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

用于肝癌局部区域和免疫联合治疗的定量多模态成像生物标志物

• 批准号: 10707985
• 负责人: JAMES S DUNCAN
• 金额: $ 57.63万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707985
• 关键词: Acidosis; Adjuvant; Aftercare; Arteries; Biological Markers; Biopsy Specimen; Biosensor; Cancer Etiology; Catheters; Cell Density; Cells; Cellularity; Cessation of life; Chemoembolization; Classification; Clinical; Clinical Treatment; Collaborations; Combination immunotherapy; Combined Modality Therapy; Data Pooling; Decision Making; Development; Environment; Goals; Grant; Graph; Guidelines; Habitats; Hepatocyte; Human; Image; Image Analysis; Immune; Immune checkpoint inhibitor; Immune response; Immune system; Immunobiology; Immunologics; Immunotherapy; Individual; Intervention; Joints; Learning; Lesion; Liver; Liver neoplasms; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of liver; Manuals; Maps; Measurement; Measures; Medical Oncology; Metabolic; Metabolism; Methods; Modality; Modeling; Multimodal Imaging; Neoadjuvant Therapy; New Zealand; Oryctolagus cuniculus; Outcome Assessment; Palliative Care; Pathology; Patient Care; Patients; Pattern; Phase; Phenotype; Primary Malignant Neoplasm of Liver; Primary carcinoma of the liver cells; Recurrence; Resolution; Standardization; Structure; Therapeutic; Time; Tissues; Translating; Tumor Volume; Western World; X-Ray Computed Tomography; automated segmentation; biomarker development; cancer therapy; chemotherapy; clinical decision-making; clinical outcome assessment; cohort; cone-beam computed tomography; contrast enhanced; convolutional neural network; deep learning; design; extracellular; feeding; graph neural network; image guided; image registration; imaging biomarker; imaging informatics; immune activation; improved; in vivo; innovation; intrahepatic cancer; ischemic injury; learning strategy; liver cancer model; machine learning method; magnetic resonance imaging biomarker; magnetic resonance spectroscopic imaging; metabolic imaging; minimally invasive; neoplastic cell; non-invasive imaging; novel; novel strategies; outcome prediction; permissiveness; pre-clinical; predicting response; radiomics; random forest; recruit; response; spatiotemporal; spectroscopic imaging; therapy outcome; treatment strategy; treatment stratification; tumor; tumor microenvironment; tumor progression

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.

项目概要 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。微创 目前，基于导管的局部治疗（LRT），例如经动脉化疗栓塞术（TACE） 中晚期 HCC 的主要治疗方法，并包含在所有管理指南中。 TACE 是一种姑息疗法，通过靶向控制肝内肿瘤进展来延长生存期： 化学损伤，同时将高浓度化疗药物输送到肿瘤供血动脉中。更多的 最近，全身免疫疗法（IMT），特别是免疫检查点抑制剂，已成为一种免疫疗法。 增强人体自身针对肿瘤的免疫反应是 HCC 的重要治疗选择。虽然 IMT 是 对于许多癌症来说，这种疗法很有希望，但只有 15-30% 的 HCC 患者对这种疗法有反应。 TACE 越来越 在新辅助和辅助场景中与 IMT 结合使用。最近的努力表明 TACE 可以 显着改变肿瘤微环境（TME），使其变得更加免疫许可，从而实现更有效的 通过 IMT 招募感染性免疫细胞对抗肿瘤。因此，LRT+IMT组合是一条可能的路径 推进 HCC 治疗策略。在这种背景下，迫切且未得到满足的临床需求是对强大的、非 侵入性定量生物标志物有助于指导治疗决策并尽早评估治疗结果 治疗期间。此前，我们的团队开发了临床和临床前先进成像、图像分析和 使用多参数磁成像生物标记物来研究、指导和评估单独使用 TACE 的 HCC 治疗 磁共振成像（mpMRI）和磁共振波谱成像（MRSI）。我们随机开发了 用于肝脏分割、组织分类和非刚性配准的森林和卷积神经网络 将这些结果映射到临床治疗环境中。使用图卷积神经网络，我们预先 预测并评估治疗结果。在兔肝癌模型 (VX2) 中，使用生物传感器成像 通过班次冗余偏差 (BIRDS)，我们成功地描述了 TME 的代谢状态 TACE 之前和之后的细胞外酸中毒。我们现在建议开发强大的定量生物标志物 结合 LRT+IMT 评估和人类结果预测。我们将开发新颖的图像分析（联合 通过解开进行结构一致嵌入的领域学习）并描述不断变化的 TME 在 LRT+IMT 过程中，通过从纵向 mpMRI（具有肝脏特异性对比）获取信息和/或 多相计算机断层扫描 (mpCT)，通过领域适应进行跨模式学习。由于 LRT+IMT 是 预计将减少治疗肝肿瘤的细胞外酸中毒，我们建议开发高分辨率的先进技术 兔子 VX2 模型中的 BIRDS 采用新颖的机器学习来空间表征细胞外的变化 LRT+IMT 引起的酸中毒，能够重点关注免疫激活最强的瘤周区域。 这些进展最终将促进个性化 HCC 治疗分层。

