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 reactiv...