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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要肝癌是全球与癌症相关死亡的第四大原因。肝细胞癌（HCC）是原发性肝癌的最常见类型，在西方世界正在上升。最小 Sive，基于导管的局部疗法（LRT），例如跨性化学栓塞（TACE），现在是中级至高级阶段HCC的主要治疗方法，并包括在所有管理指南中。 TACE是一种姑息治疗化学损伤与将高度浓缩化疗的递送到肿瘤喂养动脉中配对。更多的最近，系统性免疫疗法（IMT）（特别是免疫检查点抑制剂）已成为IM- HCC的便携式治疗选择可以增强人体对肿瘤的免疫反应。而IMT是对于许多癌症而言，只有15-30％的HCC患者对这种类型的治疗做出反应。 Tace越来越多与IMT一起使用，包括新辅助和调整场景。最近的努力表明Tace可以动态改变肿瘤微环境（TME）以变得更加免疫性，从而使更多的EF- 通过IMT针对肿瘤的人体免疫细胞募集。那就是LRT+IMT组合是一条可能的路径在这种情况下，紧急且未满足的临床需求是可靠的，非 - 侵入性定量生物标志物可帮助指导治疗决策和评估治疗结果治疗期间。以前，我们的团队开发了临床和临床前的高级成像，图像分析和对生物标志物进行成像以研究，指导和评估HCC治疗，仅使用多参数磁磁共振成像（MPMRI）和磁共振光谱成像（MRSI）。我们发展了随机森林和卷积神经网络，用于肝分割，组织分类和非辅助注册将这些结果映射到临床治疗环境中。使用图形卷积神经网络，我们预先对治疗结果进行定用和评估。在肝癌（VX2）的兔模型中，使用生物传感器成像轮班（鸟）的冗余偏差，我们成功地表征了TME的代谢状态在TACE之前和之后进行细胞外酸中毒。现在，我们建议开发出强大的定量生物标志物 LRT+IMT评估和人类结果预测的结合。我们将开发新的图像分析（关节域学习与结构一致的嵌入通过分离）并表征了变化的TME 在LRT+IMT的过程中，通过从纵向mpmri（带有肝脏特异性对比度）和/或多相计算机断层扫描（MPCT），通过域的适应性跨模态学习。由于LRT+IMT为预计将减少治疗肝肿瘤中的细胞外酸中毒，我们建议发展高分辨率晚期兔子VX2模型中的鸟类具有新颖的机器学习，以空间表征细胞外变化酸中毒是由于LRT+IMT引起的，因此将重点放在免疫激活最大的周围区域。这些发展最终将促进个性化的HCC治疗层。

项目成果

期刊论文数量（38）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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...
影响因子：
0
作者：
You,Chenyu;Xiang,Jinlin;Su,Kun;Zhang,Xiaoran;Dong,Siyuan;Onofrey,John;Staib,Lawrence;Duncan,JamesS
通讯作者：
Duncan,JamesS

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)
影响因子：
0
作者：
Zhang,Fan;Yang,Junlin;Nezami,Nariman;Laage-Gaupp,Fabian;Chapiro,Julius;DeLin,Ming;Duncan,James
通讯作者：
Duncan,James

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 in patients with hepatocellular carcinoma by fusing structured and rotationally invariant context representation.

通过融合结构化和旋转不变上下文表示对肝细胞癌患者的肝组织进行分类。