项目成果

Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial.

• DOI: 10.1016/j.clinimag.2021.05.007
• 发表时间: 2021-10
• 期刊: Clinical imaging
• 影响因子: 2.1
• 作者: Letzen BS;Malpani R;Miszczuk M;de Ruiter QMB;Petty CW;Rexha I;Nezami N;Laage-Gaupp F;Lin M;Schlachter TR;Chapiro J
• 通讯作者: Chapiro J

Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework.

使用基于自动上下文的深度神经网络和多阶段训练框架进行肝脏组织分类。

• DOI: 10.1007/978-3-030-00500-9_7
• 发表时间: 2018
• 期刊: Patch-based techniques in medical imaging : 4th international workshop, Patch-MI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. Patch-MI (Workshop) (4th : 2018 : Granada, Spain)
• 作者: Zhang,Fan;Yang,Junlin;Nezami,Nariman;Laage-Gaupp,Fabian;Chapiro,Julius;DeLin,Ming;Duncan,James
• 通讯作者: Duncan,James

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation.

渐进学习与迁移学习的结合：在多部位前列腺 MRI 分割中的应用。

• DOI: 10.1007/978-3-031-18523-6_1
• 发表时间: 2022
• 期刊: Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : Third MICCAI Workshop, DeCaF 2022 and Second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Sin...
• 作者: You,Chenyu;Xiang,Jinlin;Su,Kun;Zhang,Xiaoran;Dong,Siyuan;Onofrey,John;Staib,Lawrence;Duncan,JamesS
• 通讯作者: Duncan,JamesS

Intra-arterial therapy of neuroendocrine tumour liver metastases: comparing conventional TACE, drug-eluting beads TACE and yttrium-90 radioembolisation as treatment options using a propensity score analysis model.

• DOI: 10.1007/s00330-017-4856-2
• 发表时间: 2017-12
• 期刊: European radiology
• 影响因子: 5.9
• 作者: Do Minh D;Chapiro J;Gorodetski B;Huang Q;Liu C;Smolka S;Savic LJ;Wainstejn D;Lin M;Schlachter T;Gebauer B;Geschwind JF
• 通讯作者: Geschwind JF

Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy.

• DOI: 10.1007/s00330-021-08058-7
• 发表时间: 2021-12
• 期刊: European radiology
• 影响因子: 5.9
• 作者: Ghani MA;Fereydooni A;Chen E;Letzen B;Laage-Gaupp F;Nezami N;Deng Y;Gan G;Thakur V;Lin M;Papademetris X;Schernthaner RE;Huber S;Chapiro J;Hong K;Georgiades C
• 通讯作者: Georgiades C