Curating musculoskeletal CT data to enable the development of AI/ML approaches for analysis of clinical CT in patients with metastatic spinal disease

整理肌肉骨骼 CT 数据，以开发用于分析转移性脊柱疾病患者临床 CT 的 AI/ML 方法

• 批准号: 10593799
• 负责人: RON N ALKALAY
• 金额: $ 35.23万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593799
• 关键词: Administrative Supplement; Advanced Development; Affect; Age; Anatomy; Appearance; Architecture; Area; Artificial Intelligence; Biological; Biological Markers; Cachexia; Cancer Patient; Classification; Clinical; Clinical Data; Communities; Complex; Computer Vision Systems; Data; Data Analyses; Data Collection; Data Set; Databases; Development; Diagnostic; Digital Imaging and Communications in Medicine; Disease; Disease Progression; Ensure; Event; Evolution; Face; Fracture; Guidelines; Human; Image; Individual; Label; Lesion; Machine Learning; Malignant Neoplasms; Malnutrition; Manuals; Mechanics; Mediating; Metabolic; Metadata; Metastatic Neoplasm to the Bone; Metastatic to; Methods; Modeling; Morbidity - disease rate; Muscle; Muscular Atrophy; Musculoskeletal; Neoplasm Metastasis; Neurologic Deficit; Pain; Pathologic; Patient Care; Patients; Physical Function; Property; Quality Control; Radiation; Radiation therapy; Radiology Specialty; Readiness; Research; Research Personnel; Resolution; Risk; Scanning; Sensitivity and Specificity; Skeletal Muscle; Speed; Spinal; Spinal Diseases; Spinal Fractures; Standardization; System; The Cancer Imaging Archive; Tissues; Training; Unnecessary Procedures; Vertebral Bone; Vertebral column; X-Ray Computed Tomography; base; bone; clinical care; clinical risk; clinically significant; cost; deep learning; follow-up; fracture risk; high risk; improved; insight; large datasets; learning strategy; machine learning model; muscle form; novel; palliative; parent grant; predictive marker; prevent; radiological imaging; sarcopenia; sex; skeletal tissue; spine bone structure; treatment optimization

Project Summary/Abstract Vertebral bone metastases, widespread in patients with cancer, destroy vertebral anatomy, bone architecture, and mechanical strength, exposing patients to a high risk of pathologic vertebral fracture (PVF). PVFs cause neurological deficits in up to 50% of patients with further complications that may be fatal. Our parent grant (AR075964) develops and validates a novel computational musculoskeletal approach for patient-specific, precise prediction of PVF risk from clinical CT. Segmentation of vertebral anatomy, bone properties, and individual spinal musculature cross-sectional area from clinical CT imaging, a fundamental step for assembling the computational musculoskeletal models, faces unique challenges due to the cancer-mediated alteration in skeletal tissues radiological appearance. Application of deep learning (DL) methods will speed and standardize the critical segmentation step, permitting analysis of larger datasets promoting new DL analysis for improved insight into the drivers of PVF risk in patients with metastatic spine disease. This development is hindered by the lack of publicly available, clinically annotated image data specific to metastatic human spines. This proposal aims to establish a curated, publicly accessible, 4D CT imaging dataset of human metastatic spines and associated radiological delineations of lesions and vertebral features, to enable the advancement of DL methods to analyze PVF risk. We thus propose three specific aims: 1) prepare the CT dataset for the application of deep learning: Our data is based on our parent grant (AR075964) de-identified spinal column and thoracoabdominal muscles CT image datasets obtained from 140 patients at radiation planning and additional CT diagnostic data at 3, 6, 9, and 12 months follow up (1400 image data sets overall). We will assemble and curate the data for public distribution by a) preprocessing the CT images for machine learning applications, b) delineating the vertebrae, lesions, and muscles in the images, c) assembling metadata, including limited subject demographic and disease status into JSON files, and d) extract SIFT features for computer-vision style analyses. 2) Testbed Deep Learning (DL) Segmentation: To ensure that the curated data set is suitable for training artificial intelligence and machine learning (AI/ML) systems, we will develop and train testbed DL segmentation networks to segment bones, lesions, and muscles in baseline and follow-up clinical CT. We will use the networks to control the quality the curated CT images and delineations, 3) Disseminate 4D dataset following best practices: Upon completion of tasks 1 and 2, we will make the data available to the research community via the Cancer Image Archive (TCIA) following their established methods for de-identifying DICOM scans and annotating and encoding clinical data and analysis results. Integrating DL systems within our approach will change the patient management paradigm from reactive to data-driven proactive management to prevent PVF events and critically reduce bias in patient management.

项目总结/文摘