Immunotherapy of Liver Cancer

项目摘要 肝癌是全球第四大癌症相关死亡原因。肝细胞癌 (HCC)是最常见的原发性肝癌类型，在西方世界呈上升趋势。最小的inva- 目前，以导管为基础的局部区域治疗（LRT），如经动脉化疗栓塞（TACE）， 中晚期HCC的主要治疗方法，并包括在所有管理指南中。 TACE是一种姑息性治疗，通过靶向治疗控制肝内肿瘤进展来提高生存率， 化学损伤，伴随着高浓度化疗进入肿瘤供血动脉。更 最近，系统性免疫疗法（IMT），特别是免疫检查点抑制剂，已经成为一种免疫治疗方法， 这是HCC的一种重要治疗选择，可以增强人体自身对肿瘤的免疫反应。虽然IMT 尽管这种治疗对许多癌症有希望，但只有15-30%的HCC患者对这种类型的治疗有反应。TACE越来越多 在新辅助和辅助情况下与IMT联合使用。最近的研究表明，TACE可以 显着改变肿瘤微环境（TME）变得更加免疫许可，使更有效， 通过IMT针对肿瘤的有效免疫细胞募集。因此，LRT+IMT组合是可能的路径 肝癌的治疗策略。在这种情况下，存在对稳健的、非- 侵入性定量生物标志物有助于指导治疗决策和早期评估治疗结果 在治疗期间。此前，我们的团队开发了临床和临床前先进成像，图像分析， 使用多参数磁共振成像技术研究、指导和评估单独TACE治疗HCC的成像生物标志物 磁共振成像（mpMRI）和磁共振波谱成像（MRSI）。我们开发了随机 森林和卷积神经网络，用于肝脏分割、组织分类和非刚性配准， 将这些结果映射到临床治疗环境中。使用图卷积神经网络，我们预- 口述和评估治疗结果。在兔肝癌模型（VX 2）中，使用生物传感器成像， 冗余偏移（BIRDS），我们成功地表征了TME的代谢状态， 对肝动脉化疗栓塞术前后细胞外酸中毒的影响。我们现在建议开发强大的定量生物标志物， 联合LRT+IMT评估和结果预测。我们将开发新的图像分析（联合 域学习与结构一致的嵌入解纠缠），并表征不断变化的TME 在LRT+IMT过程中，通过从纵向mpMRI（具有肝脏特异性造影剂）获取信息，和/或 多相计算机断层扫描（mpCT），通过域自适应跨模态学习。由于LRT+IMT是 预期减少治疗肝肿瘤的细胞外酸中毒，我们建议开发高分辨率先进的 兔VX 2模型中的BIRDS，采用新型机器学习来空间表征细胞外 由于LRT+IMT导致的酸中毒，使得能够集中于免疫激活最增强的肿瘤周围区域。 这些发展将最终促进个性化HCC治疗分层。

Project Summary Liver cancer is the fourth most common cause of cancer-related death worldwide. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and is on the rise in the western world. Minimally inva- sive, catheter-based locoregional therapies (LRT), such as transarterial chemoembolization (TACE), are now the mainstay treatments for intermediate to advanced stage HCC and are included in all management guidelines. TACE is a palliative therapy that prolongs survival by controlling intra-hepatic tumor progression via targeted is- chemic injury, paired with the delivery of highly concentrated chemotherapy into the tumor-feeding artery. More recently, systemic immunotherapies (IMT), specifically immune checkpoint inhibitors, have emerged as an im- portant treatment option for HCC to boost the body's own immune response against the tumor. While IMT is promising for many cancers, only 15-30% of HCC patients respond to this type of therapy. TACE is increasingly used in conjunction with IMT, both in neoadjuvant and adjuvant scenarios. Recent efforts show that TACE can dramatically alter the tumor microenvironment (TME) to become more immune-permissive, enabling more ef- fective immune cell recruitment against the tumor through IMT. Thus, the LRT+IMT combination is a likely path forward for HCC treatment strategies. In this context, there is an urgent and unmet clinical need for robust, non- invasive quantitative biomarkers to help guide therapeutic decision making and assess therapeutic outcome early during treatment. Previously, our team developed clinical and preclinical advanced imaging, image analysis, and imaging biomarkers to study, guide and assess HCC treatment with TACE alone using multiparameter magnetic resonance imaging (mpMRI) and magnetic resonance spectroscopic imaging (MRSI). We developed random forests and convolutional neural networks for liver segmentation, tissue classification and nonrigid registration to map these results into the clinical treatment environment. Using graph convolutional neural networks, we pre- dicted and assessed therapeutic outcomes. In a rabbit model of liver cancer (VX2), using Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), we successfully characterized the metabolic state of the TME with respect to extracellular acidosis, before and after TACE. We now propose to develop robust quantitative biomarkers for combined LRT+IMT assessment and outcome prediction in humans. We will develop novel image analysis (Joint Domain Learning with Structure-Consistent Embedding by Disentanglement) and characterize the changing TME over the course of LRT+IMT by deriving information from longitudinal mpMRI (with liver-specific contrast) and/or multiphase computed tomography (mpCT), learning across modalities via domain adaptation. Since LRT+IMT is expected to reduce extracellular acidosis in treated liver tumors, we propose to develop high-resolution advanced BIRDS in the rabbit VX2 model with novel machine learning to spatially characterize changes in extracellular acidosis due to LRT+IMT, enabling focus on the peritumoral region where immune activation is most enhanced. These developments will ultimately facilitate personalized HCC treatment stratification.