A fully automated PET radiomics framework

全自动 PET 放射组学框架

• 批准号: 10458241
• 负责人: Abhinav K Jha
• 金额: $ 49.29万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-06 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10458241
• 关键词: Address; Affect; American College of Radiology Imaging Network; Anatomy; Biological Markers; Biology; Cause of Death; Characteristics; Clinical; Clinical Trials; Computer software; Data; Data Set; Discipline of Nuclear Medicine; Disease; Engineering; Evaluation; Goals; Gold; Heterogeneity; Image; Knowledge; Lead; Malignant Neoplasms; Malignant neoplasm of thorax; Manuals; Measurement; Measures; Medical; Medical Oncologist; Metabolic; Methods; Molecular; Multicenter Trials; Noise; Non-Small-Cell Lung Carcinoma; Outcome; PET/CT scan; Patients; Physics; Positron-Emission Tomography; Prediction of Response to Therapy; Procedures; Process; Progression-Free Survivals; Property; Protocols documentation; Quality of life; Radiation Therapy Oncology Group; Reader; Regimen; Reproducibility; Resolution; Retrospective Studies; Role; Smoking History; Techniques; Time; Toxic effect; Training; Translating; Treatment Cost; base; biomarker development; biomarker discovery; cancer imaging; chemoradiation; clinical application; clinical translation; deep learning; early detection biomarkers; efficacy evaluation; fluorodeoxyglucose positron emission tomography; imaging modality; imaging scientist; improved; in vivo; mortality; multidisciplinary; novel; optimal treatments; patient oriented; personalized medicine; precision medicine; prognostic value; quantitative imaging; radiologist; radiomics; reconstruction; response; simulation; theories; tumor

Summary The overall goal of this proposal is to develop a fully automated PET radiomics framework and evaluate the efficacy of PET radiomic features (RFs) derived from this framework in predicting therapy response in patients with stage III non-small cell lung cancer (NSCLC). Radiomics is showing exciting promise in deriving biomarkers for several diseases. The potential to measure and evaluate the efficacy of radiomic features derived from PET for early prediction of therapy response is highly impactful since PET probes the functional characteristics of the tumor, where changes are manifested sooner in comparison to anatomical changes. However, PET images have high noise and limited resolution, which leads to inaccurate and imprecise RF measurements that then have limited clinical value. Previously we have developed techniques to optimize quantitative imaging methods and shown that these can help estimate more reliable quantitative metrics leading to better predictive ability with these metrics. Building on these past studies and by combining concepts from imaging physics, statistical inference theory, deep learning, we propose to develop methods that accurately and precisely estimate RFs from PET. These methods will include a fully automated PET segmentation method that will enable reliable delineation of tumor boundaries using a practical approach. Next, a no-gold-standard (NGS) evaluation technique will be developed to optimize RF quantification protocols. This technique will provide a mechanism for precise measurement of RFs from PET images without access to the ground truth RF value. The methods will be rigorously validated in the context of measuring radiomics features in patients with NSCLC using a combination of realistic simulations, physical phantom studies and existing patient data. Select RFs will then be retrospectively evaluated on predicting therapy response using existing data the ACRIN 6697 longitudinal clinical trial in patients with stage III NSCLC. A strong multidisciplinary team has been assembled for this project, consisting of an imaging scientist, clinical nuclear-medicine radiologists, medical oncologist with expertise in biomarker development for thoracic malignancies and biology of NSCLC, biostatistician, and a medical physicist. The proposed methods are poised to have a strong impact on PET radiomics by enabling measurement of precise and accurate RFs, and by facilitating the clinical translation of PET radiomics. The impact is strengthened as we investigate the predictive ability of the PET RFs in patients with stage III NSCLC, a leading cause of death with low overall survival, and with an important and timely need for improved personalized therapy regimens. Further, the methods developed in this project are general and potentially impact precision-medicine approaches for other cancers as well as other diseases where PET imaging has a clinical role.

总结