Quantitative Multimodal Image Guidance for Improved Liver Cancer Treatment

定量多模态图像指导改善肝癌治疗

• 批准号: 9982672
• 负责人: JAMES S DUNCAN
• 金额: $ 60.16万
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982672
• 关键词: Acidity; Acidosis; Address; Advanced Development; Angiography; Biochemical; Biological Markers; Blood Vessels; Blood flow; Cancer Etiology; Cancer Patient; Catheters; Cellularity; Cessation of life; Chemoembolization; Classification; Clinic; Clinical; Data; Data Analyses; Deposition; Development; Diagnosis; Dictionary; Disease; Drug Delivery Systems; Drug Targeting; Drug usage; Edema; Emulsions; Environment; Epidemic; Evaluation; Excision; Future; Goals; Grant; Hepatitis B; Hepatitis C; High Dose Chemotherapy; Hypoxia; Image; Image Analysis; Imaging Techniques; Imaging technology; Incidence; Industrialization; Intervention; Learning; Liver neoplasms; Local Therapy; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of liver; Maps; Measures; Mediating; Methodology; Methods; Modification; Multimodal Imaging; Necrosis; Normal tissue morphology; Obesity; Oils; Oncology; Operative Surgical Procedures; Outcome; Outcome Assessment; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Pharmaceutical Preparations; Phase; Physiological; Preparation; Property; Recurrence; Roentgen Rays; Role; Stage at Diagnosis; Structure; System; Technology; Therapeutic Embolization; Tissues; Translational Research; Translations; Transplantation; Treatment outcome; Tumor Markers; Tumor Tissue; Tumor-Derived; Tweens; Vascularization; Visualization; Work; X-Ray Computed Tomography; angiogenesis; arm; base; cancer cell; cancer therapy; chemotherapy; clinical practice; cone-beam computed tomography; contrast imaging; curative treatments; extracellular; feeding; image guided; image guided intervention; image processing; image registration; imaging biomarker; improved; improved outcome; innovative technologies; learning strategy; machine learning method; minimally invasive; nonalcoholic steatohepatitis; nonlinear regression; novel; novel strategies; novel therapeutics; outcome prediction; palliative; personalized cancer therapy; random forest; response; systemic toxicity; targeted delivery; treatment guidelines; treatment strategy; tumor; tumor microenvironment; tumor progression

Project Summary/Abstract Liver cancer is the second most common cause of cancer-related death worldwide and is likely to grow even more in the next decade given the epidemic levels of hepatitis B and C and the emergence of non-alcoholic steatohepatitis (NASH) due to obesity in the US. Most liver cancer patients present with disease that cannot be treated surgically. Minimally invasive, catheter-based, intra-arterial therapies such as TACE (transarterial chemoembolization) have become the mainstay therapy and are included in all treatment guidelines because of their ability to achieve local tumor control and extend survival. TACE overcomes the problem of chemoresistance in cancer cells by delivering high dose chemotherapy through image guidance and embolization of the tumor feeding blood vessel. TACE most commonly uses an oily medium (Lipiodol) as a radiopaque drug delivery mate- rial by creating an emulsion between drugs and oil. The recent introduction of drug-eluting bead (DEB) technol- ogy provides an opportunity to achieve the goal of controlled and sustainable drug release to tumors, which was not possible with oily TACE. Although TACE clearly improves patient survival, limitations still exist – specifically, incomplete treatment and tumor recurrence – attributed to the stimulation of angiogenesis. Most of these issues can be addressed with a greater understanding of the tumor microenvironment, in particular the relationship that exists between hypoxia, acidosis and angiogenesis. In fact, the development of imaging biomarkers reflecting changes within the tumor microenvironment is increasingly being pursued to individualize cancer therapies and increase their potency. Yet, our ability to characterize the tumor microenvironment using current imaging tech- nology is extremely limited. TACE has had to rely on 2D X-ray angiography until recently when the emergence of intra-procedural dual phase cone beam CT (DP-CBCT) contributed significantly to improving tumor visualization, microcatheter guidance, and treatment endpoint. It is precisely through the longstanding close partnership be- tween Philips, Johns Hopkins and now Yale that this technology was optimized and became broadly accepted as the new standard of practice for TACE, demonstrating the prompt successful translation of research findings to clinical practice. However, the targeting of tumors and assessment of outcomes continues to be limited, relying on qualitative/semi-quantitative enhancement patterns from DP-CBCT and single parameter MR images. The unique partnership between Yale & Philips provides innovative technology that will directly enhance the role of image-guided intervention and address this unmet need by quantitatively characterizing the tumor microenvi- ronment and tumor tissue composition in order to maximize treatment potency and improve outcomes. We will integrate advanced, multiparameter MR with active CBCT imaging and create valuable biomarkers derived from novel machine learning methods for image and data analysis. By providing essential, quantitative information, drug delivery to tumors can be maximized because it will be based on inherent tumor properties. In the same way, the assessment of therapy will be much more precise and therefore useful to identify responders.

项目总结/文摘